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1 Commits
Author SHA1 Message Date
Anthony Stirling fc13a6ec66 ui 2025-12-22 00:50:27 +00:00
526 changed files with 8292 additions and 51594 deletions
+11 -15
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@@ -6,27 +6,22 @@ app: &app
- app/(common|core|proprietary)/src/main/java/**
openapi: &openapi
- *build
- *app
docker: &docker
- Dockerfile
- Dockerfile.fat
- Dockerfile.ultra-lite
- ".github/workflows/build.yml"
- scripts/init.sh
- scripts/init-without-ocr.sh
- exampleYmlFiles/**
- build.gradle
- app/(common|core|proprietary)/build.gradle
- app/(common|core|proprietary)/src/main/java/**
project: &project
- app/(common|core|proprietary)/src/(main|test)/java/**
- *build
- "app/(common|core|proprietary)/src/(main|test)/resources/**/!(messages_*.properties|*.md)*"
- app/(common|core|proprietary)/build.gradle
- 'app/(common|core|proprietary)/src/(main|test)/resources/**/!(messages_*.properties|*.md)*'
- exampleYmlFiles/**
- gradle/**
- libs/**
- "testing/**/!(requirements*.txt|requirements*.in)*"
- *docker
- testing/**
- build.gradle
- Dockerfile
- Dockerfile.fat
- Dockerfile.ultra-lite
- gradle.properties
- gradlew
- gradlew.bat
@@ -34,6 +29,7 @@ project: &project
- settings.gradle
- frontend/**
- docker/**
- testing/**
frontend: &frontend
- frontend/**
+1 -1
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@@ -1 +1 @@
allow-ghsas: GHSA-wrw7-89jp-8q8g
allow-ghsas: GHSA-wrw7-89jp-8q8g
-5
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@@ -1,9 +1,4 @@
{
"label_changer": [
"Frooodle",
"Ludy87",
"balazs-szucs"
],
"repo_devs": [
"Frooodle",
"sf298",
-3
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@@ -184,6 +184,3 @@
- name: "codex"
color: "ededed"
description: "chatgpt AI generated code"
- name: "break-change"
color: "FF0000"
description: "This PR introduces a breaking API change."
-4
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@@ -27,10 +27,6 @@ Closes #(issue_number)
- [ ] I have updated relevant docs on [Stirling-PDF's doc repo](https://github.com/Stirling-Tools/Stirling-Tools.github.io/blob/main/docs/) (if functionality has heavily changed)
- [ ] I have read the section [Add New Translation Tags](https://github.com/Stirling-Tools/Stirling-PDF/blob/main/devGuide/HowToAddNewLanguage.md#add-new-translation-tags) (for new translation tags only)
### Translations (if applicable)
- [ ] I ran [`scripts/counter_translation.py`](https://github.com/Stirling-Tools/Stirling-PDF/blob/main/docs/counter_translation.md)
### UI Changes (if applicable)
- [ ] Screenshots or videos demonstrating the UI changes are attached (e.g., as comments or direct attachments in the PR)
-4
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@@ -1,9 +1,5 @@
changelog:
categories:
- title: Breaking Changes
labels:
- break-change
- title: Bug Fixes
labels:
- Bug
+10 -10
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@@ -14,10 +14,12 @@ Usage:
# Sample for Windows:
# python .github/scripts/check_language_toml.py --reference-file frontend/public/locales/en-GB/translation.toml --branch "" --files frontend/public/locales/de-DE/translation.toml frontend/public/locales/fr-FR/translation.toml
import copy
import glob
import os
import argparse
import re
import json
import tomllib # Python 3.11+ (stdlib)
import tomli_w # For writing TOML files
@@ -36,7 +38,7 @@ def find_duplicate_keys(file_path, keys=None, prefix=""):
duplicates = []
# Load TOML file
with open(file_path, "rb") as file:
with open(file_path, 'rb') as file:
data = tomllib.load(file)
def process_dict(obj, current_prefix=""):
@@ -65,7 +67,7 @@ def parse_toml_file(file_path):
:param file_path: Path to the TOML file.
:return: Dictionary with flattened keys.
"""
with open(file_path, "rb") as file:
with open(file_path, 'rb') as file:
data = tomllib.load(file)
def flatten_dict(d, parent_key="", sep="."):
@@ -191,13 +193,13 @@ def check_for_differences(reference_file, file_list, branch, actor):
basename_current_file = os.path.basename(os.path.join(branch, file_normpath))
locale_dir = os.path.basename(os.path.dirname(file_normpath))
if basename_current_file == basename_reference_file and locale_dir == "en-GB":
if (
basename_current_file == basename_reference_file
and locale_dir == "en-GB"
):
continue
if (
not file_normpath.endswith(".toml")
or basename_current_file != "translation.toml"
):
if not file_normpath.endswith(".toml") or basename_current_file != "translation.toml":
continue
only_reference_file = False
@@ -286,9 +288,7 @@ def check_for_differences(reference_file, file_list, branch, actor):
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="Find missing keys in TOML translation files"
)
parser = argparse.ArgumentParser(description="Find missing keys in TOML translation files")
parser.add_argument(
"--actor",
required=False,
-1
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@@ -6,4 +6,3 @@ pillow
unoserver
opencv-python-headless
pre-commit
brotli @ git+https://github.com/google/brotli.git@028fb5a23661f123017c060daa546b55cf4bde29
+420 -338
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@@ -4,98 +4,201 @@
#
# pip-compile --allow-unsafe --generate-hashes --output-file='.github\scripts\requirements_dev.txt' --strip-extras '.github\scripts\requirements_dev.in'
#
# WARNING: pip install will require the following package to be hashed.
# Consider using a hashable URL like https://github.com/jazzband/pip-tools/archive/SOMECOMMIT.zip
# CVE-2025-6176 mitigation: pin brotli to a specific commit
brotli @ git+https://github.com/google/brotli.git@028fb5a23661f123017c060daa546b55cf4bde29
# via
# -r .github/scripts/requirements_dev.in
# fonttools
cffi==2.0.0 \
--hash=sha256:00bdf7acc5f795150faa6957054fbbca2439db2f775ce831222b66f192f03beb \
--hash=sha256:07b271772c100085dd28b74fa0cd81c8fb1a3ba18b21e03d7c27f3436a10606b \
--hash=sha256:087067fa8953339c723661eda6b54bc98c5625757ea62e95eb4898ad5e776e9f \
--hash=sha256:0a1527a803f0a659de1af2e1fd700213caba79377e27e4693648c2923da066f9 \
--hash=sha256:0cf2d91ecc3fcc0625c2c530fe004f82c110405f101548512cce44322fa8ac44 \
--hash=sha256:0f6084a0ea23d05d20c3edcda20c3d006f9b6f3fefeac38f59262e10cef47ee2 \
--hash=sha256:12873ca6cb9b0f0d3a0da705d6086fe911591737a59f28b7936bdfed27c0d47c \
--hash=sha256:19f705ada2530c1167abacb171925dd886168931e0a7b78f5bffcae5c6b5be75 \
--hash=sha256:1cd13c99ce269b3ed80b417dcd591415d3372bcac067009b6e0f59c7d4015e65 \
--hash=sha256:1e3a615586f05fc4065a8b22b8152f0c1b00cdbc60596d187c2a74f9e3036e4e \
--hash=sha256:1f72fb8906754ac8a2cc3f9f5aaa298070652a0ffae577e0ea9bd480dc3c931a \
--hash=sha256:1fc9ea04857caf665289b7a75923f2c6ed559b8298a1b8c49e59f7dd95c8481e \
--hash=sha256:203a48d1fb583fc7d78a4c6655692963b860a417c0528492a6bc21f1aaefab25 \
--hash=sha256:2081580ebb843f759b9f617314a24ed5738c51d2aee65d31e02f6f7a2b97707a \
--hash=sha256:21d1152871b019407d8ac3985f6775c079416c282e431a4da6afe7aefd2bccbe \
--hash=sha256:24b6f81f1983e6df8db3adc38562c83f7d4a0c36162885ec7f7b77c7dcbec97b \
--hash=sha256:256f80b80ca3853f90c21b23ee78cd008713787b1b1e93eae9f3d6a7134abd91 \
--hash=sha256:28a3a209b96630bca57cce802da70c266eb08c6e97e5afd61a75611ee6c64592 \
--hash=sha256:2c8f814d84194c9ea681642fd164267891702542f028a15fc97d4674b6206187 \
--hash=sha256:2de9a304e27f7596cd03d16f1b7c72219bd944e99cc52b84d0145aefb07cbd3c \
--hash=sha256:38100abb9d1b1435bc4cc340bb4489635dc2f0da7456590877030c9b3d40b0c1 \
--hash=sha256:3925dd22fa2b7699ed2617149842d2e6adde22b262fcbfada50e3d195e4b3a94 \
--hash=sha256:3e17ed538242334bf70832644a32a7aae3d83b57567f9fd60a26257e992b79ba \
--hash=sha256:3e837e369566884707ddaf85fc1744b47575005c0a229de3327f8f9a20f4efeb \
--hash=sha256:3f4d46d8b35698056ec29bca21546e1551a205058ae1a181d871e278b0b28165 \
--hash=sha256:44d1b5909021139fe36001ae048dbdde8214afa20200eda0f64c068cac5d5529 \
--hash=sha256:45d5e886156860dc35862657e1494b9bae8dfa63bf56796f2fb56e1679fc0bca \
--hash=sha256:4647afc2f90d1ddd33441e5b0e85b16b12ddec4fca55f0d9671fef036ecca27c \
--hash=sha256:4671d9dd5ec934cb9a73e7ee9676f9362aba54f7f34910956b84d727b0d73fb6 \
--hash=sha256:53f77cbe57044e88bbd5ed26ac1d0514d2acf0591dd6bb02a3ae37f76811b80c \
--hash=sha256:5eda85d6d1879e692d546a078b44251cdd08dd1cfb98dfb77b670c97cee49ea0 \
--hash=sha256:5fed36fccc0612a53f1d4d9a816b50a36702c28a2aa880cb8a122b3466638743 \
--hash=sha256:61d028e90346df14fedc3d1e5441df818d095f3b87d286825dfcbd6459b7ef63 \
--hash=sha256:66f011380d0e49ed280c789fbd08ff0d40968ee7b665575489afa95c98196ab5 \
--hash=sha256:6824f87845e3396029f3820c206e459ccc91760e8fa24422f8b0c3d1731cbec5 \
--hash=sha256:6c6c373cfc5c83a975506110d17457138c8c63016b563cc9ed6e056a82f13ce4 \
--hash=sha256:6d02d6655b0e54f54c4ef0b94eb6be0607b70853c45ce98bd278dc7de718be5d \
--hash=sha256:6d50360be4546678fc1b79ffe7a66265e28667840010348dd69a314145807a1b \
--hash=sha256:730cacb21e1bdff3ce90babf007d0a0917cc3e6492f336c2f0134101e0944f93 \
--hash=sha256:737fe7d37e1a1bffe70bd5754ea763a62a066dc5913ca57e957824b72a85e205 \
--hash=sha256:74a03b9698e198d47562765773b4a8309919089150a0bb17d829ad7b44b60d27 \
--hash=sha256:7553fb2090d71822f02c629afe6042c299edf91ba1bf94951165613553984512 \
--hash=sha256:7a66c7204d8869299919db4d5069a82f1561581af12b11b3c9f48c584eb8743d \
--hash=sha256:7cc09976e8b56f8cebd752f7113ad07752461f48a58cbba644139015ac24954c \
--hash=sha256:81afed14892743bbe14dacb9e36d9e0e504cd204e0b165062c488942b9718037 \
--hash=sha256:8941aaadaf67246224cee8c3803777eed332a19d909b47e29c9842ef1e79ac26 \
--hash=sha256:89472c9762729b5ae1ad974b777416bfda4ac5642423fa93bd57a09204712322 \
--hash=sha256:8ea985900c5c95ce9db1745f7933eeef5d314f0565b27625d9a10ec9881e1bfb \
--hash=sha256:8eca2a813c1cb7ad4fb74d368c2ffbbb4789d377ee5bb8df98373c2cc0dee76c \
--hash=sha256:92b68146a71df78564e4ef48af17551a5ddd142e5190cdf2c5624d0c3ff5b2e8 \
--hash=sha256:9332088d75dc3241c702d852d4671613136d90fa6881da7d770a483fd05248b4 \
--hash=sha256:94698a9c5f91f9d138526b48fe26a199609544591f859c870d477351dc7b2414 \
--hash=sha256:9a67fc9e8eb39039280526379fb3a70023d77caec1852002b4da7e8b270c4dd9 \
--hash=sha256:9de40a7b0323d889cf8d23d1ef214f565ab154443c42737dfe52ff82cf857664 \
--hash=sha256:a05d0c237b3349096d3981b727493e22147f934b20f6f125a3eba8f994bec4a9 \
--hash=sha256:afb8db5439b81cf9c9d0c80404b60c3cc9c3add93e114dcae767f1477cb53775 \
--hash=sha256:b18a3ed7d5b3bd8d9ef7a8cb226502c6bf8308df1525e1cc676c3680e7176739 \
--hash=sha256:b1e74d11748e7e98e2f426ab176d4ed720a64412b6a15054378afdb71e0f37dc \
--hash=sha256:b21e08af67b8a103c71a250401c78d5e0893beff75e28c53c98f4de42f774062 \
--hash=sha256:b4c854ef3adc177950a8dfc81a86f5115d2abd545751a304c5bcf2c2c7283cfe \
--hash=sha256:b882b3df248017dba09d6b16defe9b5c407fe32fc7c65a9c69798e6175601be9 \
--hash=sha256:baf5215e0ab74c16e2dd324e8ec067ef59e41125d3eade2b863d294fd5035c92 \
--hash=sha256:c649e3a33450ec82378822b3dad03cc228b8f5963c0c12fc3b1e0ab940f768a5 \
--hash=sha256:c654de545946e0db659b3400168c9ad31b5d29593291482c43e3564effbcee13 \
--hash=sha256:c6638687455baf640e37344fe26d37c404db8b80d037c3d29f58fe8d1c3b194d \
--hash=sha256:c8d3b5532fc71b7a77c09192b4a5a200ea992702734a2e9279a37f2478236f26 \
--hash=sha256:cb527a79772e5ef98fb1d700678fe031e353e765d1ca2d409c92263c6d43e09f \
--hash=sha256:cf364028c016c03078a23b503f02058f1814320a56ad535686f90565636a9495 \
--hash=sha256:d48a880098c96020b02d5a1f7d9251308510ce8858940e6fa99ece33f610838b \
--hash=sha256:d68b6cef7827e8641e8ef16f4494edda8b36104d79773a334beaa1e3521430f6 \
--hash=sha256:d9b29c1f0ae438d5ee9acb31cadee00a58c46cc9c0b2f9038c6b0b3470877a8c \
--hash=sha256:d9b97165e8aed9272a6bb17c01e3cc5871a594a446ebedc996e2397a1c1ea8ef \
--hash=sha256:da68248800ad6320861f129cd9c1bf96ca849a2771a59e0344e88681905916f5 \
--hash=sha256:da902562c3e9c550df360bfa53c035b2f241fed6d9aef119048073680ace4a18 \
--hash=sha256:dbd5c7a25a7cb98f5ca55d258b103a2054f859a46ae11aaf23134f9cc0d356ad \
--hash=sha256:dd4f05f54a52fb558f1ba9f528228066954fee3ebe629fc1660d874d040ae5a3 \
--hash=sha256:de8dad4425a6ca6e4e5e297b27b5c824ecc7581910bf9aee86cb6835e6812aa7 \
--hash=sha256:e11e82b744887154b182fd3e7e8512418446501191994dbf9c9fc1f32cc8efd5 \
--hash=sha256:e6e73b9e02893c764e7e8d5bb5ce277f1a009cd5243f8228f75f842bf937c534 \
--hash=sha256:f73b96c41e3b2adedc34a7356e64c8eb96e03a3782b535e043a986276ce12a49 \
--hash=sha256:f93fd8e5c8c0a4aa1f424d6173f14a892044054871c771f8566e4008eaa359d2 \
--hash=sha256:fc33c5141b55ed366cfaad382df24fe7dcbc686de5be719b207bb248e3053dc5 \
--hash=sha256:fc7de24befaeae77ba923797c7c87834c73648a05a4bde34b3b7e5588973a453 \
--hash=sha256:fe562eb1a64e67dd297ccc4f5addea2501664954f2692b69a76449ec7913ecbf
brotli==1.1.0 \
--hash=sha256:03d20af184290887bdea3f0f78c4f737d126c74dc2f3ccadf07e54ceca3bf208 \
--hash=sha256:0541e747cce78e24ea12d69176f6a7ddb690e62c425e01d31cc065e69ce55b48 \
--hash=sha256:069a121ac97412d1fe506da790b3e69f52254b9df4eb665cd42460c837193354 \
--hash=sha256:0737ddb3068957cf1b054899b0883830bb1fec522ec76b1098f9b6e0f02d9419 \
--hash=sha256:0b63b949ff929fbc2d6d3ce0e924c9b93c9785d877a21a1b678877ffbbc4423a \
--hash=sha256:0c6244521dda65ea562d5a69b9a26120769b7a9fb3db2fe9545935ed6735b128 \
--hash=sha256:11d00ed0a83fa22d29bc6b64ef636c4552ebafcef57154b4ddd132f5638fbd1c \
--hash=sha256:141bd4d93984070e097521ed07e2575b46f817d08f9fa42b16b9b5f27b5ac088 \
--hash=sha256:19c116e796420b0cee3da1ccec3b764ed2952ccfcc298b55a10e5610ad7885f9 \
--hash=sha256:1ab4fbee0b2d9098c74f3057b2bc055a8bd92ccf02f65944a241b4349229185a \
--hash=sha256:1ae56aca0402a0f9a3431cddda62ad71666ca9d4dc3a10a142b9dce2e3c0cda3 \
--hash=sha256:1b2c248cd517c222d89e74669a4adfa5577e06ab68771a529060cf5a156e9757 \
--hash=sha256:1e9a65b5736232e7a7f91ff3d02277f11d339bf34099a56cdab6a8b3410a02b2 \
--hash=sha256:224e57f6eac61cc449f498cc5f0e1725ba2071a3d4f48d5d9dffba42db196438 \
--hash=sha256:22fc2a8549ffe699bfba2256ab2ed0421a7b8fadff114a3d201794e45a9ff578 \
--hash=sha256:23032ae55523cc7bccb4f6a0bf368cd25ad9bcdcc1990b64a647e7bbcce9cb5b \
--hash=sha256:2333e30a5e00fe0fe55903c8832e08ee9c3b1382aacf4db26664a16528d51b4b \
--hash=sha256:2954c1c23f81c2eaf0b0717d9380bd348578a94161a65b3a2afc62c86467dd68 \
--hash=sha256:2a24c50840d89ded6c9a8fdc7b6ed3692ed4e86f1c4a4a938e1e92def92933e0 \
--hash=sha256:2de9d02f5bda03d27ede52e8cfe7b865b066fa49258cbab568720aa5be80a47d \
--hash=sha256:2feb1d960f760a575dbc5ab3b1c00504b24caaf6986e2dc2b01c09c87866a943 \
--hash=sha256:30924eb4c57903d5a7526b08ef4a584acc22ab1ffa085faceb521521d2de32dd \
--hash=sha256:316cc9b17edf613ac76b1f1f305d2a748f1b976b033b049a6ecdfd5612c70409 \
--hash=sha256:32d95b80260d79926f5fab3c41701dbb818fde1c9da590e77e571eefd14abe28 \
--hash=sha256:38025d9f30cf4634f8309c6874ef871b841eb3c347e90b0851f63d1ded5212da \
--hash=sha256:39da8adedf6942d76dc3e46653e52df937a3c4d6d18fdc94a7c29d263b1f5b50 \
--hash=sha256:3c0ef38c7a7014ffac184db9e04debe495d317cc9c6fb10071f7fefd93100a4f \
--hash=sha256:3d7954194c36e304e1523f55d7042c59dc53ec20dd4e9ea9d151f1b62b4415c0 \
--hash=sha256:3ee8a80d67a4334482d9712b8e83ca6b1d9bc7e351931252ebef5d8f7335a547 \
--hash=sha256:4093c631e96fdd49e0377a9c167bfd75b6d0bad2ace734c6eb20b348bc3ea180 \
--hash=sha256:43395e90523f9c23a3d5bdf004733246fba087f2948f87ab28015f12359ca6a0 \
--hash=sha256:43ce1b9935bfa1ede40028054d7f48b5469cd02733a365eec8a329ffd342915d \
--hash=sha256:4410f84b33374409552ac9b6903507cdb31cd30d2501fc5ca13d18f73548444a \
--hash=sha256:494994f807ba0b92092a163a0a283961369a65f6cbe01e8891132b7a320e61eb \
--hash=sha256:4d4a848d1837973bf0f4b5e54e3bec977d99be36a7895c61abb659301b02c112 \
--hash=sha256:4ed11165dd45ce798d99a136808a794a748d5dc38511303239d4e2363c0695dc \
--hash=sha256:4f3607b129417e111e30637af1b56f24f7a49e64763253bbc275c75fa887d4b2 \
--hash=sha256:510b5b1bfbe20e1a7b3baf5fed9e9451873559a976c1a78eebaa3b86c57b4265 \
--hash=sha256:524f35912131cc2cabb00edfd8d573b07f2d9f21fa824bd3fb19725a9cf06327 \
--hash=sha256:587ca6d3cef6e4e868102672d3bd9dc9698c309ba56d41c2b9c85bbb903cdb95 \
--hash=sha256:58d4b711689366d4a03ac7957ab8c28890415e267f9b6589969e74b6e42225ec \
--hash=sha256:5b3cc074004d968722f51e550b41a27be656ec48f8afaeeb45ebf65b561481dd \
--hash=sha256:5dab0844f2cf82be357a0eb11a9087f70c5430b2c241493fc122bb6f2bb0917c \
--hash=sha256:5e55da2c8724191e5b557f8e18943b1b4839b8efc3ef60d65985bcf6f587dd38 \
--hash=sha256:5eeb539606f18a0b232d4ba45adccde4125592f3f636a6182b4a8a436548b914 \
--hash=sha256:5f4d5ea15c9382135076d2fb28dde923352fe02951e66935a9efaac8f10e81b0 \
--hash=sha256:5fb2ce4b8045c78ebbc7b8f3c15062e435d47e7393cc57c25115cfd49883747a \
--hash=sha256:6172447e1b368dcbc458925e5ddaf9113477b0ed542df258d84fa28fc45ceea7 \
--hash=sha256:6967ced6730aed543b8673008b5a391c3b1076d834ca438bbd70635c73775368 \
--hash=sha256:6974f52a02321b36847cd19d1b8e381bf39939c21efd6ee2fc13a28b0d99348c \
--hash=sha256:6c3020404e0b5eefd7c9485ccf8393cfb75ec38ce75586e046573c9dc29967a0 \
--hash=sha256:6c6e0c425f22c1c719c42670d561ad682f7bfeeef918edea971a79ac5252437f \
--hash=sha256:70051525001750221daa10907c77830bc889cb6d865cc0b813d9db7fefc21451 \
--hash=sha256:7905193081db9bfa73b1219140b3d315831cbff0d8941f22da695832f0dd188f \
--hash=sha256:7bc37c4d6b87fb1017ea28c9508b36bbcb0c3d18b4260fcdf08b200c74a6aee8 \
--hash=sha256:7c4855522edb2e6ae7fdb58e07c3ba9111e7621a8956f481c68d5d979c93032e \
--hash=sha256:7e4c4629ddad63006efa0ef968c8e4751c5868ff0b1c5c40f76524e894c50248 \
--hash=sha256:7eedaa5d036d9336c95915035fb57422054014ebdeb6f3b42eac809928e40d0c \
--hash=sha256:7f4bf76817c14aa98cc6697ac02f3972cb8c3da93e9ef16b9c66573a68014f91 \
--hash=sha256:81de08ac11bcb85841e440c13611c00b67d3bf82698314928d0b676362546724 \
--hash=sha256:832436e59afb93e1836081a20f324cb185836c617659b07b129141a8426973c7 \
--hash=sha256:861bf317735688269936f755fa136a99d1ed526883859f86e41a5d43c61d8966 \
--hash=sha256:87a3044c3a35055527ac75e419dfa9f4f3667a1e887ee80360589eb8c90aabb9 \
--hash=sha256:890b5a14ce214389b2cc36ce82f3093f96f4cc730c1cffdbefff77a7c71f2a97 \
--hash=sha256:89f4988c7203739d48c6f806f1e87a1d96e0806d44f0fba61dba81392c9e474d \
--hash=sha256:8bf32b98b75c13ec7cf774164172683d6e7891088f6316e54425fde1efc276d5 \
--hash=sha256:8dadd1314583ec0bf2d1379f7008ad627cd6336625d6679cf2f8e67081b83acf \
--hash=sha256:901032ff242d479a0efa956d853d16875d42157f98951c0230f69e69f9c09bac \
--hash=sha256:9011560a466d2eb3f5a6e4929cf4a09be405c64154e12df0dd72713f6500e32b \
--hash=sha256:906bc3a79de8c4ae5b86d3d75a8b77e44404b0f4261714306e3ad248d8ab0951 \
--hash=sha256:919e32f147ae93a09fe064d77d5ebf4e35502a8df75c29fb05788528e330fe74 \
--hash=sha256:91d7cc2a76b5567591d12c01f019dd7afce6ba8cba6571187e21e2fc418ae648 \
--hash=sha256:929811df5462e182b13920da56c6e0284af407d1de637d8e536c5cd00a7daf60 \
--hash=sha256:949f3b7c29912693cee0afcf09acd6ebc04c57af949d9bf77d6101ebb61e388c \
--hash=sha256:a090ca607cbb6a34b0391776f0cb48062081f5f60ddcce5d11838e67a01928d1 \
--hash=sha256:a1fd8a29719ccce974d523580987b7f8229aeace506952fa9ce1d53a033873c8 \
--hash=sha256:a37b8f0391212d29b3a91a799c8e4a2855e0576911cdfb2515487e30e322253d \
--hash=sha256:a3daabb76a78f829cafc365531c972016e4aa8d5b4bf60660ad8ecee19df7ccc \
--hash=sha256:a469274ad18dc0e4d316eefa616d1d0c2ff9da369af19fa6f3daa4f09671fd61 \
--hash=sha256:a599669fd7c47233438a56936988a2478685e74854088ef5293802123b5b2460 \
--hash=sha256:a743e5a28af5f70f9c080380a5f908d4d21d40e8f0e0c8901604d15cfa9ba751 \
--hash=sha256:a77def80806c421b4b0af06f45d65a136e7ac0bdca3c09d9e2ea4e515367c7e9 \
--hash=sha256:a7e53012d2853a07a4a79c00643832161a910674a893d296c9f1259859a289d2 \
--hash=sha256:a93dde851926f4f2678e704fadeb39e16c35d8baebd5252c9fd94ce8ce68c4a0 \
--hash=sha256:aac0411d20e345dc0920bdec5548e438e999ff68d77564d5e9463a7ca9d3e7b1 \
--hash=sha256:ae15b066e5ad21366600ebec29a7ccbc86812ed267e4b28e860b8ca16a2bc474 \
--hash=sha256:aea440a510e14e818e67bfc4027880e2fb500c2ccb20ab21c7a7c8b5b4703d75 \
--hash=sha256:af6fa6817889314555aede9a919612b23739395ce767fe7fcbea9a80bf140fe5 \
--hash=sha256:b760c65308ff1e462f65d69c12e4ae085cff3b332d894637f6273a12a482d09f \
--hash=sha256:be36e3d172dc816333f33520154d708a2657ea63762ec16b62ece02ab5e4daf2 \
--hash=sha256:c247dd99d39e0338a604f8c2b3bc7061d5c2e9e2ac7ba9cc1be5a69cb6cd832f \
--hash=sha256:c5529b34c1c9d937168297f2c1fde7ebe9ebdd5e121297ff9c043bdb2ae3d6fb \
--hash=sha256:c8146669223164fc87a7e3de9f81e9423c67a79d6b3447994dfb9c95da16e2d6 \
--hash=sha256:c8fd5270e906eef71d4a8d19b7c6a43760c6abcfcc10c9101d14eb2357418de9 \
--hash=sha256:ca63e1890ede90b2e4454f9a65135a4d387a4585ff8282bb72964fab893f2111 \
--hash=sha256:caf9ee9a5775f3111642d33b86237b05808dafcd6268faa492250e9b78046eb2 \
--hash=sha256:cb1dac1770878ade83f2ccdf7d25e494f05c9165f5246b46a621cc849341dc01 \
--hash=sha256:cdad5b9014d83ca68c25d2e9444e28e967ef16e80f6b436918c700c117a85467 \
--hash=sha256:cdbc1fc1bc0bff1cef838eafe581b55bfbffaed4ed0318b724d0b71d4d377619 \
--hash=sha256:ceb64bbc6eac5a140ca649003756940f8d6a7c444a68af170b3187623b43bebf \
--hash=sha256:d0c5516f0aed654134a2fc936325cc2e642f8a0e096d075209672eb321cff408 \
--hash=sha256:d143fd47fad1db3d7c27a1b1d66162e855b5d50a89666af46e1679c496e8e579 \
--hash=sha256:d192f0f30804e55db0d0e0a35d83a9fead0e9a359a9ed0285dbacea60cc10a84 \
--hash=sha256:d2b35ca2c7f81d173d2fadc2f4f31e88cc5f7a39ae5b6db5513cf3383b0e0ec7 \
--hash=sha256:d342778ef319e1026af243ed0a07c97acf3bad33b9f29e7ae6a1f68fd083e90c \
--hash=sha256:d487f5432bf35b60ed625d7e1b448e2dc855422e87469e3f450aa5552b0eb284 \
--hash=sha256:d7702622a8b40c49bffb46e1e3ba2e81268d5c04a34f460978c6b5517a34dd52 \
--hash=sha256:db85ecf4e609a48f4b29055f1e144231b90edc90af7481aa731ba2d059226b1b \
--hash=sha256:de6551e370ef19f8de1807d0a9aa2cdfdce2e85ce88b122fe9f6b2b076837e59 \
--hash=sha256:e1140c64812cb9b06c922e77f1c26a75ec5e3f0fb2bf92cc8c58720dec276752 \
--hash=sha256:e4fe605b917c70283db7dfe5ada75e04561479075761a0b3866c081d035b01c1 \
--hash=sha256:e6a904cb26bfefc2f0a6f240bdf5233be78cd2488900a2f846f3c3ac8489ab80 \
--hash=sha256:e79e6520141d792237c70bcd7a3b122d00f2613769ae0cb61c52e89fd3443839 \
--hash=sha256:e84799f09591700a4154154cab9787452925578841a94321d5ee8fb9a9a328f0 \
--hash=sha256:e93dfc1a1165e385cc8239fab7c036fb2cd8093728cbd85097b284d7b99249a2 \
--hash=sha256:efa8b278894b14d6da122a72fefcebc28445f2d3f880ac59d46c90f4c13be9a3 \
--hash=sha256:f0d8a7a6b5983c2496e364b969f0e526647a06b075d034f3297dc66f3b360c64 \
--hash=sha256:f0db75f47be8b8abc8d9e31bc7aad0547ca26f24a54e6fd10231d623f183d089 \
--hash=sha256:f296c40e23065d0d6650c4aefe7470d2a25fffda489bcc3eb66083f3ac9f6643 \
--hash=sha256:f31859074d57b4639318523d6ffdca586ace54271a73ad23ad021acd807eb14b \
--hash=sha256:f66b5337fa213f1da0d9000bc8dc0cb5b896b726eefd9c6046f699b169c41b9e \
--hash=sha256:f733d788519c7e3e71f0855c96618720f5d3d60c3cb829d8bbb722dddce37985 \
--hash=sha256:fce1473f3ccc4187f75b4690cfc922628aed4d3dd013d047f95a9b3919a86596 \
--hash=sha256:fd5f17ff8f14003595ab414e45fce13d073e0762394f957182e69035c9f3d7c2 \
--hash=sha256:fdc3ff3bfccdc6b9cc7c342c03aa2400683f0cb891d46e94b64a197910dc4064
# via fonttools
cffi==1.17.1 \
--hash=sha256:045d61c734659cc045141be4bae381a41d89b741f795af1dd018bfb532fd0df8 \
--hash=sha256:0984a4925a435b1da406122d4d7968dd861c1385afe3b45ba82b750f229811e2 \
--hash=sha256:0e2b1fac190ae3ebfe37b979cc1ce69c81f4e4fe5746bb401dca63a9062cdaf1 \
--hash=sha256:0f048dcf80db46f0098ccac01132761580d28e28bc0f78ae0d58048063317e15 \
--hash=sha256:1257bdabf294dceb59f5e70c64a3e2f462c30c7ad68092d01bbbfb1c16b1ba36 \
--hash=sha256:1c39c6016c32bc48dd54561950ebd6836e1670f2ae46128f67cf49e789c52824 \
--hash=sha256:1d599671f396c4723d016dbddb72fe8e0397082b0a77a4fab8028923bec050e8 \
--hash=sha256:28b16024becceed8c6dfbc75629e27788d8a3f9030691a1dbf9821a128b22c36 \
--hash=sha256:2bb1a08b8008b281856e5971307cc386a8e9c5b625ac297e853d36da6efe9c17 \
--hash=sha256:30c5e0cb5ae493c04c8b42916e52ca38079f1b235c2f8ae5f4527b963c401caf \
--hash=sha256:31000ec67d4221a71bd3f67df918b1f88f676f1c3b535a7eb473255fdc0b83fc \
--hash=sha256:386c8bf53c502fff58903061338ce4f4950cbdcb23e2902d86c0f722b786bbe3 \
--hash=sha256:3edc8d958eb099c634dace3c7e16560ae474aa3803a5df240542b305d14e14ed \
--hash=sha256:45398b671ac6d70e67da8e4224a065cec6a93541bb7aebe1b198a61b58c7b702 \
--hash=sha256:46bf43160c1a35f7ec506d254e5c890f3c03648a4dbac12d624e4490a7046cd1 \
--hash=sha256:4ceb10419a9adf4460ea14cfd6bc43d08701f0835e979bf821052f1805850fe8 \
--hash=sha256:51392eae71afec0d0c8fb1a53b204dbb3bcabcb3c9b807eedf3e1e6ccf2de903 \
--hash=sha256:5da5719280082ac6bd9aa7becb3938dc9f9cbd57fac7d2871717b1feb0902ab6 \
--hash=sha256:610faea79c43e44c71e1ec53a554553fa22321b65fae24889706c0a84d4ad86d \
--hash=sha256:636062ea65bd0195bc012fea9321aca499c0504409f413dc88af450b57ffd03b \
--hash=sha256:6883e737d7d9e4899a8a695e00ec36bd4e5e4f18fabe0aca0efe0a4b44cdb13e \
--hash=sha256:6b8b4a92e1c65048ff98cfe1f735ef8f1ceb72e3d5f0c25fdb12087a23da22be \
--hash=sha256:6f17be4345073b0a7b8ea599688f692ac3ef23ce28e5df79c04de519dbc4912c \
--hash=sha256:706510fe141c86a69c8ddc029c7910003a17353970cff3b904ff0686a5927683 \
--hash=sha256:72e72408cad3d5419375fc87d289076ee319835bdfa2caad331e377589aebba9 \
--hash=sha256:733e99bc2df47476e3848417c5a4540522f234dfd4ef3ab7fafdf555b082ec0c \
--hash=sha256:7596d6620d3fa590f677e9ee430df2958d2d6d6de2feeae5b20e82c00b76fbf8 \
--hash=sha256:78122be759c3f8a014ce010908ae03364d00a1f81ab5c7f4a7a5120607ea56e1 \
--hash=sha256:805b4371bf7197c329fcb3ead37e710d1bca9da5d583f5073b799d5c5bd1eee4 \
--hash=sha256:85a950a4ac9c359340d5963966e3e0a94a676bd6245a4b55bc43949eee26a655 \
--hash=sha256:8f2cdc858323644ab277e9bb925ad72ae0e67f69e804f4898c070998d50b1a67 \
--hash=sha256:9755e4345d1ec879e3849e62222a18c7174d65a6a92d5b346b1863912168b595 \
--hash=sha256:98e3969bcff97cae1b2def8ba499ea3d6f31ddfdb7635374834cf89a1a08ecf0 \
--hash=sha256:a08d7e755f8ed21095a310a693525137cfe756ce62d066e53f502a83dc550f65 \
--hash=sha256:a1ed2dd2972641495a3ec98445e09766f077aee98a1c896dcb4ad0d303628e41 \
--hash=sha256:a24ed04c8ffd54b0729c07cee15a81d964e6fee0e3d4d342a27b020d22959dc6 \
--hash=sha256:a45e3c6913c5b87b3ff120dcdc03f6131fa0065027d0ed7ee6190736a74cd401 \
--hash=sha256:a9b15d491f3ad5d692e11f6b71f7857e7835eb677955c00cc0aefcd0669adaf6 \
--hash=sha256:ad9413ccdeda48c5afdae7e4fa2192157e991ff761e7ab8fdd8926f40b160cc3 \
--hash=sha256:b2ab587605f4ba0bf81dc0cb08a41bd1c0a5906bd59243d56bad7668a6fc6c16 \
--hash=sha256:b62ce867176a75d03a665bad002af8e6d54644fad99a3c70905c543130e39d93 \
--hash=sha256:c03e868a0b3bc35839ba98e74211ed2b05d2119be4e8a0f224fba9384f1fe02e \
--hash=sha256:c59d6e989d07460165cc5ad3c61f9fd8f1b4796eacbd81cee78957842b834af4 \
--hash=sha256:c7eac2ef9b63c79431bc4b25f1cd649d7f061a28808cbc6c47b534bd789ef964 \
--hash=sha256:c9c3d058ebabb74db66e431095118094d06abf53284d9c81f27300d0e0d8bc7c \
--hash=sha256:ca74b8dbe6e8e8263c0ffd60277de77dcee6c837a3d0881d8c1ead7268c9e576 \
--hash=sha256:caaf0640ef5f5517f49bc275eca1406b0ffa6aa184892812030f04c2abf589a0 \
--hash=sha256:cdf5ce3acdfd1661132f2a9c19cac174758dc2352bfe37d98aa7512c6b7178b3 \
--hash=sha256:d016c76bdd850f3c626af19b0542c9677ba156e4ee4fccfdd7848803533ef662 \
--hash=sha256:d01b12eeeb4427d3110de311e1774046ad344f5b1a7403101878976ecd7a10f3 \
--hash=sha256:d63afe322132c194cf832bfec0dc69a99fb9bb6bbd550f161a49e9e855cc78ff \
--hash=sha256:da95af8214998d77a98cc14e3a3bd00aa191526343078b530ceb0bd710fb48a5 \
--hash=sha256:dd398dbc6773384a17fe0d3e7eeb8d1a21c2200473ee6806bb5e6a8e62bb73dd \
--hash=sha256:de2ea4b5833625383e464549fec1bc395c1bdeeb5f25c4a3a82b5a8c756ec22f \
--hash=sha256:de55b766c7aa2e2a3092c51e0483d700341182f08e67c63630d5b6f200bb28e5 \
--hash=sha256:df8b1c11f177bc2313ec4b2d46baec87a5f3e71fc8b45dab2ee7cae86d9aba14 \
--hash=sha256:e03eab0a8677fa80d646b5ddece1cbeaf556c313dcfac435ba11f107ba117b5d \
--hash=sha256:e221cf152cff04059d011ee126477f0d9588303eb57e88923578ace7baad17f9 \
--hash=sha256:e31ae45bc2e29f6b2abd0de1cc3b9d5205aa847cafaecb8af1476a609a2f6eb7 \
--hash=sha256:edae79245293e15384b51f88b00613ba9f7198016a5948b5dddf4917d4d26382 \
--hash=sha256:f1e22e8c4419538cb197e4dd60acc919d7696e5ef98ee4da4e01d3f8cfa4cc5a \
--hash=sha256:f3a2b4222ce6b60e2e8b337bb9596923045681d71e5a082783484d845390938e \
--hash=sha256:f6a16c31041f09ead72d69f583767292f750d24913dadacf5756b966aacb3f1a \
--hash=sha256:f75c7ab1f9e4aca5414ed4d8e5c0e303a34f4421f8a0d47a4d019ceff0ab6af4 \
--hash=sha256:f79fc4fc25f1c8698ff97788206bb3c2598949bfe0fef03d299eb1b5356ada99 \
--hash=sha256:f7f5baafcc48261359e14bcd6d9bff6d4b28d9103847c9e136694cb0501aef87 \
--hash=sha256:fc48c783f9c87e60831201f2cce7f3b2e4846bf4d8728eabe54d60700b318a0b
# via weasyprint
cfgv==3.4.0 \
--hash=sha256:b7265b1f29fd3316bfcd2b330d63d024f2bfd8bcb8b0272f8e19a504856c48f9 \
@@ -109,73 +212,57 @@ distlib==0.4.0 \
--hash=sha256:9659f7d87e46584a30b5780e43ac7a2143098441670ff0a49d5f9034c54a6c16 \
--hash=sha256:feec40075be03a04501a973d81f633735b4b69f98b05450592310c0f401a4e0d
# via virtualenv
filelock==3.20.0 \
--hash=sha256:339b4732ffda5cd79b13f4e2711a31b0365ce445d95d243bb996273d072546a2 \
--hash=sha256:711e943b4ec6be42e1d4e6690b48dc175c822967466bb31c0c293f34334c13f4
filelock==3.18.0 \
--hash=sha256:adbc88eabb99d2fec8c9c1b229b171f18afa655400173ddc653d5d01501fb9f2 \
--hash=sha256:c401f4f8377c4464e6db25fff06205fd89bdd83b65eb0488ed1b160f780e21de
# via virtualenv
fonttools==4.60.1 \
--hash=sha256:022beaea4b73a70295b688f817ddc24ed3e3418b5036ffcd5658141184ef0d0c \
--hash=sha256:026290e4ec76583881763fac284aca67365e0be9f13a7fb137257096114cb3bc \
--hash=sha256:0b0835ed15dd5b40d726bb61c846a688f5b4ce2208ec68779bc81860adb5851a \
--hash=sha256:0eae96373e4b7c9e45d099d7a523444e3554360927225c1cdae221a58a45b856 \
--hash=sha256:122e1a8ada290423c493491d002f622b1992b1ab0b488c68e31c413390dc7eb2 \
--hash=sha256:1410155d0e764a4615774e5c2c6fc516259fe3eca5882f034eb9bfdbee056259 \
--hash=sha256:145daa14bf24824b677b9357c5e44fd8895c2a8f53596e1b9ea3496081dc692c \
--hash=sha256:1525796c3ffe27bb6268ed2a1bb0dcf214d561dfaf04728abf01489eb5339dce \
--hash=sha256:154cb6ee417e417bf5f7c42fe25858c9140c26f647c7347c06f0cc2d47eff003 \
--hash=sha256:2299df884c11162617a66b7c316957d74a18e3758c0274762d2cc87df7bc0272 \
--hash=sha256:2409d5fb7b55fd70f715e6d34e7a6e4f7511b8ad29a49d6df225ee76da76dd77 \
--hash=sha256:268ecda8ca6cb5c4f044b1fb9b3b376e8cd1b361cef275082429dc4174907038 \
--hash=sha256:282dafa55f9659e8999110bd8ed422ebe1c8aecd0dc396550b038e6c9a08b8ea \
--hash=sha256:2ee06fc57512144d8b0445194c2da9f190f61ad51e230f14836286470c99f854 \
--hash=sha256:3630e86c484263eaac71d117085d509cbcf7b18f677906824e4bace598fb70d2 \
--hash=sha256:398447f3d8c0c786cbf1209711e79080a40761eb44b27cdafffb48f52bcec258 \
--hash=sha256:4ba4bd646e86de16160f0fb72e31c3b9b7d0721c3e5b26b9fa2fc931dfdb2652 \
--hash=sha256:5664fd1a9ea7f244487ac8f10340c4e37664675e8667d6fee420766e0fb3cf08 \
--hash=sha256:583b7f8e3c49486e4d489ad1deacfb8d5be54a8ef34d6df824f6a171f8511d99 \
--hash=sha256:596ecaca36367027d525b3b426d8a8208169d09edcf8c7506aceb3a38bfb55c7 \
--hash=sha256:5c1015318e4fec75dd4943ad5f6a206d9727adf97410d58b7e32ab644a807914 \
--hash=sha256:66929e2ea2810c6533a5184f938502cfdaea4bc3efb7130d8cc02e1c1b4108d6 \
--hash=sha256:6ec722ee589e89a89f5b7574f5c45604030aa6ae24cb2c751e2707193b466fed \
--hash=sha256:6f68576bb4bbf6060c7ab047b1574a1ebe5c50a17de62830079967b211059ebb \
--hash=sha256:7473a8ed9ed09aeaa191301244a5a9dbe46fe0bf54f9d6cd21d83044c3321217 \
--hash=sha256:7b0c6d57ab00dae9529f3faf187f2254ea0aa1e04215cf2f1a8ec277c96661bc \
--hash=sha256:7b4c32e232a71f63a5d00259ca3d88345ce2a43295bb049d21061f338124246f \
--hash=sha256:8177ec9676ea6e1793c8a084a90b65a9f778771998eb919d05db6d4b1c0b114c \
--hash=sha256:839565cbf14645952d933853e8ade66a463684ed6ed6c9345d0faf1f0e868877 \
--hash=sha256:875cb7764708b3132637f6c5fb385b16eeba0f7ac9fa45a69d35e09b47045801 \
--hash=sha256:8a44788d9d91df72d1a5eac49b31aeb887a5f4aab761b4cffc4196c74907ea85 \
--hash=sha256:8b4eb332f9501cb1cd3d4d099374a1e1306783ff95489a1026bde9eb02ccc34a \
--hash=sha256:906306ac7afe2156fcf0042173d6ebbb05416af70f6b370967b47f8f00103bbb \
--hash=sha256:992775c9fbe2cf794786fa0ffca7f09f564ba3499b8fe9f2f80bd7197db60383 \
--hash=sha256:996a4d1834524adbb423385d5a629b868ef9d774670856c63c9a0408a3063401 \
--hash=sha256:9a52f254ce051e196b8fe2af4634c2d2f02c981756c6464dc192f1b6050b4e28 \
--hash=sha256:9d0ced62b59e0430b3690dbc5373df1c2aa7585e9a8ce38eff87f0fd993c5b01 \
--hash=sha256:a140761c4ff63d0cb9256ac752f230460ee225ccef4ad8f68affc723c88e2036 \
--hash=sha256:a184b2ea57b13680ab6d5fbde99ccef152c95c06746cb7718c583abd8f945ccc \
--hash=sha256:a3db56f153bd4c5c2b619ab02c5db5192e222150ce5a1bc10f16164714bc39ac \
--hash=sha256:a46b2f450bc79e06ef3b6394f0c68660529ed51692606ad7f953fc2e448bc903 \
--hash=sha256:a884aef09d45ba1206712c7dbda5829562d3fea7726935d3289d343232ecb0d3 \
--hash=sha256:b2cf105cee600d2de04ca3cfa1f74f1127f8455b71dbad02b9da6ec266e116d6 \
--hash=sha256:b33a7884fabd72bdf5f910d0cf46be50dce86a0362a65cfc746a4168c67eb96c \
--hash=sha256:b42d86938e8dda1cd9a1a87a6d82f1818eaf933348429653559a458d027446da \
--hash=sha256:b6379e7546ba4ae4b18f8ae2b9bc5960936007a1c0e30b342f662577e8bc3299 \
--hash=sha256:c7420a2696a44650120cdd269a5d2e56a477e2bfa9d95e86229059beb1c19e15 \
--hash=sha256:c8651e0d4b3bdeda6602b85fdc2abbefc1b41e573ecb37b6779c4ca50753a199 \
--hash=sha256:d066ea419f719ed87bc2c99a4a4bfd77c2e5949cb724588b9dd58f3fd90b92bf \
--hash=sha256:e6c58beb17380f7c2ea181ea11e7db8c0ceb474c9dd45f48e71e2cb577d146a1 \
--hash=sha256:e852d9dda9f93ad3651ae1e3bb770eac544ec93c3807888798eccddf84596537 \
--hash=sha256:ec3681a0cb34c255d76dd9d865a55f260164adb9fa02628415cdc2d43ee2c05d \
--hash=sha256:ee0c0b3b35b34f782afc673d503167157094a16f442ace7c6c5e0ca80b08f50c \
--hash=sha256:eedacb5c5d22b7097482fa834bda0dafa3d914a4e829ec83cdea2a01f8c813c4 \
--hash=sha256:ef00af0439ebfee806b25f24c8f92109157ff3fac5731dc7867957812e87b8d9 \
--hash=sha256:f0e8817c7d1a0c2eedebf57ef9a9896f3ea23324769a9a2061a80fe8852705ed \
--hash=sha256:f3d5be054c461d6a2268831f04091dc82753176f6ea06dc6047a5e168265a987 \
--hash=sha256:f4b5c37a5f40e4d733d3bbaaef082149bee5a5ea3156a785ff64d949bd1353fa
fonttools==4.59.0 \
--hash=sha256:052444a5d0151878e87e3e512a1aa1a0ab35ee4c28afde0a778e23b0ace4a7de \
--hash=sha256:169b99a2553a227f7b5fea8d9ecd673aa258617f466b2abc6091fe4512a0dcd0 \
--hash=sha256:209b75943d158f610b78320eacb5539aa9e920bee2c775445b2846c65d20e19d \
--hash=sha256:21e606b2d38fed938dde871c5736822dd6bda7a4631b92e509a1f5cd1b90c5df \
--hash=sha256:241313683afd3baacb32a6bd124d0bce7404bc5280e12e291bae1b9bba28711d \
--hash=sha256:26731739daa23b872643f0e4072d5939960237d540c35c14e6a06d47d71ca8fe \
--hash=sha256:2e7cf8044ce2598bb87e44ba1d2c6e45d7a8decf56055b92906dc53f67c76d64 \
--hash=sha256:31003b6a10f70742a63126b80863ab48175fb8272a18ca0846c0482968f0588e \
--hash=sha256:332bfe685d1ac58ca8d62b8d6c71c2e52a6c64bc218dc8f7825c9ea51385aa01 \
--hash=sha256:37c377f7cb2ab2eca8a0b319c68146d34a339792f9420fca6cd49cf28d370705 \
--hash=sha256:37e01c6ec0c98599778c2e688350d624fa4770fbd6144551bd5e032f1199171c \
--hash=sha256:401b1941ce37e78b8fd119b419b617277c65ae9417742a63282257434fd68ea2 \
--hash=sha256:4536f2695fe5c1ffb528d84a35a7d3967e5558d2af58b4775e7ab1449d65767b \
--hash=sha256:4c908a7036f0f3677f8afa577bcd973e3e20ddd2f7c42a33208d18bee95cdb6f \
--hash=sha256:51ab1ff33c19e336c02dee1e9fd1abd974a4ca3d8f7eef2a104d0816a241ce97 \
--hash=sha256:524133c1be38445c5c0575eacea42dbd44374b310b1ffc4b60ff01d881fabb96 \
--hash=sha256:57bb7e26928573ee7c6504f54c05860d867fd35e675769f3ce01b52af38d48e2 \
--hash=sha256:60f6665579e909b618282f3c14fa0b80570fbf1ee0e67678b9a9d43aa5d67a37 \
--hash=sha256:62224a9bb85b4b66d1b46d45cbe43d71cbf8f527d332b177e3b96191ffbc1e64 \
--hash=sha256:6770d7da00f358183d8fd5c4615436189e4f683bdb6affb02cad3d221d7bb757 \
--hash=sha256:6801aeddb6acb2c42eafa45bc1cb98ba236871ae6f33f31e984670b749a8e58e \
--hash=sha256:70d6b3ceaa9cc5a6ac52884f3b3d9544e8e231e95b23f138bdb78e6d4dc0eae3 \
--hash=sha256:78813b49d749e1bb4db1c57f2d4d7e6db22c253cb0a86ad819f5dc197710d4b2 \
--hash=sha256:841b2186adce48903c0fef235421ae21549020eca942c1da773ac380b056ab3c \
--hash=sha256:84fc186980231a287b28560d3123bd255d3c6b6659828c642b4cf961e2b923d0 \
--hash=sha256:885bde7d26e5b40e15c47bd5def48b38cbd50830a65f98122a8fb90962af7cd1 \
--hash=sha256:8b4309a2775e4feee7356e63b163969a215d663399cce1b3d3b65e7ec2d9680e \
--hash=sha256:8d77f92438daeaddc05682f0f3dac90c5b9829bcac75b57e8ce09cb67786073c \
--hash=sha256:902425f5afe28572d65d2bf9c33edd5265c612ff82c69e6f83ea13eafc0dcbea \
--hash=sha256:9bcc1e77fbd1609198966ded6b2a9897bd6c6bcbd2287a2fc7d75f1a254179c5 \
--hash=sha256:a408c3c51358c89b29cfa5317cf11518b7ce5de1717abb55c5ae2d2921027de6 \
--hash=sha256:a9bf8adc9e1f3012edc8f09b08336272aec0c55bc677422273e21280db748f7c \
--hash=sha256:b818db35879d2edf7f46c7e729c700a0bce03b61b9412f5a7118406687cb151d \
--hash=sha256:b8974b2a266b54c96709bd5e239979cddfd2dbceed331aa567ea1d7c4a2202db \
--hash=sha256:be392ec3529e2f57faa28709d60723a763904f71a2b63aabe14fee6648fe3b14 \
--hash=sha256:d3972b13148c1d1fbc092b27678a33b3080d1ac0ca305742b0119b75f9e87e38 \
--hash=sha256:d40dcf533ca481355aa7b682e9e079f766f35715defa4929aeb5597f9604272e \
--hash=sha256:e93df708c69a193fc7987192f94df250f83f3851fda49413f02ba5dded639482 \
--hash=sha256:efd7e6660674e234e29937bc1481dceb7e0336bfae75b856b4fb272b5093c5d4 \
--hash=sha256:f9b3a78f69dcbd803cf2fb3f972779875b244c1115481dfbdd567b2c22b31f6b \
--hash=sha256:fa39475eaccb98f9199eccfda4298abaf35ae0caec676ffc25b3a5e224044464 \
--hash=sha256:fbce6dae41b692a5973d0f2158f782b9ad05babc2c2019a970a1094a23909b1b
# via weasyprint
identify==2.6.15 \
--hash=sha256:1181ef7608e00704db228516541eb83a88a9f94433a8c80bb9b5bd54b1d81757 \
--hash=sha256:e4f4864b96c6557ef2a1e1c951771838f4edc9df3a72ec7118b338801b11c7bf
identify==2.6.13 \
--hash=sha256:60381139b3ae39447482ecc406944190f690d4a2997f2584062089848361b33b \
--hash=sha256:da8d6c828e773620e13bfa86ea601c5a5310ba4bcd65edf378198b56a1f9fb32
# via pre-commit
nodeenv==1.9.1 \
--hash=sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f \
@@ -251,113 +338,128 @@ pdf2image==1.17.0 \
--hash=sha256:eaa959bc116b420dd7ec415fcae49b98100dda3dd18cd2fdfa86d09f112f6d57 \
--hash=sha256:ecdd58d7afb810dffe21ef2b1bbc057ef434dabbac6c33778a38a3f7744a27e2
# via -r .github/scripts/requirements_dev.in
pillow==12.0.0 \
--hash=sha256:0869154a2d0546545cde61d1789a6524319fc1897d9ee31218eae7a60ccc5643 \
--hash=sha256:09f2d0abef9e4e2f349305a4f8cc784a8a6c2f58a8c4892eea13b10a943bd26e \
--hash=sha256:0b817e7035ea7f6b942c13aa03bb554fc44fea70838ea21f8eb31c638326584e \
--hash=sha256:0fd00cac9c03256c8b2ff58f162ebcd2587ad3e1f2e397eab718c47e24d231cc \
--hash=sha256:110486b79f2d112cf6add83b28b627e369219388f64ef2f960fef9ebaf54c642 \
--hash=sha256:1979f4566bb96c1e50a62d9831e2ea2d1211761e5662afc545fa766f996632f6 \
--hash=sha256:1ac11e8ea4f611c3c0147424eae514028b5e9077dd99ab91e1bd7bc33ff145e1 \
--hash=sha256:1b1b133e6e16105f524a8dec491e0586d072948ce15c9b914e41cdadd209052b \
--hash=sha256:1ee80a59f6ce048ae13cda1abf7fbd2a34ab9ee7d401c46be3ca685d1999a399 \
--hash=sha256:21f241bdd5080a15bc86d3466a9f6074a9c2c2b314100dd896ac81ee6db2f1ba \
--hash=sha256:266cd5f2b63ff316d5a1bba46268e603c9caf5606d44f38c2873c380950576ad \
--hash=sha256:26d9f7d2b604cd23aba3e9faf795787456ac25634d82cd060556998e39c6fa47 \
--hash=sha256:27f95b12453d165099c84f8a8bfdfd46b9e4bda9e0e4b65f0635430027f55739 \
--hash=sha256:2c54c1a783d6d60595d3514f0efe9b37c8808746a66920315bfd34a938d7994b \
--hash=sha256:2fa5f0b6716fc88f11380b88b31fe591a06c6315e955c096c35715788b339e3f \
--hash=sha256:32ed80ea8a90ee3e6fa08c21e2e091bba6eda8eccc83dbc34c95169507a91f10 \
--hash=sha256:3830c769decf88f1289680a59d4f4c46c72573446352e2befec9a8512104fa52 \
--hash=sha256:38df9b4bfd3db902c9c2bd369bcacaf9d935b2fff73709429d95cc41554f7b3d \
--hash=sha256:3adfb466bbc544b926d50fe8f4a4e6abd8c6bffd28a26177594e6e9b2b76572b \
--hash=sha256:3e42edad50b6909089750e65c91aa09aaf1e0a71310d383f11321b27c224ed8a \
--hash=sha256:4078242472387600b2ce8d93ade8899c12bf33fa89e55ec89fe126e9d6d5d9e9 \
--hash=sha256:455247ac8a4cfb7b9bc45b7e432d10421aea9fc2e74d285ba4072688a74c2e9d \
--hash=sha256:4cc6b3b2efff105c6a1656cfe59da4fdde2cda9af1c5e0b58529b24525d0a098 \
--hash=sha256:4cf7fed4b4580601c4345ceb5d4cbf5a980d030fd5ad07c4d2ec589f95f09905 \
--hash=sha256:5193fde9a5f23c331ea26d0cf171fbf67e3f247585f50c08b3e205c7aeb4589b \
--hash=sha256:5269cc1caeedb67e6f7269a42014f381f45e2e7cd42d834ede3c703a1d915fe3 \
--hash=sha256:53561a4ddc36facb432fae7a9d8afbfaf94795414f5cdc5fc52f28c1dca90371 \
--hash=sha256:55f818bd74fe2f11d4d7cbc65880a843c4075e0ac7226bc1a23261dbea531953 \
--hash=sha256:58eea5ebe51504057dd95c5b77d21700b77615ab0243d8152793dc00eb4faf01 \
--hash=sha256:5d5c411a8eaa2299322b647cd932586b1427367fd3184ffbb8f7a219ea2041ca \
--hash=sha256:6846bd2d116ff42cba6b646edf5bf61d37e5cbd256425fa089fee4ff5c07a99e \
--hash=sha256:6ace95230bfb7cd79ef66caa064bbe2f2a1e63d93471c3a2e1f1348d9f22d6b7 \
--hash=sha256:6e51b71417049ad6ab14c49608b4a24d8fb3fe605e5dfabfe523b58064dc3d27 \
--hash=sha256:71db6b4c1653045dacc1585c1b0d184004f0d7e694c7b34ac165ca70c0838082 \
--hash=sha256:7438839e9e053ef79f7112c881cef684013855016f928b168b81ed5835f3e75e \
--hash=sha256:759de84a33be3b178a64c8ba28ad5c135900359e85fb662bc6e403ad4407791d \
--hash=sha256:792a2c0be4dcc18af9d4a2dfd8a11a17d5e25274a1062b0ec1c2d79c76f3e7f8 \
--hash=sha256:7d87ef5795da03d742bf49439f9ca4d027cde49c82c5371ba52464aee266699a \
--hash=sha256:7dfb439562f234f7d57b1ac6bc8fe7f838a4bd49c79230e0f6a1da93e82f1fad \
--hash=sha256:7fa22993bac7b77b78cae22bad1e2a987ddf0d9015c63358032f84a53f23cdc3 \
--hash=sha256:805ebf596939e48dbb2e4922a1d3852cfc25c38160751ce02da93058b48d252a \
--hash=sha256:82240051c6ca513c616f7f9da06e871f61bfd7805f566275841af15015b8f98d \
--hash=sha256:87d4f8125c9988bfbed67af47dd7a953e2fc7b0cc1e7800ec6d2080d490bb353 \
--hash=sha256:8d8ca2b210ada074d57fcee40c30446c9562e542fc46aedc19baf758a93532ee \
--hash=sha256:8dc232e39d409036af549c86f24aed8273a40ffa459981146829a324e0848b4b \
--hash=sha256:90387104ee8400a7b4598253b4c406f8958f59fcf983a6cea2b50d59f7d63d0b \
--hash=sha256:905b0365b210c73afb0ebe9101a32572152dfd1c144c7e28968a331b9217b94a \
--hash=sha256:99353a06902c2e43b43e8ff74ee65a7d90307d82370604746738a1e0661ccca7 \
--hash=sha256:99a7f72fb6249302aa62245680754862a44179b545ded638cf1fef59befb57ef \
--hash=sha256:9f0b04c6b8584c2c193babcccc908b38ed29524b29dd464bc8801bf10d746a3a \
--hash=sha256:9fe611163f6303d1619bbcb653540a4d60f9e55e622d60a3108be0d5b441017a \
--hash=sha256:a3475b96f5908b3b16c47533daaa87380c491357d197564e0ba34ae75c0f3257 \
--hash=sha256:a6597ff2b61d121172f5844b53f21467f7082f5fb385a9a29c01414463f93b07 \
--hash=sha256:a7921c5a6d31b3d756ec980f2f47c0cfdbce0fc48c22a39347a895f41f4a6ea4 \
--hash=sha256:aa5129de4e174daccbc59d0a3b6d20eaf24417d59851c07ebb37aeb02947987c \
--hash=sha256:aeaefa96c768fc66818730b952a862235d68825c178f1b3ffd4efd7ad2edcb7c \
--hash=sha256:afbefa430092f71a9593a99ab6a4e7538bc9eabbf7bf94f91510d3503943edc4 \
--hash=sha256:aff9e4d82d082ff9513bdd6acd4f5bd359f5b2c870907d2b0a9c5e10d40c88fe \
--hash=sha256:b22bd8c974942477156be55a768f7aa37c46904c175be4e158b6a86e3a6b7ca8 \
--hash=sha256:b290fd8aa38422444d4b50d579de197557f182ef1068b75f5aa8558638b8d0a5 \
--hash=sha256:b2e4b27a6e15b04832fe9bf292b94b5ca156016bbc1ea9c2c20098a0320d6cf6 \
--hash=sha256:b583dc9070312190192631373c6c8ed277254aa6e6084b74bdd0a6d3b221608e \
--hash=sha256:b87843e225e74576437fd5b6a4c2205d422754f84a06942cfaf1dc32243e45a8 \
--hash=sha256:bc91a56697869546d1b8f0a3ff35224557ae7f881050e99f615e0119bf934b4e \
--hash=sha256:bd87e140e45399c818fac4247880b9ce719e4783d767e030a883a970be632275 \
--hash=sha256:bde737cff1a975b70652b62d626f7785e0480918dece11e8fef3c0cf057351c3 \
--hash=sha256:bdee52571a343d721fb2eb3b090a82d959ff37fc631e3f70422e0c2e029f3e76 \
--hash=sha256:bee2a6db3a7242ea309aa7ee8e2780726fed67ff4e5b40169f2c940e7eb09227 \
--hash=sha256:beeae3f27f62308f1ddbcfb0690bf44b10732f2ef43758f169d5e9303165d3f9 \
--hash=sha256:c50f36a62a22d350c96e49ad02d0da41dbd17ddc2e29750dbdba4323f85eb4a5 \
--hash=sha256:c607c90ba67533e1b2355b821fef6764d1dd2cbe26b8c1005ae84f7aea25ff79 \
--hash=sha256:c7b2a63fd6d5246349f3d3f37b14430d73ee7e8173154461785e43036ffa96ca \
--hash=sha256:c828a1ae702fc712978bda0320ba1b9893d99be0badf2647f693cc01cf0f04fa \
--hash=sha256:c85de1136429c524e55cfa4e033b4a7940ac5c8ee4d9401cc2d1bf48154bbc7b \
--hash=sha256:c98fa880d695de164b4135a52fd2e9cd7b7c90a9d8ac5e9e443a24a95ef9248e \
--hash=sha256:cae81479f77420d217def5f54b5b9d279804d17e982e0f2fa19b1d1e14ab5197 \
--hash=sha256:d034140032870024e6b9892c692fe2968493790dd57208b2c37e3fb35f6df3ab \
--hash=sha256:d120c38a42c234dc9a8c5de7ceaaf899cf33561956acb4941653f8bdc657aa79 \
--hash=sha256:d4827615da15cd59784ce39d3388275ec093ae3ee8d7f0c089b76fa87af756c2 \
--hash=sha256:d49e2314c373f4c2b39446fb1a45ed333c850e09d0c59ac79b72eb3b95397363 \
--hash=sha256:d52610d51e265a51518692045e372a4c363056130d922a7351429ac9f27e70b0 \
--hash=sha256:d64317d2587c70324b79861babb9c09f71fbb780bad212018874b2c013d8600e \
--hash=sha256:d77153e14b709fd8b8af6f66a3afbb9ed6e9fc5ccf0b6b7e1ced7b036a228782 \
--hash=sha256:d7e091d464ac59d2c7ad8e7e08105eaf9dafbc3883fd7265ffccc2baad6ac925 \
--hash=sha256:dd333073e0cacdc3089525c7df7d39b211bcdf31fc2824e49d01c6b6187b07d0 \
--hash=sha256:e5d8efac84c9afcb40914ab49ba063d94f5dbdf5066db4482c66a992f47a3a3b \
--hash=sha256:f135c702ac42262573fe9714dfe99c944b4ba307af5eb507abef1667e2cbbced \
--hash=sha256:f13711b1a5ba512d647a0e4ba79280d3a9a045aaf7e0cc6fbe96b91d4cdf6b0c \
--hash=sha256:f4f1231b7dec408e8670264ce63e9c71409d9583dd21d32c163e25213ee2a344 \
--hash=sha256:fa3ed2a29a9e9d2d488b4da81dcb54720ac3104a20bf0bd273f1e4648aff5af9 \
--hash=sha256:fb3096c30df99fd01c7bf8e544f392103d0795b9f98ba71a8054bcbf56b255f1
pillow==11.3.0 \
--hash=sha256:023f6d2d11784a465f09fd09a34b150ea4672e85fb3d05931d89f373ab14abb2 \
--hash=sha256:02a723e6bf909e7cea0dac1b0e0310be9d7650cd66222a5f1c571455c0a45214 \
--hash=sha256:040a5b691b0713e1f6cbe222e0f4f74cd233421e105850ae3b3c0ceda520f42e \
--hash=sha256:05f6ecbeff5005399bb48d198f098a9b4b6bdf27b8487c7f38ca16eeb070cd59 \
--hash=sha256:068d9c39a2d1b358eb9f245ce7ab1b5c3246c7c8c7d9ba58cfa5b43146c06e50 \
--hash=sha256:0743841cabd3dba6a83f38a92672cccbd69af56e3e91777b0ee7f4dba4385632 \
--hash=sha256:092c80c76635f5ecb10f3f83d76716165c96f5229addbd1ec2bdbbda7d496e06 \
--hash=sha256:0b275ff9b04df7b640c59ec5a3cb113eefd3795a8df80bac69646ef699c6981a \
--hash=sha256:0bce5c4fd0921f99d2e858dc4d4d64193407e1b99478bc5cacecba2311abde51 \
--hash=sha256:1019b04af07fc0163e2810167918cb5add8d74674b6267616021ab558dc98ced \
--hash=sha256:106064daa23a745510dabce1d84f29137a37224831d88eb4ce94bb187b1d7e5f \
--hash=sha256:118ca10c0d60b06d006be10a501fd6bbdfef559251ed31b794668ed569c87e12 \
--hash=sha256:13f87d581e71d9189ab21fe0efb5a23e9f28552d5be6979e84001d3b8505abe8 \
--hash=sha256:155658efb5e044669c08896c0c44231c5e9abcaadbc5cd3648df2f7c0b96b9a6 \
--hash=sha256:1904e1264881f682f02b7f8167935cce37bc97db457f8e7849dc3a6a52b99580 \
--hash=sha256:19d2ff547c75b8e3ff46f4d9ef969a06c30ab2d4263a9e287733aa8b2429ce8f \
--hash=sha256:1a992e86b0dd7aeb1f053cd506508c0999d710a8f07b4c791c63843fc6a807ac \
--hash=sha256:1b9c17fd4ace828b3003dfd1e30bff24863e0eb59b535e8f80194d9cc7ecf860 \
--hash=sha256:1c627742b539bba4309df89171356fcb3cc5a9178355b2727d1b74a6cf155fbd \
--hash=sha256:1cd110edf822773368b396281a2293aeb91c90a2db00d78ea43e7e861631b722 \
--hash=sha256:1f85acb69adf2aaee8b7da124efebbdb959a104db34d3a2cb0f3793dbae422a8 \
--hash=sha256:23cff760a9049c502721bdb743a7cb3e03365fafcdfc2ef9784610714166e5a4 \
--hash=sha256:2465a69cf967b8b49ee1b96d76718cd98c4e925414ead59fdf75cf0fd07df673 \
--hash=sha256:2a3117c06b8fb646639dce83694f2f9eac405472713fcb1ae887469c0d4f6788 \
--hash=sha256:2aceea54f957dd4448264f9bf40875da0415c83eb85f55069d89c0ed436e3542 \
--hash=sha256:2d6fcc902a24ac74495df63faad1884282239265c6839a0a6416d33faedfae7e \
--hash=sha256:30807c931ff7c095620fe04448e2c2fc673fcbb1ffe2a7da3fb39613489b1ddd \
--hash=sha256:30b7c02f3899d10f13d7a48163c8969e4e653f8b43416d23d13d1bbfdc93b9f8 \
--hash=sha256:3828ee7586cd0b2091b6209e5ad53e20d0649bbe87164a459d0676e035e8f523 \
--hash=sha256:3cee80663f29e3843b68199b9d6f4f54bd1d4a6b59bdd91bceefc51238bcb967 \
--hash=sha256:3e184b2f26ff146363dd07bde8b711833d7b0202e27d13540bfe2e35a323a809 \
--hash=sha256:41342b64afeba938edb034d122b2dda5db2139b9a4af999729ba8818e0056477 \
--hash=sha256:41742638139424703b4d01665b807c6468e23e699e8e90cffefe291c5832b027 \
--hash=sha256:4445fa62e15936a028672fd48c4c11a66d641d2c05726c7ec1f8ba6a572036ae \
--hash=sha256:45dfc51ac5975b938e9809451c51734124e73b04d0f0ac621649821a63852e7b \
--hash=sha256:465b9e8844e3c3519a983d58b80be3f668e2a7a5db97f2784e7079fbc9f9822c \
--hash=sha256:48d254f8a4c776de343051023eb61ffe818299eeac478da55227d96e241de53f \
--hash=sha256:4c834a3921375c48ee6b9624061076bc0a32a60b5532b322cc0ea64e639dd50e \
--hash=sha256:4c96f993ab8c98460cd0c001447bff6194403e8b1d7e149ade5f00594918128b \
--hash=sha256:504b6f59505f08ae014f724b6207ff6222662aab5cc9542577fb084ed0676ac7 \
--hash=sha256:527b37216b6ac3a12d7838dc3bd75208ec57c1c6d11ef01902266a5a0c14fc27 \
--hash=sha256:5418b53c0d59b3824d05e029669efa023bbef0f3e92e75ec8428f3799487f361 \
--hash=sha256:59a03cdf019efbfeeed910bf79c7c93255c3d54bc45898ac2a4140071b02b4ae \
--hash=sha256:5e05688ccef30ea69b9317a9ead994b93975104a677a36a8ed8106be9260aa6d \
--hash=sha256:6359a3bc43f57d5b375d1ad54a0074318a0844d11b76abccf478c37c986d3cfc \
--hash=sha256:643f189248837533073c405ec2f0bb250ba54598cf80e8c1e043381a60632f58 \
--hash=sha256:65dc69160114cdd0ca0f35cb434633c75e8e7fad4cf855177a05bf38678f73ad \
--hash=sha256:67172f2944ebba3d4a7b54f2e95c786a3a50c21b88456329314caaa28cda70f6 \
--hash=sha256:676b2815362456b5b3216b4fd5bd89d362100dc6f4945154ff172e206a22c024 \
--hash=sha256:6a418691000f2a418c9135a7cf0d797c1bb7d9a485e61fe8e7722845b95ef978 \
--hash=sha256:6abdbfd3aea42be05702a8dd98832329c167ee84400a1d1f61ab11437f1717eb \
--hash=sha256:6be31e3fc9a621e071bc17bb7de63b85cbe0bfae91bb0363c893cbe67247780d \
--hash=sha256:7107195ddc914f656c7fc8e4a5e1c25f32e9236ea3ea860f257b0436011fddd0 \
--hash=sha256:71f511f6b3b91dd543282477be45a033e4845a40278fa8dcdbfdb07109bf18f9 \
--hash=sha256:7859a4cc7c9295f5838015d8cc0a9c215b77e43d07a25e460f35cf516df8626f \
--hash=sha256:7966e38dcd0fa11ca390aed7c6f20454443581d758242023cf36fcb319b1a874 \
--hash=sha256:79ea0d14d3ebad43ec77ad5272e6ff9bba5b679ef73375ea760261207fa8e0aa \
--hash=sha256:7aee118e30a4cf54fdd873bd3a29de51e29105ab11f9aad8c32123f58c8f8081 \
--hash=sha256:7b161756381f0918e05e7cb8a371fff367e807770f8fe92ecb20d905d0e1c149 \
--hash=sha256:7c8ec7a017ad1bd562f93dbd8505763e688d388cde6e4a010ae1486916e713e6 \
--hash=sha256:7d1aa4de119a0ecac0a34a9c8bde33f34022e2e8f99104e47a3ca392fd60e37d \
--hash=sha256:7db51d222548ccfd274e4572fdbf3e810a5e66b00608862f947b163e613b67dd \
--hash=sha256:819931d25e57b513242859ce1876c58c59dc31587847bf74cfe06b2e0cb22d2f \
--hash=sha256:83e1b0161c9d148125083a35c1c5a89db5b7054834fd4387499e06552035236c \
--hash=sha256:857844335c95bea93fb39e0fa2726b4d9d758850b34075a7e3ff4f4fa3aa3b31 \
--hash=sha256:8797edc41f3e8536ae4b10897ee2f637235c94f27404cac7297f7b607dd0716e \
--hash=sha256:8924748b688aa210d79883357d102cd64690e56b923a186f35a82cbc10f997db \
--hash=sha256:89bd777bc6624fe4115e9fac3352c79ed60f3bb18651420635f26e643e3dd1f6 \
--hash=sha256:8dc70ca24c110503e16918a658b869019126ecfe03109b754c402daff12b3d9f \
--hash=sha256:91da1d88226663594e3f6b4b8c3c8d85bd504117d043740a8e0ec449087cc494 \
--hash=sha256:921bd305b10e82b4d1f5e802b6850677f965d8394203d182f078873851dada69 \
--hash=sha256:932c754c2d51ad2b2271fd01c3d121daaa35e27efae2a616f77bf164bc0b3e94 \
--hash=sha256:93efb0b4de7e340d99057415c749175e24c8864302369e05914682ba642e5d77 \
--hash=sha256:97afb3a00b65cc0804d1c7abddbf090a81eaac02768af58cbdcaaa0a931e0b6d \
--hash=sha256:97f07ed9f56a3b9b5f49d3661dc9607484e85c67e27f3e8be2c7d28ca032fec7 \
--hash=sha256:98a9afa7b9007c67ed84c57c9e0ad86a6000da96eaa638e4f8abe5b65ff83f0a \
--hash=sha256:9ab6ae226de48019caa8074894544af5b53a117ccb9d3b3dcb2871464c829438 \
--hash=sha256:9c412fddd1b77a75aa904615ebaa6001f169b26fd467b4be93aded278266b288 \
--hash=sha256:a1bc6ba083b145187f648b667e05a2534ecc4b9f2784c2cbe3089e44868f2b9b \
--hash=sha256:a418486160228f64dd9e9efcd132679b7a02a5f22c982c78b6fc7dab3fefb635 \
--hash=sha256:a4d336baed65d50d37b88ca5b60c0fa9d81e3a87d4a7930d3880d1624d5b31f3 \
--hash=sha256:a6444696fce635783440b7f7a9fc24b3ad10a9ea3f0ab66c5905be1c19ccf17d \
--hash=sha256:a7bc6e6fd0395bc052f16b1a8670859964dbd7003bd0af2ff08342eb6e442cfe \
--hash=sha256:b4b8f3efc8d530a1544e5962bd6b403d5f7fe8b9e08227c6b255f98ad82b4ba0 \
--hash=sha256:b5f56c3f344f2ccaf0dd875d3e180f631dc60a51b314295a3e681fe8cf851fbe \
--hash=sha256:be5463ac478b623b9dd3937afd7fb7ab3d79dd290a28e2b6df292dc75063eb8a \
--hash=sha256:c37d8ba9411d6003bba9e518db0db0c58a680ab9fe5179f040b0463644bc9805 \
--hash=sha256:c84d689db21a1c397d001aa08241044aa2069e7587b398c8cc63020390b1c1b8 \
--hash=sha256:c96d333dcf42d01f47b37e0979b6bd73ec91eae18614864622d9b87bbd5bbf36 \
--hash=sha256:cadc9e0ea0a2431124cde7e1697106471fc4c1da01530e679b2391c37d3fbb3a \
--hash=sha256:cc3e831b563b3114baac7ec2ee86819eb03caa1a2cef0b481a5675b59c4fe23b \
--hash=sha256:cd8ff254faf15591e724dc7c4ddb6bf4793efcbe13802a4ae3e863cd300b493e \
--hash=sha256:d000f46e2917c705e9fb93a3606ee4a819d1e3aa7a9b442f6444f07e77cf5e25 \
--hash=sha256:d9da3df5f9ea2a89b81bb6087177fb1f4d1c7146d583a3fe5c672c0d94e55e12 \
--hash=sha256:e5c5858ad8ec655450a7c7df532e9842cf8df7cc349df7225c60d5d348c8aada \
--hash=sha256:e67d793d180c9df62f1f40aee3accca4829d3794c95098887edc18af4b8b780c \
--hash=sha256:ea944117a7974ae78059fcc1800e5d3295172bb97035c0c1d9345fca1419da71 \
--hash=sha256:eb76541cba2f958032d79d143b98a3a6b3ea87f0959bbe256c0b5e416599fd5d \
--hash=sha256:ec1ee50470b0d050984394423d96325b744d55c701a439d2bd66089bff963d3c \
--hash=sha256:ee92f2fd10f4adc4b43d07ec5e779932b4eb3dbfbc34790ada5a6669bc095aa6 \
--hash=sha256:f0f5d8f4a08090c6d6d578351a2b91acf519a54986c055af27e7a93feae6d3f1 \
--hash=sha256:f1f182ebd2303acf8c380a54f615ec883322593320a9b00438eb842c1f37ae50 \
--hash=sha256:f8a5827f84d973d8636e9dc5764af4f0cf2318d26744b3d902931701b0d46653 \
--hash=sha256:f944255db153ebb2b19c51fe85dd99ef0ce494123f21b9db4877ffdfc5590c7c \
--hash=sha256:fdae223722da47b024b867c1ea0be64e0df702c5e0a60e27daad39bf960dd1e4 \
--hash=sha256:fe27fb049cdcca11f11a7bfda64043c37b30e6b91f10cb5bab275806c32f6ab3
# via
# -r .github/scripts/requirements_dev.in
# pdf2image
# weasyprint
platformdirs==4.5.0 \
--hash=sha256:70ddccdd7c99fc5942e9fc25636a8b34d04c24b335100223152c2803e4063312 \
--hash=sha256:e578a81bb873cbb89a41fcc904c7ef523cc18284b7e3b3ccf06aca1403b7ebd3
platformdirs==4.3.8 \
--hash=sha256:3d512d96e16bcb959a814c9f348431070822a6496326a4be0911c40b5a74c2bc \
--hash=sha256:ff7059bb7eb1179e2685604f4aaf157cfd9535242bd23742eadc3c13542139b4
# via virtualenv
pre-commit==4.3.0 \
--hash=sha256:2b0747ad7e6e967169136edffee14c16e148a778a54e4f967921aa1ebf2308d8 \
--hash=sha256:499fe450cc9d42e9d58e606262795ecb64dd05438943c62b66f6a8673da30b16
# via -r .github/scripts/requirements_dev.in
pycparser==2.23 \
--hash=sha256:78816d4f24add8f10a06d6f05b4d424ad9e96cfebf68a4ddc99c65c0720d00c2 \
--hash=sha256:e5c6e8d3fbad53479cab09ac03729e0a9faf2bee3db8208a550daf5af81a5934
pycparser==2.22 \
--hash=sha256:491c8be9c040f5390f5bf44a5b07752bd07f56edf992381b05c701439eec10f6 \
--hash=sha256:c3702b6d3dd8c7abc1afa565d7e63d53a1d0bd86cdc24edd75470f4de499cfcc
# via cffi
pydyf==0.11.0 \
--hash=sha256:0aaf9e2ebbe786ec7a78ec3fbffa4cdcecde53fd6f563221d53c6bc1328848a3 \
@@ -367,80 +469,60 @@ pyphen==0.17.2 \
--hash=sha256:3a07fb017cb2341e1d9ff31b8634efb1ae4dc4b130468c7c39dd3d32e7c3affd \
--hash=sha256:f60647a9c9b30ec6c59910097af82bc5dd2d36576b918e44148d8b07ef3b4aa3
# via weasyprint
pyyaml==6.0.3 \
--hash=sha256:00c4bdeba853cc34e7dd471f16b4114f4162dc03e6b7afcc2128711f0eca823c \
--hash=sha256:0150219816b6a1fa26fb4699fb7daa9caf09eb1999f3b70fb6e786805e80375a \
--hash=sha256:02893d100e99e03eda1c8fd5c441d8c60103fd175728e23e431db1b589cf5ab3 \
--hash=sha256:02ea2dfa234451bbb8772601d7b8e426c2bfa197136796224e50e35a78777956 \
--hash=sha256:0f29edc409a6392443abf94b9cf89ce99889a1dd5376d94316ae5145dfedd5d6 \
--hash=sha256:10892704fc220243f5305762e276552a0395f7beb4dbf9b14ec8fd43b57f126c \
--hash=sha256:16249ee61e95f858e83976573de0f5b2893b3677ba71c9dd36b9cf8be9ac6d65 \
--hash=sha256:1d37d57ad971609cf3c53ba6a7e365e40660e3be0e5175fa9f2365a379d6095a \
--hash=sha256:1ebe39cb5fc479422b83de611d14e2c0d3bb2a18bbcb01f229ab3cfbd8fee7a0 \
--hash=sha256:214ed4befebe12df36bcc8bc2b64b396ca31be9304b8f59e25c11cf94a4c033b \
--hash=sha256:2283a07e2c21a2aa78d9c4442724ec1eb15f5e42a723b99cb3d822d48f5f7ad1 \
--hash=sha256:22ba7cfcad58ef3ecddc7ed1db3409af68d023b7f940da23c6c2a1890976eda6 \
--hash=sha256:27c0abcb4a5dac13684a37f76e701e054692a9b2d3064b70f5e4eb54810553d7 \
--hash=sha256:28c8d926f98f432f88adc23edf2e6d4921ac26fb084b028c733d01868d19007e \
--hash=sha256:2e71d11abed7344e42a8849600193d15b6def118602c4c176f748e4583246007 \
--hash=sha256:34d5fcd24b8445fadc33f9cf348c1047101756fd760b4dacb5c3e99755703310 \
--hash=sha256:37503bfbfc9d2c40b344d06b2199cf0e96e97957ab1c1b546fd4f87e53e5d3e4 \
--hash=sha256:3c5677e12444c15717b902a5798264fa7909e41153cdf9ef7ad571b704a63dd9 \
--hash=sha256:3ff07ec89bae51176c0549bc4c63aa6202991da2d9a6129d7aef7f1407d3f295 \
--hash=sha256:41715c910c881bc081f1e8872880d3c650acf13dfa8214bad49ed4cede7c34ea \
--hash=sha256:418cf3f2111bc80e0933b2cd8cd04f286338bb88bdc7bc8e6dd775ebde60b5e0 \
--hash=sha256:44edc647873928551a01e7a563d7452ccdebee747728c1080d881d68af7b997e \
--hash=sha256:4a2e8cebe2ff6ab7d1050ecd59c25d4c8bd7e6f400f5f82b96557ac0abafd0ac \
--hash=sha256:4ad1906908f2f5ae4e5a8ddfce73c320c2a1429ec52eafd27138b7f1cbe341c9 \
--hash=sha256:501a031947e3a9025ed4405a168e6ef5ae3126c59f90ce0cd6f2bfc477be31b7 \
--hash=sha256:5190d403f121660ce8d1d2c1bb2ef1bd05b5f68533fc5c2ea899bd15f4399b35 \
--hash=sha256:5498cd1645aa724a7c71c8f378eb29ebe23da2fc0d7a08071d89469bf1d2defb \
--hash=sha256:5cf4e27da7e3fbed4d6c3d8e797387aaad68102272f8f9752883bc32d61cb87b \
--hash=sha256:5e0b74767e5f8c593e8c9b5912019159ed0533c70051e9cce3e8b6aa699fcd69 \
--hash=sha256:5ed875a24292240029e4483f9d4a4b8a1ae08843b9c54f43fcc11e404532a8a5 \
--hash=sha256:5fcd34e47f6e0b794d17de1b4ff496c00986e1c83f7ab2fb8fcfe9616ff7477b \
--hash=sha256:5fdec68f91a0c6739b380c83b951e2c72ac0197ace422360e6d5a959d8d97b2c \
--hash=sha256:6344df0d5755a2c9a276d4473ae6b90647e216ab4757f8426893b5dd2ac3f369 \
--hash=sha256:64386e5e707d03a7e172c0701abfb7e10f0fb753ee1d773128192742712a98fd \
--hash=sha256:652cb6edd41e718550aad172851962662ff2681490a8a711af6a4d288dd96824 \
--hash=sha256:66291b10affd76d76f54fad28e22e51719ef9ba22b29e1d7d03d6777a9174198 \
--hash=sha256:66e1674c3ef6f541c35191caae2d429b967b99e02040f5ba928632d9a7f0f065 \
--hash=sha256:6adc77889b628398debc7b65c073bcb99c4a0237b248cacaf3fe8a557563ef6c \
--hash=sha256:79005a0d97d5ddabfeeea4cf676af11e647e41d81c9a7722a193022accdb6b7c \
--hash=sha256:7c6610def4f163542a622a73fb39f534f8c101d690126992300bf3207eab9764 \
--hash=sha256:7f047e29dcae44602496db43be01ad42fc6f1cc0d8cd6c83d342306c32270196 \
--hash=sha256:8098f252adfa6c80ab48096053f512f2321f0b998f98150cea9bd23d83e1467b \
--hash=sha256:850774a7879607d3a6f50d36d04f00ee69e7fc816450e5f7e58d7f17f1ae5c00 \
--hash=sha256:8d1fab6bb153a416f9aeb4b8763bc0f22a5586065f86f7664fc23339fc1c1fac \
--hash=sha256:8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8 \
--hash=sha256:8dc52c23056b9ddd46818a57b78404882310fb473d63f17b07d5c40421e47f8e \
--hash=sha256:9149cad251584d5fb4981be1ecde53a1ca46c891a79788c0df828d2f166bda28 \
--hash=sha256:93dda82c9c22deb0a405ea4dc5f2d0cda384168e466364dec6255b293923b2f3 \
--hash=sha256:96b533f0e99f6579b3d4d4995707cf36df9100d67e0c8303a0c55b27b5f99bc5 \
--hash=sha256:9c57bb8c96f6d1808c030b1687b9b5fb476abaa47f0db9c0101f5e9f394e97f4 \
--hash=sha256:9c7708761fccb9397fe64bbc0395abcae8c4bf7b0eac081e12b809bf47700d0b \
--hash=sha256:9f3bfb4965eb874431221a3ff3fdcddc7e74e3b07799e0e84ca4a0f867d449bf \
--hash=sha256:a33284e20b78bd4a18c8c2282d549d10bc8408a2a7ff57653c0cf0b9be0afce5 \
--hash=sha256:a80cb027f6b349846a3bf6d73b5e95e782175e52f22108cfa17876aaeff93702 \
--hash=sha256:b30236e45cf30d2b8e7b3e85881719e98507abed1011bf463a8fa23e9c3e98a8 \
--hash=sha256:b3bc83488de33889877a0f2543ade9f70c67d66d9ebb4ac959502e12de895788 \
--hash=sha256:b865addae83924361678b652338317d1bd7e79b1f4596f96b96c77a5a34b34da \
--hash=sha256:b8bb0864c5a28024fac8a632c443c87c5aa6f215c0b126c449ae1a150412f31d \
--hash=sha256:ba1cc08a7ccde2d2ec775841541641e4548226580ab850948cbfda66a1befcdc \
--hash=sha256:bdb2c67c6c1390b63c6ff89f210c8fd09d9a1217a465701eac7316313c915e4c \
--hash=sha256:c1ff362665ae507275af2853520967820d9124984e0f7466736aea23d8611fba \
--hash=sha256:c2514fceb77bc5e7a2f7adfaa1feb2fb311607c9cb518dbc378688ec73d8292f \
--hash=sha256:c3355370a2c156cffb25e876646f149d5d68f5e0a3ce86a5084dd0b64a994917 \
--hash=sha256:c458b6d084f9b935061bc36216e8a69a7e293a2f1e68bf956dcd9e6cbcd143f5 \
--hash=sha256:d0eae10f8159e8fdad514efdc92d74fd8d682c933a6dd088030f3834bc8e6b26 \
--hash=sha256:d76623373421df22fb4cf8817020cbb7ef15c725b9d5e45f17e189bfc384190f \
--hash=sha256:ebc55a14a21cb14062aa4162f906cd962b28e2e9ea38f9b4391244cd8de4ae0b \
--hash=sha256:eda16858a3cab07b80edaf74336ece1f986ba330fdb8ee0d6c0d68fe82bc96be \
--hash=sha256:ee2922902c45ae8ccada2c5b501ab86c36525b883eff4255313a253a3160861c \
--hash=sha256:efd7b85f94a6f21e4932043973a7ba2613b059c4a000551892ac9f1d11f5baf3 \
--hash=sha256:f7057c9a337546edc7973c0d3ba84ddcdf0daa14533c2065749c9075001090e6 \
--hash=sha256:fa160448684b4e94d80416c0fa4aac48967a969efe22931448d853ada8baf926 \
--hash=sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0
pyyaml==6.0.2 \
--hash=sha256:01179a4a8559ab5de078078f37e5c1a30d76bb88519906844fd7bdea1b7729ff \
--hash=sha256:0833f8694549e586547b576dcfaba4a6b55b9e96098b36cdc7ebefe667dfed48 \
--hash=sha256:0a9a2848a5b7feac301353437eb7d5957887edbf81d56e903999a75a3d743086 \
--hash=sha256:0b69e4ce7a131fe56b7e4d770c67429700908fc0752af059838b1cfb41960e4e \
--hash=sha256:0ffe8360bab4910ef1b9e87fb812d8bc0a308b0d0eef8c8f44e0254ab3b07133 \
--hash=sha256:11d8f3dd2b9c1207dcaf2ee0bbbfd5991f571186ec9cc78427ba5bd32afae4b5 \
--hash=sha256:17e311b6c678207928d649faa7cb0d7b4c26a0ba73d41e99c4fff6b6c3276484 \
--hash=sha256:1e2120ef853f59c7419231f3bf4e7021f1b936f6ebd222406c3b60212205d2ee \
--hash=sha256:1f71ea527786de97d1a0cc0eacd1defc0985dcf6b3f17bb77dcfc8c34bec4dc5 \
--hash=sha256:23502f431948090f597378482b4812b0caae32c22213aecf3b55325e049a6c68 \
--hash=sha256:24471b829b3bf607e04e88d79542a9d48bb037c2267d7927a874e6c205ca7e9a \
--hash=sha256:29717114e51c84ddfba879543fb232a6ed60086602313ca38cce623c1d62cfbf \
--hash=sha256:2e99c6826ffa974fe6e27cdb5ed0021786b03fc98e5ee3c5bfe1fd5015f42b99 \
--hash=sha256:39693e1f8320ae4f43943590b49779ffb98acb81f788220ea932a6b6c51004d8 \
--hash=sha256:3ad2a3decf9aaba3d29c8f537ac4b243e36bef957511b4766cb0057d32b0be85 \
--hash=sha256:3b1fdb9dc17f5a7677423d508ab4f243a726dea51fa5e70992e59a7411c89d19 \
--hash=sha256:41e4e3953a79407c794916fa277a82531dd93aad34e29c2a514c2c0c5fe971cc \
--hash=sha256:43fa96a3ca0d6b1812e01ced1044a003533c47f6ee8aca31724f78e93ccc089a \
--hash=sha256:50187695423ffe49e2deacb8cd10510bc361faac997de9efef88badc3bb9e2d1 \
--hash=sha256:5ac9328ec4831237bec75defaf839f7d4564be1e6b25ac710bd1a96321cc8317 \
--hash=sha256:5d225db5a45f21e78dd9358e58a98702a0302f2659a3c6cd320564b75b86f47c \
--hash=sha256:6395c297d42274772abc367baaa79683958044e5d3835486c16da75d2a694631 \
--hash=sha256:688ba32a1cffef67fd2e9398a2efebaea461578b0923624778664cc1c914db5d \
--hash=sha256:68ccc6023a3400877818152ad9a1033e3db8625d899c72eacb5a668902e4d652 \
--hash=sha256:70b189594dbe54f75ab3a1acec5f1e3faa7e8cf2f1e08d9b561cb41b845f69d5 \
--hash=sha256:797b4f722ffa07cc8d62053e4cff1486fa6dc094105d13fea7b1de7d8bf71c9e \
--hash=sha256:7c36280e6fb8385e520936c3cb3b8042851904eba0e58d277dca80a5cfed590b \
--hash=sha256:7e7401d0de89a9a855c839bc697c079a4af81cf878373abd7dc625847d25cbd8 \
--hash=sha256:80bab7bfc629882493af4aa31a4cfa43a4c57c83813253626916b8c7ada83476 \
--hash=sha256:82d09873e40955485746739bcb8b4586983670466c23382c19cffecbf1fd8706 \
--hash=sha256:8388ee1976c416731879ac16da0aff3f63b286ffdd57cdeb95f3f2e085687563 \
--hash=sha256:8824b5a04a04a047e72eea5cec3bc266db09e35de6bdfe34c9436ac5ee27d237 \
--hash=sha256:8b9c7197f7cb2738065c481a0461e50ad02f18c78cd75775628afb4d7137fb3b \
--hash=sha256:9056c1ecd25795207ad294bcf39f2db3d845767be0ea6e6a34d856f006006083 \
--hash=sha256:936d68689298c36b53b29f23c6dbb74de12b4ac12ca6cfe0e047bedceea56180 \
--hash=sha256:9b22676e8097e9e22e36d6b7bda33190d0d400f345f23d4065d48f4ca7ae0425 \
--hash=sha256:a4d3091415f010369ae4ed1fc6b79def9416358877534caf6a0fdd2146c87a3e \
--hash=sha256:a8786accb172bd8afb8be14490a16625cbc387036876ab6ba70912730faf8e1f \
--hash=sha256:a9f8c2e67970f13b16084e04f134610fd1d374bf477b17ec1599185cf611d725 \
--hash=sha256:bc2fa7c6b47d6bc618dd7fb02ef6fdedb1090ec036abab80d4681424b84c1183 \
--hash=sha256:c70c95198c015b85feafc136515252a261a84561b7b1d51e3384e0655ddf25ab \
--hash=sha256:cc1c1159b3d456576af7a3e4d1ba7e6924cb39de8f67111c735f6fc832082774 \
--hash=sha256:ce826d6ef20b1bc864f0a68340c8b3287705cae2f8b4b1d932177dcc76721725 \
--hash=sha256:d584d9ec91ad65861cc08d42e834324ef890a082e591037abe114850ff7bbc3e \
--hash=sha256:d7fded462629cfa4b685c5416b949ebad6cec74af5e2d42905d41e257e0869f5 \
--hash=sha256:d84a1718ee396f54f3a086ea0a66d8e552b2ab2017ef8b420e92edbc841c352d \
--hash=sha256:d8e03406cac8513435335dbab54c0d385e4a49e4945d2909a581c83647ca0290 \
--hash=sha256:e10ce637b18caea04431ce14fabcf5c64a1c61ec9c56b071a4b7ca131ca52d44 \
--hash=sha256:ec031d5d2feb36d1d1a24380e4db6d43695f3748343d99434e6f5f9156aaa2ed \
--hash=sha256:ef6107725bd54b262d6dedcc2af448a266975032bc85ef0172c5f059da6325b4 \
--hash=sha256:efdca5630322a10774e8e98e1af481aad470dd62c3170801852d752aa7a783ba \
--hash=sha256:f753120cb8181e736c57ef7636e83f31b9c0d1722c516f7e86cf15b7aa57ff12 \
--hash=sha256:ff3824dc5261f50c9b0dfb3be22b4567a6f938ccce4587b38952d85fd9e9afe4
# via pre-commit
tinycss2==1.4.0 \
--hash=sha256:10c0972f6fc0fbee87c3edb76549357415e94548c1ae10ebccdea16fb404a9b7 \
@@ -452,13 +534,13 @@ tinyhtml5==2.0.0 \
--hash=sha256:086f998833da24c300c414d9fe81d9b368fd04cb9d2596a008421cbc705fcfcc \
--hash=sha256:13683277c5b176d070f82d099d977194b7a1e26815b016114f581a74bbfbf47e
# via weasyprint
unoserver==3.4 \
--hash=sha256:3dcf2204013def1d1ddd3671f38b11346bdf349fef9728277462666a8a634419 \
--hash=sha256:64c24d33d4f65d680a2d9f676518cb28e7fd6c1f9d9a745c33e4a4cb59afdfcd
unoserver==3.3.2 \
--hash=sha256:1eeb7467cf6b56b8eff3b576e2d1b2b2ff4e0eb2052e995ac80a1456de300639 \
--hash=sha256:87e144f903ee21951b2e06a97549450c13ed7eca5bcebad942d3352d4e882616
# via -r .github/scripts/requirements_dev.in
virtualenv==20.35.4 \
--hash=sha256:643d3914d73d3eeb0c552cbb12d7e82adf0e504dbf86a3182f8771a153a1971c \
--hash=sha256:c21c9cede36c9753eeade68ba7d523529f228a403463376cf821eaae2b650f1b
virtualenv==20.33.1 \
--hash=sha256:07c19bc66c11acab6a5958b815cbcee30891cd1c2ccf53785a28651a0d8d8a67 \
--hash=sha256:1b44478d9e261b3fb8baa5e74a0ca3bc0e05f21aa36167bf9cbf850e542765b8
# via pre-commit
weasyprint==66.0 \
--hash=sha256:82b0783b726fcd318e2c977dcdddca76515b30044bc7a830cc4fbe717582a6d0 \
@@ -546,9 +628,9 @@ zopfli==0.2.3.post1 \
# via fonttools
# The following packages are considered to be unsafe in a requirements file:
pip==25.3 \
--hash=sha256:8d0538dbbd7babbd207f261ed969c65de439f6bc9e5dbd3b3b9a77f25d95f343 \
--hash=sha256:9655943313a94722b7774661c21049070f6bbb0a1516bf02f7c8d5d9201514cd
pip==25.2 \
--hash=sha256:578283f006390f85bb6282dffb876454593d637f5d1be494b5202ce4877e71f2 \
--hash=sha256:6d67a2b4e7f14d8b31b8b52648866fa717f45a1eb70e83002f4331d07e953717
# via -r .github/scripts/requirements_dev.in
setuptools==80.9.0 \
--hash=sha256:062d34222ad13e0cc312a4c02d73f059e86a4acbfbdea8f8f76b28c99f306922 \
+9 -9
View File
@@ -12,9 +12,9 @@ distlib==0.4.0 \
--hash=sha256:9659f7d87e46584a30b5780e43ac7a2143098441670ff0a49d5f9034c54a6c16 \
--hash=sha256:feec40075be03a04501a973d81f633735b4b69f98b05450592310c0f401a4e0d
# via virtualenv
filelock==3.20.0 \
--hash=sha256:339b4732ffda5cd79b13f4e2711a31b0365ce445d95d243bb996273d072546a2 \
--hash=sha256:711e943b4ec6be42e1d4e6690b48dc175c822967466bb31c0c293f34334c13f4
filelock==3.19.1 \
--hash=sha256:66eda1888b0171c998b35be2bcc0f6d75c388a7ce20c3f3f37aa8e96c2dddf58 \
--hash=sha256:d38e30481def20772f5baf097c122c3babc4fcdb7e14e57049eb9d88c6dc017d
# via virtualenv
identify==2.6.15 \
--hash=sha256:1181ef7608e00704db228516541eb83a88a9f94433a8c80bb9b5bd54b1d81757 \
@@ -24,9 +24,9 @@ nodeenv==1.9.1 \
--hash=sha256:6ec12890a2dab7946721edbfbcd91f3319c6ccc9aec47be7c7e6b7011ee6645f \
--hash=sha256:ba11c9782d29c27c70ffbdda2d7415098754709be8a7056d79a737cd901155c9
# via pre-commit
platformdirs==4.5.0 \
--hash=sha256:70ddccdd7c99fc5942e9fc25636a8b34d04c24b335100223152c2803e4063312 \
--hash=sha256:e578a81bb873cbb89a41fcc904c7ef523cc18284b7e3b3ccf06aca1403b7ebd3
platformdirs==4.4.0 \
--hash=sha256:abd01743f24e5287cd7a5db3752faf1a2d65353f38ec26d98e25a6db65958c85 \
--hash=sha256:ca753cf4d81dc309bc67b0ea38fd15dc97bc30ce419a7f58d13eb3bf14c4febf
# via virtualenv
pre-commit==4.3.0 \
--hash=sha256:2b0747ad7e6e967169136edffee14c16e148a778a54e4f967921aa1ebf2308d8 \
@@ -107,7 +107,7 @@ pyyaml==6.0.3 \
--hash=sha256:fa160448684b4e94d80416c0fa4aac48967a969efe22931448d853ada8baf926 \
--hash=sha256:fc09d0aa354569bc501d4e787133afc08552722d3ab34836a80547331bb5d4a0
# via pre-commit
virtualenv==20.35.4 \
--hash=sha256:643d3914d73d3eeb0c552cbb12d7e82adf0e504dbf86a3182f8771a153a1971c \
--hash=sha256:c21c9cede36c9753eeade68ba7d523529f228a403463376cf821eaae2b650f1b
virtualenv==20.34.0 \
--hash=sha256:341f5afa7eee943e4984a9207c025feedd768baff6753cd660c857ceb3e36026 \
--hash=sha256:44815b2c9dee7ed86e387b842a84f20b93f7f417f95886ca1996a72a4138eb1a
# via pre-commit
+4 -4
View File
@@ -37,7 +37,7 @@ jobs:
- name: Resolve PR info
id: resolve
uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
with:
script: |
const { owner, repo } = context.repo;
@@ -128,7 +128,7 @@ jobs:
- name: Add deployment started comment
id: deployment-started
uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
with:
github-token: ${{ steps.setup-bot.outputs.token }}
script: |
@@ -352,7 +352,7 @@ jobs:
- name: Post V2 deployment URL to PR
if: success()
uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
with:
github-token: ${{ steps.setup-bot.outputs.token }}
script: |
@@ -419,7 +419,7 @@ jobs:
private-key: ${{ secrets.GH_APP_PRIVATE_KEY }}
- name: Clean up V2 deployment comments
uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
uses: actions/github-script@60a0d83039c74a4aee543508d2ffcb1c3799cdea # v7.0.1
with:
github-token: ${{ steps.setup-bot.outputs.token }}
script: |
@@ -41,12 +41,12 @@ jobs:
enable_enterprise: ${{ steps.check-pro-flag.outputs.enable_enterprise }}
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- name: Checkout PR
uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Setup GitHub App Bot
if: github.actor != 'dependabot[bot]'
@@ -129,12 +129,12 @@ jobs:
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- name: Checkout PR
uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Setup GitHub App Bot
if: github.actor != 'dependabot[bot]'
@@ -146,7 +146,7 @@ jobs:
private-key: ${{ secrets.GH_APP_PRIVATE_KEY }}
- name: Checkout PR
uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
with:
ref: refs/pull/${{ needs.check-comment.outputs.pr_number }}/merge
token: ${{ steps.setup-bot.outputs.token }}
@@ -357,149 +357,3 @@ jobs:
rm -f ../private.key docker-compose.yml
echo "Cleanup complete."
continue-on-error: true
handle-label-commands:
if: ${{ github.event.issue.pull_request != null }}
runs-on: ubuntu-latest
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
with:
egress-policy: audit
- name: Check out the repository
uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
- name: Setup GitHub App Bot
id: setup-bot
uses: ./.github/actions/setup-bot
with:
app-id: ${{ secrets.GH_APP_ID }}
private-key: ${{ secrets.GH_APP_PRIVATE_KEY }}
- name: Apply label commands
uses: actions/github-script@ed597411d8f924073f98dfc5c65a23a2325f34cd # v8.0.0
with:
github-token: ${{ steps.setup-bot.outputs.token }}
script: |
const fs = require('fs');
const path = require('path');
const { comment, issue } = context.payload;
const commentBody = comment?.body ?? '';
if (!commentBody.includes('::label::')) {
core.info('No label commands detected in comment.');
return;
}
const configPath = path.join(process.env.GITHUB_WORKSPACE, '.github', 'config', 'repo_devs.json');
const repoDevsConfig = JSON.parse(fs.readFileSync(configPath, 'utf8'));
const label_changer = (repoDevsConfig.label_changer || []).map((login) => login.toLowerCase());
const commenter = (comment?.user?.login || '').toLowerCase();
if (!label_changer.includes(commenter)) {
core.info(`User ${commenter} is not authorized to manage labels.`);
return;
}
const labelsConfigPath = path.join(process.env.GITHUB_WORKSPACE, '.github', 'labels.yml');
const labelsFile = fs.readFileSync(labelsConfigPath, 'utf8');
const labelNameMap = new Map();
for (const match of labelsFile.matchAll(/-\s+name:\s*(?:"([^"]+)"|'([^']+)'|([^\n]+))/g)) {
const labelName = (match[1] ?? match[2] ?? match[3] ?? '').trim();
if (!labelName) {
continue;
}
const normalized = labelName.toLowerCase();
if (!labelNameMap.has(normalized)) {
labelNameMap.set(normalized, labelName);
}
}
if (!labelNameMap.size) {
core.warning('No labels could be read from .github/labels.yml; aborting label commands.');
return;
}
let allowedLabelNames = new Set(labelNameMap.values());
const labelsToAdd = new Set();
const labelsToRemove = new Set();
const commandRegex = /^(\w+)::(label)::"([^"]+)"/gim;
let match;
while ((match = commandRegex.exec(commentBody)) !== null) {
core.info(`Found label command: ${match[0]} (action: ${match[1]}, label: ${match[2]}, labelName: ${match[3]})`);
const action = match[1].toLowerCase();
const labelName = match[3].trim();
if (!labelName) {
continue;
}
const normalized = labelName.toLowerCase();
const resolvedLabelName = labelNameMap.get(normalized);
if (action === 'add') {
if (!resolvedLabelName) {
core.warning(`Label "${labelName}" is not defined in .github/labels.yml and cannot be added.`);
continue;
}
if (!allowedLabelNames.has(resolvedLabelName)) {
core.warning(`Label "${resolvedLabelName}" is not allowed for add commands and will be skipped.`);
continue;
}
labelsToAdd.add(resolvedLabelName);
} else if (action === 'rm') {
const labelToRemove = resolvedLabelName ?? labelName;
if (!resolvedLabelName) {
core.warning(`Label "${labelName}" is not defined in .github/labels.yml; attempting to remove as provided.`);
}
labelsToRemove.add(labelToRemove);
}
}
const addLabels = Array.from(labelsToAdd);
const removeLabels = Array.from(labelsToRemove);
if (!addLabels.length && !removeLabels.length) {
core.info('No valid label commands found after parsing.');
return;
}
const issueParams = {
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: issue.number,
};
if (addLabels.length) {
core.info(`Adding labels: ${addLabels.join(', ')}`);
await github.rest.issues.addLabels({
...issueParams,
labels: addLabels,
});
}
for (const labelName of removeLabels) {
core.info(`Removing label: ${labelName}`);
try {
await github.rest.issues.removeLabel({
...issueParams,
name: labelName,
});
} catch (error) {
if (error.status === 404) {
core.warning(`Label "${labelName}" was not present on the pull request.`);
} else {
throw error;
}
}
}
await github.rest.issues.deleteComment({
owner: context.repo.owner,
repo: context.repo.repo,
comment_id: comment.id,
});
core.info('Processed label commands and deleted the comment.');
+2 -2
View File
@@ -21,12 +21,12 @@ jobs:
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- name: Checkout PR
uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Setup GitHub App Bot
if: github.actor != 'dependabot[bot]'
+3 -3
View File
@@ -19,11 +19,11 @@ jobs:
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
- uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
with:
fetch-depth: 0
@@ -87,7 +87,7 @@ jobs:
- name: AI PR Title Analysis
if: steps.actor.outputs.is_repo_dev == 'true'
id: ai-title-analysis
uses: actions/ai-inference@334892bb203895caaed82ec52d23c1ed9385151e # v2.0.4
uses: actions/ai-inference@b81b2afb8390ee6839b494a404766bef6493c7d9 # v1.2.8
with:
model: openai/gpt-4o
system-prompt-file: ".github/config/system-prompt.txt"
+2 -5
View File
@@ -2,9 +2,6 @@ name: "Auto Pull Request Labeler V2"
on:
pull_request_target:
types: [opened, synchronize]
branches:
- main
- V2
permissions:
contents: read
@@ -16,11 +13,11 @@ jobs:
pull-requests: write
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
- uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Setup GitHub App Bot
id: setup-bot
+1 -1
View File
@@ -238,7 +238,7 @@ jobs:
sudo chmod +x /usr/local/bin/docker-compose
- name: Set up Python
uses: actions/setup-python@83679a892e2d95755f2dac6acb0bfd1e9ac5d548 # v6.1.0
uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0
with:
python-version: "3.12"
cache: 'pip' # caching pip dependencies
+3 -3
View File
@@ -25,12 +25,12 @@ jobs:
pull-requests: write # Allow writing to pull requests
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- name: Checkout main branch first
uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Setup GitHub App Bot
id: setup-bot
@@ -194,7 +194,7 @@ jobs:
core.exportVariable("REFERENCE_FILE", referenceFilePath);
- name: Set up Python
uses: actions/setup-python@83679a892e2d95755f2dac6acb0bfd1e9ac5d548 # v6.1.0
uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0
with:
python-version: "3.12"
+3 -3
View File
@@ -17,13 +17,13 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- name: "Checkout Repository"
uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: "Dependency Review"
uses: actions/dependency-review-action@3c4e3dcb1aa7874d2c16be7d79418e9b7efd6261 # v4.8.2
uses: actions/dependency-review-action@56339e523c0409420f6c2c9a2f4292bbb3c07dd3 # v4.8.0
with:
config-file: './.github/config/dependency-review-config.yml'
@@ -253,7 +253,7 @@ jobs:
- name: Create Pull Request (Push only)
id: cpr
if: github.event_name == 'push' && env.CHANGES_DETECTED == 'true'
uses: peter-evans/create-pull-request@98357b18bf14b5342f975ff684046ec3b2a07725 # v8.0.0
uses: peter-evans/create-pull-request@271a8d0340265f705b14b6d32b9829c1cb33d45e # v7.0.8
with:
token: ${{ steps.setup-bot.outputs.token }}
commit-message: "Update Frontend 3rd Party Licenses"
+4 -4
View File
@@ -31,12 +31,12 @@ jobs:
repository-projects: write # Required for enabling automerge
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- name: Check out code
uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
with:
fetch-depth: 0
@@ -64,7 +64,7 @@ jobs:
- name: Upload artifact on failure
if: failure()
uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # v5.0.0
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
with:
name: dependencies-without-allowed-license.json
path: build/reports/dependency-license/dependencies-without-allowed-license.json
@@ -82,7 +82,7 @@ jobs:
- name: Create Pull Request
id: cpr
if: env.CHANGES_DETECTED == 'true'
uses: peter-evans/create-pull-request@98357b18bf14b5342f975ff684046ec3b2a07725 # v8.0.0
uses: peter-evans/create-pull-request@271a8d0340265f705b14b6d32b9829c1cb33d45e # v7.0.8
with:
token: ${{ steps.setup-bot.outputs.token }}
commit-message: "Update 3rd Party Licenses"
+2 -2
View File
@@ -15,12 +15,12 @@ jobs:
issues: write
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- name: Check out the repository
uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Run Labeler
uses: crazy-max/ghaction-github-labeler@24d110aa46a59976b8a7f35518cb7f14f434c916 # v5.3.0
+15 -15
View File
@@ -38,14 +38,14 @@ jobs:
version: ${{ steps.versionNumber.outputs.versionNumber }}
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
- name: Set up JDK 21
uses: actions/setup-java@dded0888837ed1f317902acf8a20df0ad188d165 # v5.0.0
uses: actions/setup-java@c5195efecf7bdfc987ee8bae7a71cb8b11521c00 # v4.7.1
with:
java-version: "21"
distribution: "temurin"
@@ -106,14 +106,14 @@ jobs:
file_suffix: "-server"
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
- uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
- name: Set up JDK 21
uses: actions/setup-java@dded0888837ed1f317902acf8a20df0ad188d165 # v5.0.0
uses: actions/setup-java@c5195efecf7bdfc987ee8bae7a71cb8b11521c00 # v4.7.1
with:
java-version: "21"
distribution: "temurin"
@@ -145,7 +145,7 @@ jobs:
cp app/core/build/libs/stirling-pdf-${{ needs.determine-matrix.outputs.version }}.jar ./jar-dist/Stirling-PDF${{ matrix.variant.file_suffix }}.jar
- name: Upload JAR artifacts
uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # v5.0.0
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
with:
name: jar${{ matrix.variant.file_suffix }}
path: ./jar-dist/*.jar
@@ -189,7 +189,7 @@ jobs:
targets: ${{ (matrix.platform == 'macos-15' || matrix.platform == 'macos-15-intel') && 'aarch64-apple-darwin,x86_64-apple-darwin' || '' }}
- name: Set up JDK 21
uses: actions/setup-java@dded0888837ed1f317902acf8a20df0ad188d165 # v5.0.0
uses: actions/setup-java@c5195efecf7bdfc987ee8bae7a71cb8b11521c00 # v4.7.1
with:
java-version: "21"
distribution: "temurin"
@@ -516,7 +516,7 @@ jobs:
fi
- name: Upload build artifacts
uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # v5.0.0
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
with:
name: Stirling-PDF-${{ matrix.name }}
path: ./dist/*
@@ -530,30 +530,30 @@ jobs:
contents: write
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- name: Download all Tauri artifacts
uses: actions/download-artifact@018cc2cf5baa6db3ef3c5f8a56943fffe632ef53 # v6.0.0
uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 # v5.0.0
with:
pattern: Stirling-PDF-*
path: ./artifacts/tauri
- name: Download JAR artifact (default)
uses: actions/download-artifact@018cc2cf5baa6db3ef3c5f8a56943fffe632ef53 # v6.0.0
uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 # v5.0.0
with:
name: jar
path: ./artifacts/jars
- name: Download JAR artifact (with login)
uses: actions/download-artifact@018cc2cf5baa6db3ef3c5f8a56943fffe632ef53 # v6.0.0
uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 # v5.0.0
with:
name: jar-with-login
path: ./artifacts/jars
- name: Download JAR artifact (server only)
uses: actions/download-artifact@018cc2cf5baa6db3ef3c5f8a56943fffe632ef53 # v6.0.0
uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 # v5.0.0
with:
name: jar-server
path: ./artifacts/jars
@@ -562,7 +562,7 @@ jobs:
run: ls -R ./artifacts
- name: Upload binaries to Release
uses: softprops/action-gh-release@a06a81a03ee405af7f2048a818ed3f03bbf83c7b # v2.5.0
uses: softprops/action-gh-release@62c96d0c4e8a889135c1f3a25910db8dbe0e85f7 # v2.3.4
with:
tag_name: v${{ needs.determine-matrix.outputs.version }}
generate_release_notes: true
+4 -4
View File
@@ -21,12 +21,12 @@ jobs:
pull-requests: write
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- name: Checkout repository
uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
with:
fetch-depth: 0
@@ -38,7 +38,7 @@ jobs:
private-key: ${{ secrets.GH_APP_PRIVATE_KEY }}
- name: Set up Python
uses: actions/setup-python@83679a892e2d95755f2dac6acb0bfd1e9ac5d548 # v6.1.0
uses: actions/setup-python@e797f83bcb11b83ae66e0230d6156d7c80228e7c # v6.0.0
with:
python-version: 3.12
cache: 'pip' # caching pip dependencies
@@ -67,7 +67,7 @@ jobs:
- name: Create Pull Request
if: env.CHANGES_DETECTED == 'true'
uses: peter-evans/create-pull-request@98357b18bf14b5342f975ff684046ec3b2a07725 # v8.0.0
uses: peter-evans/create-pull-request@271a8d0340265f705b14b6d32b9829c1cb33d45e # v7.0.8
with:
token: ${{ steps.setup-bot.outputs.token }}
commit-message: ":file_folder: pre-commit"
+13 -13
View File
@@ -5,7 +5,7 @@ on:
push:
branches:
- V2-master
- V1_V2_merge
- alljavadocker
# cancel in-progress jobs if a new job is triggered
# This is useful to avoid running multiple builds for the same branch if a new commit is pushed
@@ -83,7 +83,7 @@ jobs:
- name: Generate tags for latest (V2-master branch - production)
id: meta
uses: docker/metadata-action@c299e40c65443455700f0fdfc63efafe5b349051 # v5.10.0
uses: docker/metadata-action@c1e51972afc2121e065aed6d45c65596fe445f3f # v5.8.0
if: github.ref == 'refs/heads/V2-master'
with:
images: |
@@ -95,10 +95,10 @@ jobs:
type=raw,value=${{ steps.versionNumber.outputs.versionNumber }}
type=raw,value=latest
- name: Generate tags for latest (V1_V2_merge branch - test)
- name: Generate tags for latest (alljavadocker branch - test)
id: meta-test
uses: docker/metadata-action@c299e40c65443455700f0fdfc63efafe5b349051 # v5.10.0
if: github.ref == 'refs/heads/V1_V2_merge'
uses: docker/metadata-action@c1e51972afc2121e065aed6d45c65596fe445f3f # v5.8.0
if: github.ref == 'refs/heads/alljavadocker'
with:
images: |
ghcr.io/stirling-tools/stirling-pdf-test
@@ -139,7 +139,7 @@ jobs:
- name: Generate tags for latest-fat (V2-master branch - production)
id: meta-fat
uses: docker/metadata-action@c299e40c65443455700f0fdfc63efafe5b349051 # v5.10.0
uses: docker/metadata-action@c1e51972afc2121e065aed6d45c65596fe445f3f # v5.8.0
if: github.ref == 'refs/heads/V2-master'
with:
images: |
@@ -151,10 +151,10 @@ jobs:
type=raw,value=${{ steps.versionNumber.outputs.versionNumber }}-fat
type=raw,value=latest-fat
- name: Generate tags for latest-fat (V1_V2_merge branch - test)
- name: Generate tags for latest-fat (alljavadocker branch - test)
id: meta-fat-test
uses: docker/metadata-action@c299e40c65443455700f0fdfc63efafe5b349051 # v5.10.0
if: github.ref == 'refs/heads/V1_V2_merge'
uses: docker/metadata-action@c1e51972afc2121e065aed6d45c65596fe445f3f # v5.8.0
if: github.ref == 'refs/heads/alljavadocker'
with:
images: |
ghcr.io/stirling-tools/stirling-pdf-test
@@ -193,7 +193,7 @@ jobs:
- name: Generate tags for ultra-lite (V2-master branch - production)
id: meta-lite
uses: docker/metadata-action@c299e40c65443455700f0fdfc63efafe5b349051 # v5.10.0
uses: docker/metadata-action@c1e51972afc2121e065aed6d45c65596fe445f3f # v5.8.0
if: github.ref == 'refs/heads/V2-master'
with:
images: |
@@ -205,10 +205,10 @@ jobs:
type=raw,value=${{ steps.versionNumber.outputs.versionNumber }}-ultra-lite
type=raw,value=latest-ultra-lite
- name: Generate tags for ultra-lite (V1_V2_merge branch - test)
- name: Generate tags for ultra-lite (alljavadocker branch - test)
id: meta-lite-test
uses: docker/metadata-action@c299e40c65443455700f0fdfc63efafe5b349051 # v5.10.0
if: github.ref == 'refs/heads/V1_V2_merge'
uses: docker/metadata-action@c1e51972afc2121e065aed6d45c65596fe445f3f # v5.8.0
if: github.ref == 'refs/heads/alljavadocker'
with:
images: |
ghcr.io/stirling-tools/stirling-pdf-test
+7 -7
View File
@@ -31,11 +31,11 @@ jobs:
id-token: write
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
- uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Set up JDK 17
uses: actions/setup-java@dded0888837ed1f317902acf8a20df0ad188d165 # v5.0.0
@@ -55,7 +55,7 @@ jobs:
- name: Install cosign
if: github.ref == 'refs/heads/master'
uses: sigstore/cosign-installer@faadad0cce49287aee09b3a48701e75088a2c6ad # v4.0.0
uses: sigstore/cosign-installer@d7543c93d881b35a8faa02e8e3605f69b7a1ce62 # v3.10.0
with:
cosign-release: "v2.4.1"
@@ -81,7 +81,7 @@ jobs:
password: ${{ github.token }}
- name: Set up QEMU
uses: docker/setup-qemu-action@c7c53464625b32c7a7e944ae62b3e17d2b600130 # v3.7.0
uses: docker/setup-qemu-action@29109295f81e9208d7d86ff1c6c12d2833863392 # v3.6.0
- name: Convert repository owner to lowercase
id: repoowner
@@ -89,7 +89,7 @@ jobs:
- name: Generate tags
id: meta
uses: docker/metadata-action@c299e40c65443455700f0fdfc63efafe5b349051 # v5.10.0
uses: docker/metadata-action@c1e51972afc2121e065aed6d45c65596fe445f3f # v5.8.0
if: github.ref != 'refs/heads/main'
with:
images: |
@@ -135,7 +135,7 @@ jobs:
- name: Generate tags ultra-lite
id: meta2
uses: docker/metadata-action@c299e40c65443455700f0fdfc63efafe5b349051 # v5.10.0
uses: docker/metadata-action@c1e51972afc2121e065aed6d45c65596fe445f3f # v5.8.0
if: github.ref != 'refs/heads/main'
with:
images: |
@@ -166,7 +166,7 @@ jobs:
- name: Generate tags fat
id: meta3
uses: docker/metadata-action@c299e40c65443455700f0fdfc63efafe5b349051 # v5.10.0
uses: docker/metadata-action@c1e51972afc2121e065aed6d45c65596fe445f3f # v5.8.0
with:
images: |
${{ secrets.DOCKER_HUB_USERNAME }}/s-pdf
+7 -7
View File
@@ -26,7 +26,7 @@ jobs:
matrix: ${{ steps.set-matrix.outputs.matrix }}
version: ${{ steps.versionNumber.outputs.versionNumber }}
steps:
- uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
- uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Get version number
id: versionNumber
@@ -68,12 +68,12 @@ jobs:
WINDOWS_CERTIFICATE: ${{ secrets.WINDOWS_CERTIFICATE }}
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@002fdce3c6a235733a90a27c80493a3241e56863 # v2.12.1
with:
egress-policy: audit
- name: Checkout repository
uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
uses: actions/checkout@11bd71901bbe5b1630ceea73d27597364c9af683 # v4.2.2
- name: Install dependencies (ubuntu only)
if: matrix.platform == 'ubuntu-22.04'
@@ -95,7 +95,7 @@ jobs:
targets: ${{ (matrix.platform == 'macos-15' || matrix.platform == 'macos-15-intel') && 'aarch64-apple-darwin,x86_64-apple-darwin' || '' }}
- name: Set up JDK 21
uses: actions/setup-java@dded0888837ed1f317902acf8a20df0ad188d165 # v5.0.0
uses: actions/setup-java@c5195efecf7bdfc987ee8bae7a71cb8b11521c00 # v4.7.1
with:
java-version: "21"
distribution: "temurin"
@@ -448,7 +448,7 @@ jobs:
egress-policy: audit
- name: Download build artifacts
uses: actions/download-artifact@018cc2cf5baa6db3ef3c5f8a56943fffe632ef53 # v6.0.0
uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 # v5.0.0
with:
name: Stirling-PDF-${{ matrix.name }}
@@ -517,7 +517,7 @@ jobs:
egress-policy: audit
- name: Download all signed artifacts
uses: actions/download-artifact@018cc2cf5baa6db3ef3c5f8a56943fffe632ef53 # v6.0.0
uses: actions/download-artifact@634f93cb2916e3fdff6788551b99b062d0335ce0 # v5.0.0
with:
pattern: Stirling-PDF-*-signed
path: ./artifacts
@@ -526,7 +526,7 @@ jobs:
run: ls -R ./artifacts
- name: Create GitHub Release
uses: softprops/action-gh-release@a06a81a03ee405af7f2048a818ed3f03bbf83c7b # v2.5.0
uses: softprops/action-gh-release@62c96d0c4e8a889135c1f3a25910db8dbe0e85f7 # v2.3.4
with:
tag_name: v${{ needs.determine-matrix.outputs.version }}
generate_release_notes: true
+4 -4
View File
@@ -35,12 +35,12 @@ jobs:
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- name: "Checkout code"
uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
with:
persist-credentials: false
@@ -67,7 +67,7 @@ jobs:
# Upload the results as artifacts (optional). Commenting out will disable uploads of run results in SARIF
# format to the repository Actions tab.
- name: "Upload artifact"
uses: actions/upload-artifact@330a01c490aca151604b8cf639adc76d48f6c5d4 # v5.0.0
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
with:
name: SARIF file
path: results.sarif
@@ -75,6 +75,6 @@ jobs:
# Upload the results to GitHub's code scanning dashboard.
- name: "Upload to code-scanning"
uses: github/codeql-action/upload-sarif@fdbfb4d2750291e159f0156def62b853c2798ca2 # v3.29.5
uses: github/codeql-action/upload-sarif@64d10c13136e1c5bce3e5fbde8d4906eeaafc885 # v3.29.5
with:
sarif_file: results.sarif
+76
View File
@@ -0,0 +1,76 @@
name: Run Sonarqube
on:
push:
branches:
- master
pull_request_target:
branches:
- main
workflow_dispatch:
# cancel in-progress jobs if a new job is triggered
# This is useful to avoid running multiple builds for the same branch if a new commit is pushed
# or a pull request is updated.
# It helps to save resources and time by ensuring that only the latest commit is built and tested
# This is particularly useful for long-running jobs that may take a while to complete.
# The `group` is set to a combination of the workflow name, event name, and branch name.
# This ensures that jobs are grouped by the workflow and branch, allowing for cancellation of
# in-progress jobs when a new commit is pushed to the same branch or a new pull request is opened.
concurrency:
group: ${{ github.workflow }}-${{ github.event_name }}-${{ github.event.pull_request.number || github.ref_name || github.ref }}
cancel-in-progress: true
permissions:
pull-requests: read
actions: read
jobs:
sonarqube:
if: ${{ vars.CI_PROFILE != 'lite' }}
runs-on: ubuntu-latest
steps:
- name: Harden Runner
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
with:
fetch-depth: 0
- name: Setup Gradle
uses: gradle/actions/setup-gradle@4d9f0ba0025fe599b4ebab900eb7f3a1d93ef4c2 # v5.0.0
- name: Build and analyze with Gradle
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
SONAR_TOKEN: ${{ secrets.SONAR_TOKEN }}
DISABLE_ADDITIONAL_FEATURES: false
STIRLING_PDF_DESKTOP_UI: true
run: |
./gradlew clean build sonar \
-Dsonar.projectKey=Stirling-Tools_Stirling-PDF \
-Dsonar.organization=stirling-tools \
-Dsonar.host.url=https://sonarcloud.io \
-Dsonar.login=${SONAR_TOKEN} \
-Dsonar.log.level=DEBUG \
--info
- name: Upload Problems Report on Failure
if: failure()
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
with:
name: gradle-problems-report
path: build/reports/problems/problems-report.html
retention-days: 7
- name: Upload Sonar Logs on Failure
if: failure()
uses: actions/upload-artifact@ea165f8d65b6e75b540449e92b4886f43607fa02 # v4.6.2
with:
name: sonar-logs
path: |
.scannerwork/report-task.txt
build/sonar/
retention-days: 7
+2 -2
View File
@@ -17,12 +17,12 @@ jobs:
pull-requests: write
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- name: 30 days stale issues
uses: actions/stale@997185467fa4f803885201cee163a9f38240193d # v10.1.1
uses: actions/stale@5f858e3efba33a5ca4407a664cc011ad407f2008 # v10.1.0
with:
repo-token: ${{ secrets.GITHUB_TOKEN }}
days-before-stale: 30
+2 -2
View File
@@ -27,11 +27,11 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
- uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Set up JDK 17
uses: actions/setup-java@dded0888837ed1f317902acf8a20df0ad188d165 # v5.0.0
+4 -4
View File
@@ -33,11 +33,11 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@ec9f2d5744a09debf3a187a3f4f675c53b671911 # v2.13.0
with:
egress-policy: audit
- uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
- uses: actions/checkout@08eba0b27e820071cde6df949e0beb9ba4906955 # v4.3.0
- name: Setup GitHub App Bot
id: setup-bot
@@ -47,7 +47,7 @@ jobs:
private-key: ${{ secrets.GH_APP_PRIVATE_KEY }}
- name: Set up Python
uses: actions/setup-python@83679a892e2d95755f2dac6acb0bfd1e9ac5d548 # v6.1.0
uses: actions/setup-python@a26af69be951a213d495a4c3e4e4022e16d87065 # v5.6.0
with:
python-version: "3.12"
cache: "pip" # caching pip dependencies
@@ -79,7 +79,7 @@ jobs:
- name: Create Pull Request
if: always()
uses: peter-evans/create-pull-request@98357b18bf14b5342f975ff684046ec3b2a07725 # v8.0.0
uses: peter-evans/create-pull-request@271a8d0340265f705b14b6d32b9829c1cb33d45e # v7.0.8
with:
token: ${{ steps.setup-bot.outputs.token }}
commit-message: Update files
+7 -7
View File
@@ -25,12 +25,12 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- name: Checkout repository
uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Set up JDK
uses: actions/setup-java@dded0888837ed1f317902acf8a20df0ad188d165 # v5.0.0
@@ -125,7 +125,7 @@ jobs:
outputs:
frontend: ${{ steps.changes.outputs.frontend }}
steps:
- uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
- uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Check for file changes
uses: dorny/paths-filter@de90cc6fb38fc0963ad72b210f1f284cd68cea36 # v3.0.2
@@ -140,14 +140,14 @@ jobs:
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
- uses: actions/checkout@1af3b93b6815bc44a9784bd300feb67ff0d1eeb3 # v6.0.0
- uses: actions/checkout@08c6903cd8c0fde910a37f88322edcfb5dd907a8 # v5.0.0
- name: Set up Node
uses: actions/setup-node@2028fbc5c25fe9cf00d9f06a71cc4710d4507903 # v6.0.0
uses: actions/setup-node@a0853c24544627f65ddf259abe73b1d18a591444 # v5.0.0
with:
cache: 'npm'
cache-dependency-path: frontend/package-lock.json
@@ -176,7 +176,7 @@ jobs:
steps:
- name: Harden Runner
uses: step-security/harden-runner@95d9a5deda9de15063e7595e9719c11c38c90ae2 # v2.13.2
uses: step-security/harden-runner@f4a75cfd619ee5ce8d5b864b0d183aff3c69b55a # v2.13.1
with:
egress-policy: audit
+1 -5
View File
@@ -31,11 +31,6 @@ exampleYmlFiles/stirling/
/testing/file_snapshots
SwaggerDoc.json
# Docker bind-mount volumes (local data)
AI-Document-Generator-main/backend/data/
AI-Document-Generator-main/backend/output/
docker/compose/stirling/
# Frontend build artifacts copied to backend static resources
# These are generated by npm build and should not be committed
app/core/src/main/resources/static/assets/
@@ -246,3 +241,4 @@ docs/type3/signatures/
# Type3 sample PDFs (development only)
**/type3/samples/
+2 -2
View File
@@ -1,6 +1,6 @@
repos:
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.14.2
rev: v0.12.7
hooks:
- id: ruff
args:
@@ -26,7 +26,7 @@ repos:
hooks:
- id: gitleaks
- repo: https://github.com/pre-commit/pre-commit-hooks
rev: v6.0.0
rev: v5.0.0
hooks:
- id: end-of-file-fixer
files: ^.*(\.js|\.java|\.py|\.yml)$
-45
View File
@@ -1,45 +0,0 @@
# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
env/
venv/
ENV/
build/
dist/
*.egg-info/
# Node
node_modules/
npm-debug.log*
yarn-debug.log*
yarn-error.log*
dist/
.vite/
# Environment
.env
.env.local
# LaTeX outputs
*.aux
*.log
*.out
*.toc
*.pdf
*.tex
backend/output/
backend/data/user_styles.json
# IDE
.vscode/
.idea/
*.swp
*.swo
*~
# OS
.DS_Store
Thumbs.db
-32
View File
@@ -1,32 +0,0 @@
# syntax=docker/dockerfile:1.5
FROM python:3.11-slim
# Install full TeXLive so LLM outputs (siunitx, paracol, tikz, etc.) compile reliably.
RUN --mount=type=cache,target=/var/cache/apt,sharing=locked \
apt-get update && \
apt-get install -y --no-install-recommends \
texlive-full \
latexmk \
ghostscript \
poppler-utils \
ca-certificates \
&& rm -rf /var/lib/apt/lists/*
# Set working directory
WORKDIR /app
# Copy backend files
COPY backend/requirements.txt .
RUN --mount=type=cache,target=/root/.cache/pip \
pip install -r requirements.txt
COPY backend/ .
# Create output directories
RUN mkdir -p /app/output /app/data
# Expose port
EXPOSE 5000
# Run the Flask app
CMD ["python", "app.py"]
-173
View File
@@ -1,173 +0,0 @@
# LaTeX PDF Generator
AI-powered document generator using LaTeX. Creates professional documents (invoices, resumes, contracts, etc.) from natural language prompts with conversational editing.
## Features
- 🎨 ChatGPT-like interface with split-screen PDF preview (main window only)
- 📝 Generates LaTeX documents from natural language and compiles to PDF in Docker
- 🔄 Conversational editing with PDF regeneration on every turn
- 💾 Style memory + template reuse per user/team and document type
- 🗂️ Version history with per-iteration PDFs you can reopen
- 📄 Supports: Invoices, Resumes, Contracts, Letters, Reports, poems, proposals, and more
## Quick Start
### Option 1: Docker (Recommended)
```bash
# Build the Docker image
docker build -t latex-generator .
# Run the backend
docker run -p 5000:5000 latex-generator
```
### Option 2: Local Development
#### Backend Setup
```bash
cd backend
# Install Python dependencies
pip install -r requirements.txt
# Install LaTeX (if not already installed)
# On Ubuntu/Debian:
sudo apt-get install texlive-latex-base texlive-latex-extra
# On Mac:
brew install --cask mactex
# On Windows:
# Download and install MiKTeX from https://miktex.org/download
# Run the backend
python app.py
```
Backend will run on `http://localhost:5000`
#### Frontend Setup
```bash
cd frontend
# Install dependencies
npm install
# Run the dev server
npm run dev
```
Frontend will run on `http://localhost:3000`
## Usage
1. Open `http://localhost:3000` in your browser
2. Type a prompt like "Create an invoice for web development services"
3. The AI generates LaTeX code and compiles it to PDF
4. Continue chatting to refine the document
5. Download the final PDF
## Example Prompts
- "Create a professional invoice for $1,500 in consulting services"
- "Generate a resume for a senior software engineer with 5 years experience"
- "Make a business letter to a client about project completion"
- "Create a contract for freelance web development"
## Configuration
### OpenAI API (Optional)
To use real AI generation instead of mock templates:
1. Copy `.env.example` to `.env`
2. Add your OpenAI API key: `OPENAI_API_KEY=sk-...`
3. (Optional) Choose models:
- Smart (full generation): `SMART_MODEL=gpt-5.1` (default)
- Fast (intent/pre checks): `FAST_MODEL=gpt-4.1-nano` (default)
3. (Legacy SDK) We pin `openai==0.28.1` in Docker to avoid client init issues. No code changes needed.
## Project Structure
```
latex-pdf-generator/
├── frontend/ # React + TypeScript + Tailwind
│ ├── src/
│ │ ├── components/
│ │ │ ├── landing/ # Landing hero + CTA
│ │ │ ├── modals/ # Reusable modal(s)
│ │ │ ├── workspace/ # Chat panel, preview, history
│ │ │ └── ui/ # Buttons, button groups, etc.
│ │ ├── hooks/ # Workflow + speech capture hooks
│ │ ├── types/ # Shared TypeScript interfaces
│ │ ├── App.tsx # Thin orchestrator
│ │ └── main.tsx
│ ├── .eslintrc.cjs # ESLint config (React + TS)
│ ├── package.json
│ └── vite.config.ts
├── backend/ # Flask + LaTeX
│ ├── app.py # Routes + Flask app factory
│ ├── ai_generation.py # OpenAI + mock generation helpers
│ ├── briefs.py # Guided brief collection utilities
│ ├── config.py # Logging + environment setup
│ ├── document_types.py # Doc-type heuristics
│ ├── latex_utils.py # LaTeX sanitizers + layout helpers
│ ├── pdf_utils.py # PDF compilation + render helpers
│ ├── storage.py # JSON persistence for users/templates
│ ├── styles.py # Style preference heuristics
│ ├── vision.py # Layout extraction via multimodal GPT
│ ├── requirements.txt
│ └── data/ # User style storage
├── Dockerfile
└── README.md
```
### Linting
The frontend now ships with ESLint + TypeScript rules that keep the new modular structure tidy:
```bash
cd frontend
npm run lint
```
Backend linting can be added with your preferred tool (e.g., ruff or flake8) by pointing it at the new small modules in `backend/`.
## Troubleshooting
### LaTeX compilation fails
- Ensure `pdflatex` is in your PATH
- Check logs in the backend console
- Verify LaTeX packages are installed
### CORS errors
- Make sure both frontend and backend are running
- Frontend proxy is configured in `vite.config.ts`
### PDF not displaying
- Check browser console for errors
- Ensure the backend `/output` endpoint is accessible
- Try opening the PDF URL directly
## Development
### Mock Mode (Current)
The app currently uses mock LaTeX templates for quick testing. To enable real AI:
1. Get an OpenAI API key
2. Set `OPENAI_API_KEY` and optionally override:
- `SMART_MODEL` (default `gpt-5.1`)
- `FAST_MODEL` (default `gpt-4.1-nano`)
3. Restart the backend; it auto-detects whether to call the live model or the bundled mock templates
## License
MIT
@@ -1,659 +0,0 @@
from __future__ import annotations
import json
import re
from typing import Any, Dict, List, Optional
import time
from config import CLIENT_MODE, SMART_MODEL, STREAMING_ENABLED, get_chat_model, logger
from langchain_utils import to_lc_messages
from storage import save_user_style
from prompts import latex_system_prompt, latex_context_messages
def generate_outline_with_llm(
prompt: str,
document_type: str,
constraints: Optional[Dict[str, Any]] = None,
) -> str:
if CLIENT_MODE == "langchain":
constraint_text = ""
if constraints:
tone = constraints.get("tone")
audience = constraints.get("audience")
pages = constraints.get("pageCount")
constraint_text = f"Tone: {tone}. Audience: {audience}. Target pages: {pages}."
system_prompt = (
"You are an outline generator for document creation.\n"
f"Document type: {document_type}\n"
f"{constraint_text}\n"
"Return a concise outline with section titles and short descriptions.\n"
"Keep each description to roughly 6-12 words.\n"
"Ensure the outline scope fits the target page count.\n"
"Output plain text only, using a numbered list with 5-9 sections."
)
messages: List[Dict[str, Any]] = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": prompt},
]
try:
llm = get_chat_model(SMART_MODEL)
if llm:
start = time.perf_counter()
response = llm.invoke(to_lc_messages(messages))
elapsed = time.perf_counter() - start
content = response.content or ""
usage = getattr(response, "usage_metadata", None)
logger.info(
"[AI] outline model=%s elapsed=%.2fs chars=%s usage=%s",
SMART_MODEL,
elapsed,
len(str(content)),
usage,
)
if content:
return str(content).strip()
except Exception as exc:
logger.error("[AI] Outline generation failed, falling back: %s", exc)
safe_prompt = prompt.strip() or "Document"
return (
"1) Introduction - Summary of the document goals.\n"
"2) Background - Context and key assumptions.\n"
"3) Main Content - Core points and supporting details.\n"
"4) Evidence - Data, examples, or references.\n"
"5) Conclusion - Wrap-up and next steps.\n"
f"Notes: Tailor details to '{safe_prompt}'."
)
def _parse_outline_to_sections(outline_text: str) -> List[Dict[str, str]]:
lines = [
line.strip()
for line in outline_text.split("\n")
if line.strip() and not re.match(r"^(section|details)$", line.strip(), re.IGNORECASE)
]
sections: List[Dict[str, str]] = []
i = 0
while i < len(lines):
cleaned = re.sub(r"^\d+[\).\s-]+", "", lines[i]).strip()
if not cleaned:
i += 1
continue
split = re.split(r"[-:]+", cleaned, maxsplit=1)
if len(split) > 1:
sections.append({"label": split[0].strip() or "Section", "value": split[1].strip()})
i += 1
continue
next_line = lines[i + 1].strip() if i + 1 < len(lines) else ""
if next_line and not re.match(r"^\d+[\).\s-]+", next_line):
sections.append({"label": cleaned, "value": next_line})
i += 2
continue
sections.append({"label": cleaned, "value": ""})
i += 1
return sections
def _extract_fields_from_prompt(prompt: str, fields: List[Dict[str, Any]]) -> List[Dict[str, str]]:
lines = [line.strip() for line in prompt.split("\n") if line.strip()]
kv_pairs: Dict[str, str] = {}
for line in lines:
match = re.match(r"^([^:]{2,40}):\s*(.+)$", line)
if match:
kv_pairs[match.group(1).strip().lower()] = match.group(2).strip()
email_match = re.search(r"[A-Z0-9._%+-]+@[A-Z0-9.-]+\.[A-Z]{2,}", prompt, re.IGNORECASE)
phone_match = re.search(r"(\+?\d[\d\s().-]{7,})", prompt)
date_match = re.search(r"\b\d{1,2}[\/.-]\d{1,2}[\/.-]\d{2,4}\b", prompt)
money_match = re.search(r"\$\s?\d[\d,]*(?:\.\d{2})?", prompt)
filled: List[Dict[str, str]] = []
for field in fields:
label = str(field.get("label", "Field"))
value = str(field.get("value", "") or "")
if value.strip():
filled.append({"label": label, "value": value})
continue
label_lower = label.lower()
for key, val in kv_pairs.items():
if key in label_lower:
value = val
break
if not value and email_match and "email" in label_lower:
value = email_match.group(0)
if not value and phone_match and "phone" in label_lower:
value = phone_match.group(0)
if not value and date_match and ("date" in label_lower or "due" in label_lower):
value = date_match.group(0)
if not value and money_match and ("total" in label_lower or "amount" in label_lower):
value = money_match.group(0)
filled.append({"label": label, "value": value})
return filled
def generate_field_values(
prompt: str,
document_type: str,
fields: List[Dict[str, Any]],
constraints: Optional[Dict[str, Any]] = None,
) -> List[Dict[str, str]]:
if CLIENT_MODE == "langchain":
constraint_text = ""
if constraints:
tone = constraints.get("tone")
audience = constraints.get("audience")
pages = constraints.get("pageCount")
constraint_text = f"Tone: {tone}. Audience: {audience}. Target pages: {pages}."
system_prompt = (
"You are extracting field values from a user prompt.\n"
"Return a JSON array of objects with keys: label, value.\n"
"Only fill values that are explicitly stated or strongly implied.\n"
"If unknown, return an empty string.\n"
f"{constraint_text}\n"
"Output JSON only."
)
messages: List[Dict[str, Any]] = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": f"Document type: {document_type}"},
{"role": "user", "content": f"Prompt:\n{prompt}"},
{"role": "user", "content": f"Fields:\n{json.dumps(fields, ensure_ascii=True)}"},
]
try:
llm = get_chat_model(SMART_MODEL)
if llm:
start = time.perf_counter()
response = llm.invoke(to_lc_messages(messages))
elapsed = time.perf_counter() - start
content = response.content or ""
usage = getattr(response, "usage_metadata", None)
logger.info(
"[AI] field-extract model=%s elapsed=%.2fs chars=%s usage=%s",
SMART_MODEL,
elapsed,
len(str(content)),
usage,
)
if content:
parsed = _extract_json_array(str(content))
if parsed:
return [
{
"label": str(item.get("label", "Field")),
"value": str(item.get("value", "")),
}
for item in parsed
if isinstance(item, dict)
]
except Exception as exc:
logger.error("[AI] Field extraction failed, falling back: %s", exc)
return _extract_fields_from_prompt(prompt, fields)
def _extract_json_array(payload: str) -> Optional[List[Dict[str, Any]]]:
try:
return json.loads(payload)
except json.JSONDecodeError:
match = re.search(r"\[[\s\S]*\]", payload)
if not match:
return None
try:
return json.loads(match.group(0))
except json.JSONDecodeError:
return None
def generate_section_draft(
prompt: str,
document_type: str,
outline_text: str,
constraints: Optional[Dict[str, Any]] = None,
) -> List[Dict[str, str]]:
if CLIENT_MODE == "langchain":
constraint_text = ""
if constraints:
tone = constraints.get("tone")
audience = constraints.get("audience")
pages = constraints.get("pageCount")
constraint_text = f"Tone: {tone}. Audience: {audience}. Target pages: {pages}."
system_prompt = (
"You are generating section content for a document.\n"
"Return a JSON array of objects with keys: label, value.\n"
"Use the provided outline sections as labels; values should be polished draft text.\n"
f"{constraint_text}\n"
"Keep the total length appropriate to the target pages.\n"
"Output JSON only."
)
messages: List[Dict[str, Any]] = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": f"Document type: {document_type}"},
{"role": "user", "content": f"Outline:\n{outline_text}"},
{"role": "user", "content": f"Prompt:\n{prompt}"},
]
try:
llm = get_chat_model(SMART_MODEL)
if llm:
start = time.perf_counter()
response = llm.invoke(to_lc_messages(messages))
elapsed = time.perf_counter() - start
content = response.content or ""
usage = getattr(response, "usage_metadata", None)
logger.info(
"[AI] section-draft model=%s elapsed=%.2fs chars=%s usage=%s",
SMART_MODEL,
elapsed,
len(str(content)),
usage,
)
if content:
parsed = _extract_json_array(str(content))
if parsed:
return [
{
"label": str(item.get("label", "Section")),
"value": str(item.get("value", "")),
}
for item in parsed
if isinstance(item, dict)
]
except Exception as exc:
logger.error("[AI] Section draft generation failed, falling back: %s", exc)
if outline_text.strip():
return _parse_outline_to_sections(outline_text)
fallback_label = "Main Content"
return [{"label": fallback_label, "value": prompt.strip() or "Draft content"}]
def _fallback_template_fill(template_latex: str, outline_text: str, draft_text: Optional[str] = None) -> str:
default_text = draft_text or outline_text or "Details pending."
replacements = {
"TITLE": "Project Overview",
"SUBTITLE": "Executive Summary",
"AUTHOR": "Jane Doe",
"AUTHOR_LIST": "Jane Doe, John Smith",
"AFFILIATIONS": "John Smith Consulting",
"ABSTRACT": default_text,
"KEYWORDS": "keyword1, keyword2, keyword3",
"INTRODUCTION": default_text,
"RELATED_WORK": default_text,
"METHODOLOGY": default_text,
"RESULTS": default_text,
"DISCUSSION": default_text,
"CONCLUSION": default_text,
"REFERENCES": default_text,
"MAIN_TEXT": default_text,
"FIGURES_TABLES": default_text,
"REPORT_TITLE": "Business Report",
"DATE": "2025-01-01",
"EXEC_SUMMARY": default_text,
"BACKGROUND": default_text,
"FINDINGS": default_text,
"RECOMMENDATIONS": default_text,
"APPENDIX": default_text,
"NEWSLETTER_TITLE": "Doe Consulting Monthly",
"TOP_STORY": default_text,
"UPDATES": default_text,
"SPOTLIGHT": default_text,
"FOOTER": "Contact: info@example.com",
"RECIPE_TITLE": "Recipe Title",
"SERVINGS": "Serves 4",
"TIME": "30 minutes",
"INGREDIENTS": "\\\\begin{itemize}\\\\item Ingredient A\\\\item Ingredient B\\\\end{itemize}",
"INSTRUCTIONS": default_text,
"NOTES": "Notes and tips.",
"BUSINESS_NAME": "John Smith Consulting",
"BUSINESS_ADDRESS": "123 Example Street, Example City",
"BUSINESS_CONTACT": "billing@example.com | (555) 000-0000",
"INVOICE_NUMBER": "INV-1001",
"ISSUE_DATE": "2025-01-01",
"DUE_DATE": "2025-01-15",
"CLIENT_NAME": "Doe Corporation",
"CLIENT_ADDRESS": "456 Sample Avenue, Example City",
"CLIENT_CONTACT": "ap@example.com",
"LINE_ITEMS": "Service & 1 & $1000 & $1000 \\\\\\\\",
"SUBTOTAL": "$1000",
"TAXES": "$0",
"TOTAL": "$1000",
"PAYMENT_TERMS": "Net 15",
"PAYMENT_METHODS": "Bank transfer, credit card",
"STUDENT_NAME": "Jane Doe",
"COURSE_NAME": "Business Communications",
"INSTRUCTOR_NAME": "Dr. Rivera",
"ASSIGNMENT_TITLE": "Market Analysis",
"PROMPT": default_text,
"RESPONSE": default_text,
"CHAPTER_ONE_TITLE": "Chapter One",
"CHAPTER_ONE": default_text,
"CHAPTER_TWO_TITLE": "Chapter Two",
"CHAPTER_TWO": default_text,
"PREFACE": default_text,
"PUBLISHER": "Doe Press",
"NAME": "Jane Doe",
"TITLE_PAGE": "Project Overview",
"EMAIL": "jane.doe@example.com",
"PHONE": "(555) 000-0000",
"LOCATION": "Example City, USA",
"SUMMARY": default_text,
"EXPERIENCE": default_text,
"EDUCATION": default_text,
"SKILLS": default_text,
"PROJECTS": default_text,
"SUBJECT": "Subject",
"BODY": default_text,
"RECIPIENT_NAME": "John Smith",
"RECIPIENT_TITLE": "Hiring Manager",
"RECIPIENT_COMPANY": "Doe Corporation",
"RECIPIENT_ADDRESS": "456 Sample Avenue, Example City",
"SENDER_NAME": "Jane Doe",
"SENDER_ADDRESS": "123 Example Street, Example City",
"SENDER_EMAIL": "jane.doe@example.com",
"MONTH_YEAR": "January 2025",
"THEME": "Theme",
"WEEK_ROWS": "1 & 2 & 3 & 4 & 5 & 6 & 7 \\\\\\\\ \\\\hline",
"HEADLINE": "Launch Announcement",
"SUBTEXT": "Introducing our latest release.",
"CALL_TO_ACTION": "Visit example.com to learn more.",
"CONTACT": "contact@example.com",
"EXPERIMENT_TITLE": "Experiment",
"OBJECTIVE": default_text,
"MATERIALS": default_text,
"PROCEDURE": default_text,
"OBSERVATIONS": default_text,
"INSTITUTION": "Doe Institute",
"PRESENTER": "Jane Doe",
"AGENDA": default_text,
"KEY_POINTS": default_text,
"DATA_VISUALS": default_text,
}
def replace(match: re.Match[str]) -> str:
key = match.group(1).strip()
return replacements.get(key, default_text)
return re.sub(r"<<([A-Z0-9_]+)>>", replace, template_latex)
def generate_template_fill_stream(
template_latex: str,
document_type: str,
outline_text: str,
draft_sections: Optional[List[Dict[str, str]]] = None,
constraints: Optional[Dict[str, Any]] = None,
style_profile: Optional[Dict[str, Any]] = None,
):
"""Fill a LaTeX template by replacing placeholders."""
if CLIENT_MODE == "langchain":
constraints_text = ""
if constraints:
tone = constraints.get("tone")
audience = constraints.get("audience")
pages = constraints.get("pageCount")
constraints_text = f"Tone: {tone}. Audience: {audience}. Target pages: {pages}."
style_text = ""
if style_profile:
font = style_profile.get("font_preference")
layout = style_profile.get("layout_preference")
accent = style_profile.get("color_accent")
style_text = f"Style preferences: font={font}, layout={layout}, accent={accent}."
if draft_sections:
constraints_text = f"{constraints_text}\nUse the section content to inform placeholder values."
system_prompt = (
"You are a LaTeX template filler.\n"
"Return the full LaTeX document with placeholders filled.\n"
"Rules:\n"
"1) Only replace placeholders like <<PLACEHOLDER>>.\n"
"2) Do not change any other LaTeX layout/commands.\n"
"3) Output ONLY LaTeX (no markdown).\n"
"4) If you add color, use the accent token name 'accent'.\n"
f"{constraints_text}\n"
f"{style_text}\n"
"Keep the final output within the target page count.\n"
)
messages: List[Dict[str, Any]] = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": f"Document type: {document_type}"},
{"role": "user", "content": f"Outline/context:\n{outline_text}"},
{"role": "user", "content": f"Template:\n{template_latex}"},
]
if draft_sections:
messages.append(
{
"role": "user",
"content": f"Section content (JSON):\n{json.dumps(draft_sections, ensure_ascii=True)}",
}
)
try:
llm = get_chat_model(SMART_MODEL, streaming=True)
if llm:
start = time.perf_counter()
total_chars = 0
chunk_count = 0
first_chunk = None
for chunk in llm.stream(to_lc_messages(messages)):
if chunk.content:
if first_chunk is None:
first_chunk = time.perf_counter()
chunk_count += 1
total_chars += len(str(chunk.content))
yield chunk.content
elapsed = time.perf_counter() - start
logger.info(
"[AI] template-fill-stream model=%s elapsed=%.2fs first_chunk=%.2fs chunks=%s chars=%s",
SMART_MODEL,
elapsed,
(first_chunk - start) if first_chunk else -1.0,
chunk_count,
total_chars,
)
return
except Exception as exc:
logger.error("[AI] Template fill failed, falling back: %s", exc)
draft_text = None
if draft_sections:
draft_text = "\n".join(
f"{section.get('label', 'Section')}: {section.get('value', '')}"
for section in draft_sections
)
filled = _fallback_template_fill(template_latex, outline_text, draft_text)
chunk_size = 200
for i in range(0, len(filled), chunk_size):
yield filled[i : i + chunk_size]
def generate_latex_with_llm(
prompt: str,
history: List[Dict[str, str]],
style_profile: Dict[str, Any],
document_type: str,
template_hint: Optional[str] = None,
current_latex: Optional[str] = None,
structured_brief: Optional[str] = None,
edit_mode: bool = False,
) -> str:
"""Call the LLM (when available) or fall back to deterministic templates."""
if CLIENT_MODE == "langchain":
messages: List[Dict[str, Any]] = [{"role": "system", "content": latex_system_prompt(style_profile, document_type, template_hint)}]
messages.extend(history)
messages.extend(latex_context_messages(template_hint, current_latex, structured_brief))
messages.append({"role": "user", "content": prompt})
try:
logger.info(
"[AI] Using live model=%s doc_type=%s template_hint=%s",
SMART_MODEL,
document_type,
"yes" if template_hint else "no",
)
llm = get_chat_model(SMART_MODEL)
if llm:
start = time.perf_counter()
response = llm.invoke(to_lc_messages(messages))
elapsed = time.perf_counter() - start
content = response.content or ""
usage = getattr(response, "usage_metadata", None)
logger.info(
"[AI] latex-generate model=%s elapsed=%.2fs chars=%s usage=%s",
SMART_MODEL,
elapsed,
len(str(content)),
usage,
)
return content
except Exception as exc:
logger.error("[AI] LangChain generation failed, falling back to mock: %s", exc)
lower_prompt = prompt.lower()
if "invoice" in lower_prompt:
save_user_style("default_user", {"last_doc_type": "invoice"})
details = structured_brief or prompt or "Invoice details provided by user."
return r"""\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage{geometry}
\geometry{a4paper, margin=1in}
\begin{document}
\begin{center}
{\LARGE \textbf{INVOICE}}\\[0.5cm]
\#1023\\
\today
\end{center}
\section*{Bill To:}
Client Name \\
123 Business Rd.
\section*{Items}
\begin{tabular}{lr}
\textbf{Service} & \textbf{Amount} \\
\hline
% Replace with your line items
Description & \$0.00 \\
Description & \$0.00 \\
\hline
\textbf{Total Due} & \textbf{\$0.00} \\
\end{tabular}
\section*{Notes}
""" + details + r"""
\end{document}"""
if "resume" in lower_prompt or "cv" in lower_prompt:
save_user_style("default_user", {"last_doc_type": "resume"})
details = structured_brief or prompt or "Resume details provided by user."
return r"""\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage{geometry}
\geometry{a4paper, margin=0.75in}
\begin{document}
\section*{Details}
""" + details + r"""
\end{document}"""
base_doc = r"""\documentclass{article}
\usepackage[utf8]{inputenc}
\usepackage{geometry}
\geometry{a4paper, margin=1in}
\begin{document}
"""
content = structured_brief or prompt or ""
base_doc += r"""\section*{Document}
""" + content + r"""
\end{document}"""
return base_doc
def generate_latex_with_llm_stream(
prompt: str,
history: List[Dict[str, str]],
style_profile: Dict[str, Any],
document_type: str,
template_hint: Optional[str] = None,
current_latex: Optional[str] = None,
structured_brief: Optional[str] = None,
edit_mode: bool = False,
):
"""Stream LaTeX generation from LLM, yielding chunks as they arrive."""
if not STREAMING_ENABLED:
full_latex = generate_latex_with_llm(
prompt, history, style_profile, document_type, template_hint, current_latex, structured_brief, edit_mode=edit_mode
)
chunk_size = 100
for i in range(0, len(full_latex), chunk_size):
yield full_latex[i:i + chunk_size]
return
if CLIENT_MODE == "langchain":
messages: List[Dict[str, Any]] = [{"role": "system", "content": latex_system_prompt(style_profile, document_type, template_hint)}]
messages.extend(history)
messages.extend(latex_context_messages(template_hint, current_latex, structured_brief))
messages.append({"role": "user", "content": prompt})
try:
logger.info(
"[AI] Streaming from model=%s doc_type=%s template_hint=%s",
SMART_MODEL,
document_type,
"yes" if template_hint else "no",
)
llm = get_chat_model(SMART_MODEL, streaming=True)
if llm:
start = time.perf_counter()
total_chars = 0
chunk_count = 0
first_chunk = None
for chunk in llm.stream(to_lc_messages(messages)):
if chunk.content:
if first_chunk is None:
first_chunk = time.perf_counter()
chunk_count += 1
total_chars += len(str(chunk.content))
yield chunk.content
elapsed = time.perf_counter() - start
logger.info(
"[AI] latex-stream model=%s elapsed=%.2fs first_chunk=%.2fs chunks=%s chars=%s",
SMART_MODEL,
elapsed,
(first_chunk - start) if first_chunk else -1.0,
chunk_count,
total_chars,
)
return
except Exception as exc:
logger.error("[AI] LangChain streaming failed, falling back to mock: %s", exc)
# Fallback to non-streaming for mock mode
full_latex = generate_latex_with_llm(
prompt, history, style_profile, document_type, template_hint, current_latex, structured_brief, edit_mode=edit_mode
)
# Simulate streaming by yielding chunks
chunk_size = 100
for i in range(0, len(full_latex), chunk_size):
yield full_latex[i:i + chunk_size]
__all__ = [
"generate_outline_with_llm",
"generate_section_draft",
"generate_field_values",
"generate_latex_with_llm",
"generate_latex_with_llm_stream",
"generate_template_fill_stream",
]
-964
View File
@@ -1,964 +0,0 @@
import os
import mimetypes
import subprocess
import uuid
from pathlib import Path
from datetime import datetime
from typing import Any, Dict, List, Optional, Tuple
import re
import time
import threading
import queue
import urllib.error
import urllib.request
from concurrent.futures import ThreadPoolExecutor
from flask import Flask, jsonify, request, send_file, Response, stream_with_context
from flask_cors import CORS
import json
from ai_generation import (
generate_latex_with_llm,
generate_latex_with_llm_stream,
generate_outline_with_llm,
generate_section_draft,
generate_field_values,
generate_template_fill_stream,
)
from briefs import gather_brief, _preprocess_intent
from config import (
CLIENT_MODE,
SMART_MODEL,
OUTPUT_DIR,
ASSETS_DIR,
TEMPLATE_DIR,
JAVA_BACKEND_URL,
PREVIEW_MAX_INFLIGHT,
get_chat_model,
logger,
)
from langchain_utils import to_lc_messages
from document_types import detect_document_type
from latex_utils import apply_style_overrides, clean_generated_latex
from pdf_utils import compile_latex_to_pdf, render_pdf_to_images
from pdf_text_editor import convert_pdf_to_text_editor_document
from storage import (
load_user_style,
load_user_templates,
load_versions,
save_user_style,
save_user_template,
save_version,
)
from styles import update_style_profile_from_prompt
from vision import vision_layout_from_images
from prompts import pdf_qa_system_prompt
app = Flask(__name__)
CORS(app)
@app.before_request
def log_job_request_sequence() -> None:
job_id = request.headers.get("X-Job-Id")
if not job_id:
return
seq = request.headers.get("X-Job-Seq", "?")
total = request.headers.get("X-Job-Total", "?")
logger.info("[HTTP] job_id=%s req=%s/%s %s %s", job_id, seq, total, request.method, request.path)
def _json_body() -> Dict[str, Any]:
return request.get_json(silent=True) or {}
def _require_ai_enabled() -> Optional[Any]:
if CLIENT_MODE != "langchain":
return jsonify({"error": "AI is disabled. Set OPENAI_API_KEY to enable AI features."}), 503
return None
def _java_url(path: str) -> str:
base = JAVA_BACKEND_URL.rstrip("/")
if not path.startswith("/"):
path = "/" + path
return f"{base}{path}"
def _java_request_json(method: str, path: str, payload: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
url = _java_url(path)
data = None
headers = {"Content-Type": "application/json"}
if payload is not None:
data = json.dumps(payload).encode("utf-8")
req = urllib.request.Request(url, data=data, headers=headers, method=method)
try:
with urllib.request.urlopen(req, timeout=10) as resp:
body = resp.read().decode("utf-8")
return json.loads(body) if body else {}
except urllib.error.HTTPError as exc:
detail = exc.read().decode("utf-8") if exc.fp else ""
logger.error("[JAVA] %s %s failed status=%s detail=%s", method, path, exc.code, detail)
raise
def _fetch_ai_session(session_id: str) -> Dict[str, Any]:
return _java_request_json("GET", f"/api/v1/ai/create/internal/sessions/{session_id}")
def _update_ai_session(session_id: str, payload: Dict[str, Any]) -> None:
_java_request_json("POST", f"/api/v1/ai/create/internal/sessions/{session_id}/update", payload)
def _sanitize_doc_type(value: str) -> str:
cleaned = re.sub(r"[^a-zA-Z0-9_]+", "", (value or "").lower())
return cleaned or "miscellaneous"
def _select_template(doc_type: str, template_id: Optional[str]) -> Optional[str]:
safe_doc_type = _sanitize_doc_type(doc_type)
base_dir = Path(TEMPLATE_DIR) / safe_doc_type
if not base_dir.exists() or not base_dir.is_dir():
return None
if template_id:
safe_template = re.sub(r"[^a-zA-Z0-9_-]+", "", template_id)
if safe_template:
candidate = base_dir / f"{safe_template}.tex"
if candidate.exists():
return candidate.read_text(encoding="utf-8", errors="replace")
default_path = base_dir / "default.tex"
if default_path.exists():
return default_path.read_text(encoding="utf-8", errors="replace")
for tex_file in sorted(base_dir.glob("*.tex")):
return tex_file.read_text(encoding="utf-8", errors="replace")
return None
@app.route("/api/intent/check", methods=["POST"])
def intent_check() -> Any:
try:
data = _json_body()
prompt: str = data.get("prompt", "")
history: List[Dict[str, str]] = data.get("conversationHistory") or []
current_latex: Optional[str] = data.get("currentLatex")
current_pdf_url: Optional[str] = data.get("currentPdfUrl")
doc_type = detect_document_type(prompt, current_latex)
intent = _preprocess_intent(prompt, history, bool(current_pdf_url), current_latex)
intent["documentType"] = doc_type
intent["hasPdf"] = bool(current_pdf_url)
return jsonify(intent)
except Exception as exc: # noqa: BLE001
logger.error("[INTENT] intent_check failed: %s", exc, exc_info=True)
return jsonify({"wants_pdf": True, "has_enough_info": True, "allow_makeup": False, "reason": str(exc)}), 500
@app.route("/api/pdf/answer", methods=["POST"])
def pdf_answer() -> Any:
data = _json_body()
pdf_url = data.get("pdfUrl")
question = data.get("question")
if not pdf_url or not question:
return jsonify({"error": "Missing pdfUrl or question"}), 400
filename = os.path.basename(pdf_url.split("?")[0])
if not filename.lower().endswith(".pdf"):
return jsonify({"error": "Invalid pdf file"}), 400
pdf_path = os.path.join(OUTPUT_DIR, filename)
if not os.path.exists(pdf_path):
return jsonify({"error": "PDF not found"}), 404
try:
doc = convert_pdf_to_text_editor_document(pdf_path)
except Exception as exc: # noqa: BLE001
logger.error("[PDF-ANSWER] failed to parse pdf: %s", exc, exc_info=True)
return jsonify({"error": "Failed to read PDF content"}), 500
pages = doc.get("document", {}).get("pages", []) if doc else []
snippets: List[str] = []
for page in pages:
for elem in page.get("textElements", []) or []:
text = elem.get("text")
if text:
snippets.append(str(text))
if not snippets:
return jsonify({"error": "No readable text in PDF"}), 400
# Normalize and limit context
context = " ".join(snippets)
context = " ".join(context.split()) # normalize whitespace
max_context = 10000
if len(context) > max_context:
context = context[:max_context]
# Heuristic helpers
def _sentences(text: str) -> List[str]:
return [s.strip() for s in re.split(r"(?<=[.!?])\s+", text) if s.strip()]
def _heuristic_summary(text: str, limit: int = 480) -> str:
sentences = _sentences(text)
hits = [s for s in sentences if re.search(r"\b(difficult|challenge|problem|issue|hard)\b", s, re.IGNORECASE)]
chosen = hits[:3] if hits else sentences[:3]
summary = " ".join(chosen).strip()
return summary[:limit] + ("" if len(summary) > limit else "")
def _heuristic_first_difficulty(text: str) -> str:
sentences = _sentences(text)
for s in sentences:
if re.search(r"\b(difficult|challenge|problem|issue|hard)\b", s, re.IGNORECASE):
return s
return sentences[0] if sentences else "No difficulty found in PDF text."
if CLIENT_MODE != "langchain":
return jsonify({"error": "PDF Q&A unavailable (no AI client configured)."}), 503
model_name = SMART_MODEL
system_prompt = pdf_qa_system_prompt()
user_prompt = f"Question: {question}\n\nPDF text:\n{context}"
try:
llm = get_chat_model(model_name, max_tokens=220)
if not llm:
return jsonify({"error": "PDF Q&A unavailable (no AI client configured)."}), 503
start = time.perf_counter()
response = llm.invoke(
to_lc_messages(
[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
]
)
)
elapsed = time.perf_counter() - start
content = response.content or ""
usage = getattr(response, "usage_metadata", None)
logger.info(
"[PDF-ANSWER] model=%s elapsed=%.2fs chars=%s usage=%s",
model_name,
elapsed,
len(str(content)),
usage,
)
answer = response.content
if not answer or not str(answer).strip():
answer = _heuristic_summary(context)
# If model parrots title/metadata, replace with heuristic
title_like = re.match(r"^why pdfs|^minimalist|^author:", answer.strip(), re.IGNORECASE) if answer else None
normalized_answer = re.sub(r"\s+", " ", answer or "").strip().lower()
normalized_context = re.sub(r"\s+", " ", context).strip().lower()
copied_context = bool(normalized_answer) and normalized_answer in normalized_context
if title_like or copied_context:
answer = _heuristic_summary(context)
return jsonify({"answer": answer, "mode": "model"})
except Exception as exc: # noqa: BLE001
logger.error("[PDF-ANSWER] model failed: %s", exc, exc_info=True)
answer = _heuristic_first_difficulty(context)
return jsonify({"answer": answer, "mode": "heuristic"})
@app.route("/api/generate", methods=["POST"])
def generate() -> Any:
"""Generate LaTeX + PDF in a single call."""
data = _json_body()
user_id = data.get("userId", "default_user")
prompt: str = data.get("prompt", "")
history: List[Dict[str, str]] = data.get("conversationHistory") or []
current_latex: str | None = data.get("currentLatex")
skip_template = bool(data.get("skipTemplate"))
force_new_document: Optional[bool] = data.get("forceNewDocument")
edit_mode = bool(current_latex) and not bool(force_new_document)
latex_source = current_latex if edit_mode else None
style_profile = update_style_profile_from_prompt(user_id, prompt)
doc_type = detect_document_type(prompt, latex_source or current_latex)
brief = gather_brief(doc_type, prompt, history)
if brief.get("needsInfo"):
logger.info("[REQ] brief incomplete doc_type=%s missing=%s", doc_type, brief.get("missing"))
return jsonify(
{
"needsInfo": True,
"message": brief.get("message"),
"missing": brief.get("missing", []),
"collected": brief.get("collected", {}),
"documentType": doc_type,
}
)
templates = load_user_templates(user_id)
template_hint = None if (skip_template or edit_mode) else templates.get(doc_type)
logger.info(
"[REQ] user=%s doc_type=%s template=%s history_len=%s current_latex=%s skip_template=%s edit_mode=%s",
user_id,
doc_type,
"yes" if template_hint else "no",
len(history),
bool(current_latex),
skip_template,
edit_mode,
)
mode = "live" if CLIENT_MODE == "langchain" else "mock"
latex_code_raw = generate_latex_with_llm(
prompt,
history,
style_profile,
doc_type,
template_hint,
latex_source,
brief.get("structured_brief"),
edit_mode=edit_mode,
)
latex_code = apply_style_overrides(clean_generated_latex(latex_code_raw), style_profile)
doc_type = detect_document_type(prompt, latex_code)
save_user_style(user_id, {"last_doc_type": doc_type})
if not skip_template and not edit_mode:
save_user_template(user_id, doc_type, latex_code)
elif skip_template:
logger.info("[TEMPLATE] skip flag set; not persisting template for %s", doc_type)
else:
logger.info("[TEMPLATE] edit mode active; not updating template for %s", doc_type)
template_used = bool(template_hint)
job_id = str(uuid.uuid4())
pdf_path = compile_latex_to_pdf(latex_code, job_id)
if pdf_path and os.path.exists(pdf_path):
pdf_url = f"/output/{job_id}.pdf"
version_entry = {
"id": job_id,
"prompt": prompt,
"documentType": doc_type,
"pdfUrl": pdf_url,
"latex": latex_code,
"createdAt": datetime.utcnow().isoformat() + "Z",
"styleProfile": style_profile,
"templateUsed": template_used,
"editMode": edit_mode,
}
save_version(user_id, version_entry)
logger.info("[OK] job_id=%s doc_type=%s pdf_url=%s", job_id, doc_type, pdf_url)
return jsonify(
{
"latex": latex_code,
"pdfUrl": pdf_url,
"documentType": doc_type,
"message": f"Generated {doc_type} successfully! mode={mode}",
"version": version_entry,
"styleProfile": style_profile,
"mode": mode,
"templateUsed": template_used,
"editingExisting": edit_mode,
}
)
logger.error("[FAIL] job_id=%s doc_type=%s compile_failed", job_id, doc_type)
return jsonify(
{
"error": "LaTeX compilation failed. Check logs.",
"latex": latex_code,
"mode": mode,
"editingExisting": edit_mode,
}
), 500
@app.route("/api/generate_stream", methods=["POST"])
def generate_stream() -> Any:
"""Stream LaTeX generation and compile PDFs incrementally."""
try:
data = _json_body()
user_id = data.get("userId", "default_user")
prompt: str = data.get("prompt", "")
history: List[Dict[str, str]] = data.get("conversationHistory") or []
current_latex: str | None = data.get("currentLatex")
skip_template = bool(data.get("skipTemplate"))
force_new_document: Optional[bool] = data.get("forceNewDocument")
edit_mode = bool(current_latex) and not bool(force_new_document)
latex_source = current_latex if edit_mode else None
style_profile = update_style_profile_from_prompt(user_id, prompt)
doc_type = detect_document_type(prompt, latex_source or current_latex)
brief = gather_brief(doc_type, prompt, history, current_latex, bool(current_latex))
logger.info(
"[STREAM] brief gate doc_type=%s needsInfo=%s missing=%s allowFabrication=%s",
doc_type,
brief.get("needsInfo"),
brief.get("missing"),
brief.get("allowFabrication"),
)
except Exception as exc: # noqa: BLE001
logger.error("[STREAM] Failed to prepare generation request: %s", exc, exc_info=True)
return jsonify({"error": "Failed to start generation", "detail": str(exc)}), 500
if brief.get("needsInfo"):
# Return a normal 200 with guidance so the assistant can ask follow-up questions
return jsonify(
{
"needsInfo": True,
"message": brief.get("message"),
"missing": brief.get("missing", []),
"collected": brief.get("collected", {}),
"documentType": doc_type,
"allowFabrication": brief.get("allowFabrication", False),
}
)
templates = load_user_templates(user_id)
template_hint = None if (skip_template or edit_mode) else templates.get(doc_type)
job_id = str(uuid.uuid4())
accumulated_latex = ""
last_compile_idx = 0
compile_interval = 250 # Compile every ~250 characters for faster previews
compile_time_budget = 2.0 # Or every ~2 seconds, whichever comes first
last_compile_time = time.perf_counter()
last_heartbeat_time = time.perf_counter()
stream_start = time.perf_counter()
first_chunk_time: Optional[float] = None
last_chunk_time: Optional[float] = None
total_chunk_chars = 0
total_chunks = 0
preview_executor = ThreadPoolExecutor(max_workers=2)
preview_tasks: Dict[str, Tuple[Any, int]] = {}
chunk_queue: "queue.Queue[Tuple[str, Optional[str]]]" = queue.Queue()
def stream_latex():
try:
for chunk in generate_latex_with_llm_stream(
prompt,
history,
style_profile,
doc_type,
template_hint,
latex_source,
brief.get("structured_brief"),
edit_mode=edit_mode,
):
chunk_queue.put(("chunk", chunk))
except Exception as exc:
logger.error("[STREAM] LLM streaming failed: %s", exc, exc_info=True)
chunk_queue.put(("error", str(exc)))
finally:
chunk_queue.put(("done", None))
def submit_preview(latex: str, progress: int) -> None:
if len(preview_tasks) >= PREVIEW_MAX_INFLIGHT:
return
preview_job_id = f"{job_id}-preview-{progress}"
def _run_compile() -> Optional[str]:
return compile_latex_to_pdf(latex, preview_job_id, log_errors=False)
fut = preview_executor.submit(_run_compile)
preview_tasks[preview_job_id] = (fut, progress)
def drain_previews():
nonlocal last_compile_idx, last_compile_time
completed = []
for pid, (fut, progress) in list(preview_tasks.items()):
if fut.done():
completed.append(pid)
try:
pdf_path = fut.result()
if pdf_path and os.path.exists(pdf_path):
pdf_url = f"/output/{pid}.pdf"
yield f"data: {json.dumps({'type': 'pdf_update', 'pdfUrl': pdf_url, 'progress': progress})}\n\n"
last_compile_idx = progress
last_compile_time = time.perf_counter()
except Exception as e: # noqa: BLE001
logger.error("[STREAM] Preview compile failed (async): %s", e, exc_info=True)
for pid in completed:
preview_tasks.pop(pid, None)
def generate():
nonlocal accumulated_latex, last_compile_time, last_heartbeat_time
nonlocal total_chunk_chars, total_chunks, first_chunk_time, last_chunk_time
# Send initial metadata
yield f"data: {json.dumps({'type': 'start', 'jobId': job_id, 'documentType': doc_type, 'editingExisting': edit_mode})}\n\n"
streamer = threading.Thread(target=stream_latex, daemon=True)
streamer.start()
try:
while True:
try:
item_type, payload = chunk_queue.get(timeout=0.5)
except queue.Empty:
now = time.perf_counter()
if now - last_heartbeat_time >= 1.0:
yield f"data: {json.dumps({'type': 'heartbeat', 'ts': now})}\n\n"
last_heartbeat_time = now
yield from drain_previews()
continue
if item_type == "chunk":
chunk = payload or ""
accumulated_latex += chunk
total_chunks += 1
total_chunk_chars += len(chunk)
last_chunk_time = time.perf_counter()
if first_chunk_time is None:
first_chunk_time = last_chunk_time
yield f"data: {json.dumps({'type': 'latex_chunk', 'chunk': chunk, 'accumulated': accumulated_latex})}\n\n"
elapsed = time.perf_counter() - last_compile_time
if (len(accumulated_latex) - last_compile_idx >= compile_interval) or elapsed >= compile_time_budget:
compile_latex = accumulated_latex
if "\\begin{document}" in compile_latex and "\\end{document}" not in compile_latex:
if compile_latex.count("\\begin{") > compile_latex.count("\\end{"):
temp_latex = compile_latex
last_begin = compile_latex.rfind("\\begin{")
if last_begin != -1:
env_start = last_begin + len("\\begin{")
env_end = compile_latex.find("}", env_start)
if env_end != -1:
env_name = compile_latex[env_start:env_end]
temp_latex += f"\\end{{{env_name}}}\n"
temp_latex += "\\end{document}\n"
compile_latex = temp_latex
else:
compile_latex += "\\end{document}\n"
if "\\begin{document}" in compile_latex and "\\end{document}" in compile_latex:
submit_preview(compile_latex, len(accumulated_latex))
now = time.perf_counter()
if now - last_heartbeat_time >= 1.0:
yield f"data: {json.dumps({'type': 'heartbeat', 'ts': now})}\n\n"
last_heartbeat_time = now
yield from drain_previews()
elif item_type == "error":
message = payload or "Streaming failed"
yield f"data: {json.dumps({'type': 'error', 'message': message})}\n\n"
break
elif item_type == "done":
break
stream_end = time.perf_counter()
if first_chunk_time is None:
logger.info(
"[STREAM] LLM finished with no chunks job_id=%s elapsed=%.2fs",
job_id,
stream_end - stream_start,
)
else:
logger.info(
"[STREAM] LLM stats job_id=%s chunks=%s chars=%s first_chunk=%.2fs last_chunk=%.2fs elapsed=%.2fs",
job_id,
total_chunks,
total_chunk_chars,
first_chunk_time - stream_start,
(last_chunk_time or stream_end) - stream_start,
stream_end - stream_start,
)
# Final compilation with complete LaTeX
latex_code = apply_style_overrides(clean_generated_latex(accumulated_latex), style_profile)
final_doc_type = detect_document_type(prompt, latex_code)
save_user_style(user_id, {"last_doc_type": final_doc_type})
if not skip_template and not edit_mode:
save_user_template(user_id, final_doc_type, latex_code)
elif skip_template:
logger.info("[TEMPLATE] skip flag set; not persisting template for %s", final_doc_type)
else:
logger.info("[TEMPLATE] edit mode active; not updating template for %s", final_doc_type)
template_used = bool(template_hint)
pdf_path = compile_latex_to_pdf(latex_code, job_id)
if pdf_path and os.path.exists(pdf_path):
pdf_url = f"/output/{job_id}.pdf"
version_entry = {
"id": job_id,
"prompt": prompt,
"documentType": final_doc_type,
"pdfUrl": pdf_url,
"latex": latex_code,
"createdAt": datetime.utcnow().isoformat() + "Z",
"styleProfile": style_profile,
"templateUsed": template_used,
"editMode": edit_mode,
}
save_version(user_id, version_entry)
yield f"data: {json.dumps({'type': 'complete', 'pdfUrl': pdf_url, 'latex': latex_code, 'version': version_entry, 'documentType': final_doc_type, 'templateUsed': template_used, 'styleProfile': style_profile, 'editingExisting': edit_mode})}\n\n"
else:
logger.error("[STREAM] Final PDF compilation failed for job_id=%s", job_id)
yield f"data: {json.dumps({'type': 'error', 'message': 'Final PDF compilation failed'})}\n\n"
except Exception as e: # noqa: BLE001
logger.error("[STREAM] Generation error job_id=%s: %s", job_id, e, exc_info=True)
yield f"data: {json.dumps({'type': 'error', 'message': str(e)})}\n\n"
finally:
try:
preview_executor.shutdown(wait=False, cancel_futures=True)
except Exception:
pass
try:
return Response(
stream_with_context(generate()),
mimetype="text/event-stream",
headers={
"Cache-Control": "no-cache",
"X-Accel-Buffering": "no",
},
)
except Exception as exc: # noqa: BLE001
logger.error("[STREAM] Failed to start response: %s", exc, exc_info=True)
return jsonify({"error": "Unable to start streaming response", "detail": str(exc)}), 500
@app.route("/api/create/sessions/<session_id>/stream", methods=["GET"])
def create_stream(session_id: str) -> Any:
disabled = _require_ai_enabled()
if disabled:
return disabled
phase = (request.args.get("phase") or "outline").strip().lower()
try:
session = _fetch_ai_session(session_id)
except Exception: # noqa: BLE001
return jsonify({"error": "Session not found"}), 404
user_id = session.get("userId", "default_user")
prompt = session.get("promptLatest") or session.get("promptInitial") or ""
doc_type = session.get("docType") or detect_document_type(prompt, None)
template_id = session.get("templateId")
outline_text = session.get("outlineText") or ""
constraints = session.get("outlineConstraints")
if isinstance(constraints, str) and constraints.strip():
try:
constraints = json.loads(constraints)
except json.JSONDecodeError:
constraints = None
draft_sections_raw = session.get("draftSections")
draft_sections = None
if isinstance(draft_sections_raw, list):
draft_sections = draft_sections_raw
elif isinstance(draft_sections_raw, str) and draft_sections_raw.strip():
try:
draft_sections = json.loads(draft_sections_raw)
except json.JSONDecodeError:
draft_sections = None
style_profile = load_user_style(user_id)
def sse(data: Dict[str, Any]) -> str:
return f"data: {json.dumps(data)}\n\n"
def generate():
yield sse({"type": "phase_changed", "phase": phase})
if phase == "outline":
outline = generate_outline_with_llm(prompt, doc_type, constraints)
_update_ai_session(
session_id,
{
"outlineText": outline,
"outlineConstraints": json.dumps(constraints, ensure_ascii=True) if constraints else None,
"docType": doc_type,
"status": "OUTLINE_PENDING",
},
)
yield sse({"type": "outline_ready", "outlineText": outline})
yield sse({"type": "phase_complete", "phase": "outline"})
return
if phase == "draft":
base_outline = outline_text or prompt
sections = generate_section_draft(prompt, doc_type, base_outline, constraints)
_update_ai_session(
session_id,
{
"draftSections": json.dumps(sections, ensure_ascii=True),
"outlineConstraints": json.dumps(constraints, ensure_ascii=True) if constraints else None,
"docType": doc_type,
"status": "DRAFT_READY",
},
)
yield sse({"type": "draft_sections", "sections": sections})
yield sse({"type": "phase_complete", "phase": "draft", "sections": sections})
return
if phase == "polish":
accumulated = ""
template_latex = _select_template(doc_type, template_id)
if template_latex:
for chunk in generate_template_fill_stream(
template_latex,
doc_type,
outline_text or prompt,
draft_sections=draft_sections,
constraints=constraints,
style_profile=style_profile,
):
accumulated += chunk
yield sse({"type": "latex_delta", "phase": "polish", "delta": chunk})
else:
section_text = ""
if draft_sections:
section_text = "\n".join(
f"{section.get('label', 'Section')}: {section.get('value', '')}"
for section in draft_sections
)
constraint_text = ""
if constraints:
tone = constraints.get("tone")
audience = constraints.get("audience")
pages = constraints.get("pageCount")
constraint_text = f"Tone: {tone}. Audience: {audience}. Target pages: {pages}."
polish_prompt = (
f"Create a polished LaTeX document for a {doc_type}.\n"
"Use the provided section content and keep the substance consistent.\n"
f"{constraint_text}\n"
"Keep the final document within the target page count.\n"
)
for chunk in generate_latex_with_llm_stream(
polish_prompt,
[],
style_profile,
doc_type,
None,
None,
section_text or outline_text or prompt,
edit_mode=True,
):
accumulated += chunk
yield sse({"type": "latex_delta", "phase": "polish", "delta": chunk})
accumulated = apply_style_overrides(accumulated, style_profile)
_update_ai_session(
session_id,
{"polishedLatex": accumulated, "docType": doc_type, "status": "POLISHED_READY"},
)
pdf_job_id = f"{session_id}-polished"
pdf_path = compile_latex_to_pdf(accumulated, pdf_job_id, log_errors=False)
if pdf_path and os.path.exists(pdf_path):
pdf_url = f"/output/{pdf_job_id}.pdf"
yield sse({"type": "save_complete", "docId": session_id, "pdfUrl": pdf_url})
yield sse({"type": "phase_complete", "phase": "polish", "latex": accumulated})
return
yield sse({"type": "error", "message": f"Unknown phase: {phase}"})
return Response(
stream_with_context(generate()),
mimetype="text/event-stream",
headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
)
@app.route("/api/create/sessions/<session_id>/fields", methods=["POST"])
def fill_fields(session_id: str) -> Any:
disabled = _require_ai_enabled()
if disabled:
return disabled
try:
logger.info("[AI create] fill_fields session_id=%s", session_id)
session = _fetch_ai_session(session_id)
except Exception as exc: # noqa: BLE001
logger.warning("[AI create] fill_fields session lookup failed session_id=%s error=%s", session_id, exc)
return jsonify({"error": "Session not found"}), 404
data = _json_body()
fields = data.get("fields") or []
extra_prompt = data.get("extraPrompt") or ""
if not isinstance(fields, list):
return jsonify({"error": "Fields must be a list"}), 400
prompt = session.get("promptLatest") or session.get("promptInitial") or ""
if extra_prompt:
prompt = f"{prompt}\n{extra_prompt}"
doc_type = session.get("docType") or detect_document_type(prompt, None)
constraints = session.get("outlineConstraints")
if isinstance(constraints, str) and constraints.strip():
try:
constraints = json.loads(constraints)
except json.JSONDecodeError:
constraints = None
filled = generate_field_values(prompt, doc_type, fields, constraints)
return jsonify({"fields": filled})
@app.route("/api/progressive_render", methods=["POST"])
def progressive_render() -> Any:
"""Compile arbitrary LaTeX (partial or masked) for progressive previews."""
data = _json_body()
latex = data.get("latex")
if not latex or not isinstance(latex, str):
return jsonify({"error": "Missing LaTeX payload"}), 400
job_id = data.get("jobId") or str(uuid.uuid4())
pdf_path = compile_latex_to_pdf(latex, job_id)
if pdf_path and os.path.exists(pdf_path):
return jsonify({"pdfUrl": f"/output/{job_id}.pdf"})
return jsonify({"error": "Progressive compilation failed"}), 500
@app.route("/output/<path:filename>", methods=["GET"])
def serve_output_file(filename: str) -> Any:
"""Serve generated PDF files and stored assets."""
file_path = os.path.join(OUTPUT_DIR, filename)
if os.path.exists(file_path):
mime_type, _ = mimetypes.guess_type(file_path)
return send_file(file_path, mimetype=mime_type or "application/octet-stream")
return jsonify({"error": "File not found"}), 404
@app.route("/api/versions/<user_id>", methods=["GET"])
def list_versions(user_id: str) -> Any:
return jsonify({"versions": load_versions(user_id)})
@app.route("/api/style/<user_id>", methods=["GET"])
def get_style(user_id: str) -> Any:
return jsonify({"style": load_user_style(user_id)})
@app.route("/api/style/<user_id>", methods=["POST"])
def update_style(user_id: str) -> Any:
data = _json_body()
if not isinstance(data, dict):
return jsonify({"error": "Style payload must be an object"}), 400
current = load_user_style(user_id) or {}
merged = {**current, **data}
save_user_style(user_id, merged)
return jsonify({"style": merged})
@app.route("/api/style/apply", methods=["POST"])
def apply_style() -> Any:
data = _json_body()
latex = data.get("latex")
style = data.get("style") or {}
if not latex or not isinstance(latex, str):
return jsonify({"error": "Missing LaTeX payload"}), 400
if not isinstance(style, dict):
return jsonify({"error": "Style payload must be an object"}), 400
updated = apply_style_overrides(latex, style)
return jsonify({"latex": updated})
@app.route("/api/import_template", methods=["POST"])
def import_template() -> Any:
"""Accept a PDF upload, extract layout via vision model, and save as a template."""
user_id = request.form.get("userId", "default_user")
doc_type = request.form.get("docType", "document")
file = request.files.get("file")
if not file:
return jsonify({"error": "No file uploaded"}), 400
pdf_bytes = file.read()
images = render_pdf_to_images(pdf_bytes, max_pages=2, dpi=170)
if not images:
return jsonify({"error": "Failed to render PDF"}), 400
layout_latex = vision_layout_from_images(images, doc_type) or ""
if not layout_latex:
layout_latex = f"""\\documentclass{{article}}
\\usepackage[margin=1in]{{geometry}}
\\usepackage{{tabularx}}
\\usepackage{{multicol}}
\\begin{{document}}
% Fallback template for {doc_type}
\\section*{{Title placeholder}}
Body text goes here.
\\end{{document}}
"""
sanitized = clean_generated_latex(layout_latex)
save_user_template(user_id, doc_type, sanitized)
return jsonify({"message": "Template imported", "docType": doc_type, "pages": len(images)})
@app.route("/api/assets/upload", methods=["POST"])
def upload_asset() -> Any:
file = request.files.get("file")
if not file:
return jsonify({"error": "Missing file"}), 400
_, ext = os.path.splitext(file.filename or "")
ext = ext.lower()
if ext not in {".png", ".jpg", ".jpeg", ".gif"}:
return jsonify({"error": "Unsupported file type"}), 400
asset_id = f"{uuid.uuid4().hex}{ext}"
output_path = os.path.join(ASSETS_DIR, asset_id)
os.makedirs(ASSETS_DIR, exist_ok=True)
file.save(output_path)
return jsonify(
{
"assetId": asset_id,
"assetUrl": f"/output/assets/{asset_id}",
"latexPath": f"assets/{asset_id}",
}
)
@app.route("/api/pdf-editor/document", methods=["GET"])
def pdf_editor_document() -> Any:
"""Expose a JSON snapshot of the PDF for rich text editing."""
pdf_url = request.args.get("pdfUrl")
if not pdf_url:
return jsonify({"error": "Missing pdfUrl"}), 400
filename = os.path.basename(pdf_url.split("?")[0])
if not filename:
return jsonify({"error": "Invalid pdf file"}), 400
if not filename.lower().endswith(".pdf"):
return jsonify({"error": "Invalid pdf file"}), 400
pdf_path = os.path.join(OUTPUT_DIR, filename)
if not os.path.exists(pdf_path):
return jsonify({"error": "PDF not found"}), 404
try:
document = convert_pdf_to_text_editor_document(pdf_path)
return jsonify(document)
except FileNotFoundError:
return jsonify({"error": "Conversion failed"}), 500
except subprocess.CalledProcessError as exc:
logger.error("[PDF-EDITOR] Conversion failed: %s", exc)
return jsonify({"error": "Conversion failed"}), 500
except Exception as exc: # noqa: BLE001
logger.error("[PDF-EDITOR] Unexpected conversion failure: %s", exc)
return jsonify({"error": "Conversion failed"}), 500
@app.route("/api/pdf-editor/upload", methods=["POST"])
def pdf_editor_upload() -> Any:
"""Accept an edited PDF and save it so the preview can refresh."""
file = request.files.get("file")
if not file:
return jsonify({"error": "Missing file"}), 400
job_id = str(uuid.uuid4())
filename = f"{job_id}-edited.pdf"
output_path = os.path.join(OUTPUT_DIR, filename)
os.makedirs(OUTPUT_DIR, exist_ok=True)
file.save(output_path)
logger.info("[PDF-EDITOR] uploaded edited PDF job_id=%s -> %s", job_id, filename)
return jsonify({"pdfUrl": f"/output/{filename}"})
@app.route("/health", methods=["GET"])
def health() -> Any:
return jsonify({"status": "ok", "engine": "pdflatex"})
if __name__ == "__main__":
app.run(host="0.0.0.0", port=5000, debug=True)
@@ -1,525 +0,0 @@
from __future__ import annotations
import re
import json
from typing import Any, Dict, List, Optional
import time
from config import CLIENT_MODE, FAST_MODEL, SMART_MODEL, get_chat_model, logger
from langchain_utils import to_lc_messages
from prompts import brief_missing_info_system_prompt
BRIEF_SCHEMAS: Dict[str, Dict[str, Any]] = {
"resume": {
"field_order": [
"name",
"contact",
"location",
"target_role",
"summary",
"work_history",
"education",
"skills",
"achievements",
"links",
"constraints",
],
"labels": {
"name": ["name", "full name"],
"contact": ["contact", "contact info", "contact information", "email/phone"],
"location": ["location", "city/country"],
"target_role": ["target role", "role", "title", "headline"],
"summary": ["summary", "objective", "about"],
"work_history": ["experience", "work history", "roles"],
"education": ["education", "studies"],
"skills": ["skills", "stack"],
"achievements": ["achievements", "certifications", "awards"],
"links": ["links", "profiles", "linkedin/github"],
"constraints": ["constraints", "tone/length/style"],
},
"questions": {
"name": "What's your name as you'd like it on the page?",
"contact": "How can someone reach you (email/phone)?",
"location": "Where are you based (or remote)?",
"target_role": "What role/title and industry are you aiming for?",
"summary": "Give me a 12 sentence summary about you.",
"work_history": "Recent roles: company, title, dates, location, and a few bullets with impact.",
"education": "Degree(s), school, and graduation year?",
"skills": "Key skills/stack (tech + relevant soft skills)?",
"achievements": "Awards/certifications/major achievements?",
"links": "Any LinkedIn/GitHub/portfolio links?",
"constraints": "Any tone/length constraints (ATS, one-page, etc.)?",
},
"intro": "Hey! To build a strong resume, you can paste your old resume or just dump everything you remember—name, how to reach you, where you're based, what you're aiming for, your roles, education, skills, links. Share whatever you have and I'll work with it.",
},
"invoice": {
"field_order": [
"your_business",
"client",
"issue_date",
"due_date",
"line_items",
"currency",
"payment_terms",
"notes",
"constraints",
],
"labels": {
"your_business": ["your business", "seller", "from"],
"client": ["client", "bill to"],
"issue_date": ["issue date", "invoice date"],
"due_date": ["due date"],
"line_items": ["line items", "services/items"],
"currency": ["currency"],
"payment_terms": ["payment terms"],
"notes": ["notes"],
"constraints": ["constraints", "layout/style"],
},
"questions": {
"your_business": "Who is issuing the invoice (business name + contact)?",
"client": "Who is being billed (name + contact)?",
"issue_date": "Invoice issue date?",
"due_date": "Due date?",
"line_items": "Line items with description, qty, rate, tax (if any)?",
"currency": "Currency?",
"payment_terms": "Payment terms and payment methods?",
"notes": "Notes to include (late fees, thank you, PO #)?",
"constraints": "Branding/layout preferences?",
},
"intro": "I'll draft an accurate invoice if I know who is billing, who is paying, and the line items. Paste an old invoice or list the details.",
},
}
def classify_intent_with_llm(prompt: str, history: List[Dict[str, str]], current_latex: Optional[str], has_pdf: bool) -> Optional[Dict[str, Any]]:
"""
Use a small model to classify intent instead of brittle regex.
Returns a dict like:
{
"documentType": "invoice|resume|contract|letter|report|form|document",
"action": "new|edit|question",
"allowFabrication": bool,
"wantsPdf": bool,
"hasEnoughInfo": bool,
"missingFields": [str],
"notes": str
}
"""
if CLIENT_MODE != "langchain":
logger.info("[INTENT] skip llm classify: client_mode=%s", CLIENT_MODE)
return None
system = (
"You classify user requests about documents. "
"Output strict JSON. "
"documentType must be one of: academic, agenda, brochure, business_card, case_study, checklist, "
"contract, creative, datasheet, document, flyer, invoice, letter, manual, menu, minutes, newsletter, "
"one_pager, poster, presentation, press_release, proposal, recipe, report, resume, timeline, whitepaper. "
"action: 'new' (make/generate), 'edit' (modify existing), 'question' (asking about it). "
"allowFabrication: true if the user invites making up/placeholder/dummy/random details "
"OR asks you to use your knowledge about a fictional/real character (e.g., 'use what you know about James Bond', "
"'make it for agent 007', 'create resume for Sherlock Holmes', etc.). "
"Basically, if they're NOT providing their own personal details and expect you to fill in from common knowledge or imagination, set this to true. "
"wantsPdf: true if they expect/gave permission to generate a PDF. "
"hasEnoughInfo: true if there is enough info to proceed without asking questions (or if allowFabrication is true). "
"missingFields: key details still needed (e.g., for invoice: seller, client, line items; resume: name, contact, work). "
"notes: short free-form note."
)
conversation = [{"role": "system", "content": system}]
# Trim history to keep request small
trimmed_history = history[-6:] if len(history) > 6 else history
for msg in trimmed_history:
if msg.get("content") and msg.get("role") in {"user", "assistant", "system"}:
conversation.append({"role": msg["role"], "content": msg["content"]})
conversation.append({"role": "user", "content": prompt})
try:
llm = get_chat_model(
FAST_MODEL or SMART_MODEL,
max_tokens=800,
model_kwargs={"response_format": {"type": "json_object"}},
)
if not llm:
logger.info("[INTENT] skip llm classify: no LangChain client")
return None
start = time.perf_counter()
response = llm.invoke(to_lc_messages(conversation))
elapsed = time.perf_counter() - start
content = response.content or ""
usage = getattr(response, "usage_metadata", None)
logger.info(
"[INTENT] llm_classify model=%s elapsed=%.2fs chars=%s usage=%s",
FAST_MODEL or SMART_MODEL,
elapsed,
len(str(content)),
usage,
)
content = response.content
if not content:
logger.info("[INTENT] llm_classify empty content")
return None
data = json.loads(content)
# Normalize
doc_type = str(data.get("documentType") or "document").lower()
allowed_types = {
"academic", "agenda", "brochure", "business_card", "case_study", "checklist",
"contract", "creative", "datasheet", "document", "flyer", "invoice", "letter",
"manual", "menu", "minutes", "newsletter", "one_pager", "poster", "presentation",
"press_release", "proposal", "recipe", "report", "resume", "timeline", "whitepaper"
}
if doc_type not in allowed_types:
doc_type = "document"
action = str(data.get("action") or "new").lower()
if action not in {"new", "edit", "question"}:
action = "new"
result = {
"documentType": doc_type,
"action": action,
"allowFabrication": bool(data.get("allowFabrication")),
"wantsPdf": bool(data.get("wantsPdf", True)),
"hasEnoughInfo": bool(data.get("hasEnoughInfo", True)),
"missingFields": data.get("missingFields") or [],
"notes": data.get("notes") or "",
}
logger.info(
"[INTENT] llm_classify doc_type=%s action=%s allowFabrication=%s wantsPdf=%s hasEnoughInfo=%s missing=%s notes=%s",
result["documentType"],
result["action"],
result["allowFabrication"],
result["wantsPdf"],
result["hasEnoughInfo"],
result["missingFields"],
(result["notes"] or "")[:120],
)
return result
except Exception as exc: # noqa: BLE001
logger.error("[INTENT] LLM classify failed: %s", exc, exc_info=True)
return None
def detect_fabrication_opt_in(prompt: str, history: List[Dict[str, str]]) -> bool:
"""
Ask a small model to decide if the user has permitted invention of missing details.
Returns True when the user says to make things up / whatever is fine /
no preference, even if they haven't provided concrete fields.
"""
if CLIENT_MODE != "langchain":
return False
system = (
"Decide if the user has explicitly permitted you to invent or make up missing details. "
"Reply with strict JSON: {\"allowFabrication\": true|false}. "
"Consider any user instruction like 'make it up', 'whatever you want', 'use dummy info', "
"'fabricate the rest', 'fill in anything' as permission. "
"Do not require specific keywords; infer intent from the conversation. "
"If unclear, set allowFabrication to false."
)
conversation = [{"role": "system", "content": system}]
trimmed_history = history[-8:] if len(history) > 8 else history
for msg in trimmed_history:
if msg.get("content") and msg.get("role") in {"user", "assistant", "system"}:
conversation.append({"role": msg["role"], "content": msg["content"]})
conversation.append({"role": "user", "content": prompt})
try:
llm = get_chat_model(
FAST_MODEL or SMART_MODEL,
max_tokens=100,
model_kwargs={"response_format": {"type": "json_object"}},
)
if not llm:
return False
start = time.perf_counter()
response = llm.invoke(to_lc_messages(conversation))
elapsed = time.perf_counter() - start
content = response.content or ""
usage = getattr(response, "usage_metadata", None)
logger.info(
"[INTENT] fabrication-check model=%s elapsed=%.2fs chars=%s usage=%s",
FAST_MODEL or SMART_MODEL,
elapsed,
len(str(content)),
usage,
)
content = response.content
if not content:
return False
data = json.loads(content)
return bool(data.get("allowFabrication"))
except Exception as exc: # noqa: BLE001
logger.error("[INTENT] fabrication opt-in check failed: %s", exc)
return False
def _preprocess_intent(
prompt: str,
history: List[Dict[str, str]],
has_pdf: bool,
current_latex: Optional[str],
) -> Dict[str, Any]:
"""
Lightweight intent classifier used by /api/intent/check.
It is intentionally heuristic-only to avoid extra model calls. The goal is to
decide whether we should proceed with PDF generation and whether it's OK to
fabricate placeholder content when the user explicitly asks for it.
"""
user_texts = [entry.get("content", "") for entry in history if entry.get("role") == "user"]
user_texts.append(prompt or "")
combined_text = " ".join([t for t in user_texts if t]).strip().lower()
llm = classify_intent_with_llm(prompt, history, current_latex, has_pdf)
if llm:
return {
"wants_pdf": llm.get("wantsPdf", True),
"has_enough_info": llm.get("hasEnoughInfo", True),
"allow_makeup": llm.get("allowFabrication", False),
"document_type": llm.get("documentType"),
"missing_fields": llm.get("missingFields", []),
}
# Fallback heuristics (only if no LLM)
avoid_pdf = bool(re.search(r"\b(no pdf|text only|markdown only|dont (make|generate) pdf)\b", combined_text))
wants_pdf = not avoid_pdf or bool(current_latex) or has_pdf
has_meaningful_text = len(combined_text) > 20
return {
"wants_pdf": wants_pdf,
"has_enough_info": bool(current_latex or has_pdf or has_meaningful_text),
"allow_makeup": False,
"document_type": None,
"missing_fields": [],
}
def _extract_structured_fields(text: str, schema: Dict[str, Any]) -> Dict[str, str]:
"""Naively parse user text to pull schema fields."""
found: Dict[str, str] = {}
lower = text.lower()
if schema.get("field_order") == BRIEF_SCHEMAS["resume"]["field_order"]:
work_matches = re.findall(
r"(?:experience|work history|role|company|position)\s*[:\-]\s*(.+?)(?=\n\n|\Z)",
text,
flags=re.IGNORECASE | re.DOTALL,
)
if work_matches:
found["work_history"] = "\n".join(work_matches[:3])
education_matches = re.findall(
r"(?:education|degree)\s*[:\-]\s*(.+?)(?=\n\n|\Z)",
text,
flags=re.IGNORECASE | re.DOTALL,
)
if education_matches:
found["education"] = "\n".join(education_matches[:2])
skills_match = re.search(r"(?:skills|stack)\s*[:\-]\s*(.+)", text, flags=re.IGNORECASE)
if skills_match:
found["skills"] = skills_match.group(1).strip()
for field, labels in schema.get("labels", {}).items():
for label in labels:
pattern = rf"{label}\s*[:\-]\s*(.+?)(?=\n[A-Z][a-zA-Z ]+[:\-]|\Z)"
match = re.search(pattern, text, flags=re.IGNORECASE | re.DOTALL)
if match:
found[field] = match.group(1).strip()
break
if not found.get("summary") and len(lower) < 200:
found["summary"] = text.strip()
return {k: v for k, v in found.items() if v}
def _format_missing_message(
doc_type: str,
schema: Dict[str, Any],
collected: Dict[str, str],
missing: List[str],
preface: Optional[str] = None,
) -> str:
"""Fallback text asking the user for missing fields."""
intro = schema.get("intro") or f"Need a few details to finish your {doc_type}."
lines = [intro]
if preface:
lines.append(preface)
if collected:
lines.append("Already have:")
for field, value in collected.items():
label = schema.get("labels", {}).get(field, [field])[0]
lines.append(f"- {label}: {value}")
if missing:
lines.append("Still need:")
questions = schema.get("questions", {})
for field in missing[:4]:
ask = questions.get(field) or f"{field}?"
lines.append(f"- {ask}")
lines.append("Partial info is fine—share whatever you remember.")
return "\n".join(lines)
def _ai_missing_message(
doc_type: str,
schema: Dict[str, Any],
collected: Dict[str, str],
missing: List[str],
) -> Optional[str]:
"""Let the model craft clarifying questions when available."""
if CLIENT_MODE != "langchain" or not missing:
return None
collected_lines = [f"- {schema.get('labels', {}).get(field, [field])[0]}: {value}" for field, value in collected.items()]
missing_labels = [schema.get("labels", {}).get(field, [field])[0] for field in missing]
user_text = "We already have:\n" + "\n".join(collected_lines) if collected_lines else "We have nothing yet."
user_text += "\nNeed to ask for: " + ", ".join(missing_labels)
if not collected_lines:
user_text += "\nInvite them to paste an old resume or dump all details if they have them."
system_prompt = brief_missing_info_system_prompt(doc_type)
try:
llm = get_chat_model(SMART_MODEL, max_tokens=400)
if not llm:
return None
start = time.perf_counter()
response = llm.invoke(
to_lc_messages(
[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_text},
]
)
)
elapsed = time.perf_counter() - start
content = response.content or ""
usage = getattr(response, "usage_metadata", None)
logger.info(
"[AI] missing-questions model=%s elapsed=%.2fs chars=%s usage=%s",
SMART_MODEL,
elapsed,
len(str(content)),
usage,
)
return response.content
except Exception as exc:
logger.error("[AI] missing-questions failed: %s", exc)
return None
def gather_brief(doc_type: str, prompt: str, history: List[Dict[str, str]], current_latex: Optional[str] = None, has_pdf: bool = False) -> Dict[str, Any]:
"""
Determine whether we have enough structured details to generate without fabricating.
Returns needsInfo + a formatted message when details are missing, or a structured brief.
"""
classifier = classify_intent_with_llm(prompt, history, current_latex, has_pdf)
if classifier:
doc_type = classifier.get("documentType", doc_type)
logger.info(
"[BRIEF] using llm doc_type=%s allowFabrication=%s missing=%s hasEnoughInfo=%s wantsPdf=%s",
doc_type,
classifier.get("allowFabrication"),
classifier.get("missingFields"),
classifier.get("hasEnoughInfo"),
classifier.get("wantsPdf"),
)
else:
logger.info("[BRIEF] llm classifier unavailable, using fallback schema doc_type=%s", doc_type)
schema = BRIEF_SCHEMAS.get(doc_type)
if not schema:
return {"needsInfo": False, "structured_brief": None, "collected": {}, "missing": []}
user_texts = [entry.get("content", "") for entry in history if entry.get("role") == "user"]
user_texts.append(prompt or "")
combined_text = "\n".join(user_texts)
collected = _extract_structured_fields(combined_text, schema)
missing = [field for field in schema.get("field_order", []) if field not in collected]
classifier_has_enough = bool(classifier.get("hasEnoughInfo")) if classifier else True
allow_makeup = bool(classifier.get("allowFabrication")) if classifier else False
if not allow_makeup:
allow_makeup = detect_fabrication_opt_in(prompt, history)
if classifier and classifier.get("missingFields"):
# If the model provided missing fields, respect that list.
missing = classifier.get("missingFields") or missing
# If the user gave no usable content, do not allow fabrication shortcuts.
# Force the flow to ask for the required fields instead of silently proceeding.
missing_all_fields = len(missing) == len(schema.get("field_order", []))
low_signal_request = not collected and len(combined_text.strip()) < 12
if missing_all_fields and low_signal_request:
allow_makeup = False
def has_minimum_resume(data: Dict[str, str]) -> bool:
has_name = bool(data.get("name"))
has_core = any(data.get(key) for key in ["work_history", "education", "skills", "contact", "target_role"])
return has_name and has_core
def has_minimum_invoice(data: Dict[str, str]) -> bool:
has_parties = data.get("your_business") and data.get("client")
has_items = bool(data.get("line_items"))
return bool(has_parties and has_items)
has_minimum = True
if doc_type == "resume":
has_minimum = has_minimum_resume(collected)
elif doc_type == "invoice":
has_minimum = has_minimum_invoice(collected)
# Decide if we must pause to ask for details. Avoid regex "ready" guesses;
# rely on the classifier's allowFabrication flag and collected data.
must_ask_first = bool(missing) and ((not allow_makeup and not has_minimum) or not classifier_has_enough)
if must_ask_first:
preface = None
if doc_type == "resume" and not has_minimum:
preface = (
"I only have a tiny bit so far. I need at least your name plus one of: contact, "
"a role snippet, education, skills, or target role."
)
if doc_type == "invoice" and not has_minimum:
preface = "Need who is billing, who is paying, and the line items so I don't invent details."
logger.info(
"[BRIEF] gating doc_type=%s missing=%s has_minimum=%s allowFabrication=%s",
doc_type,
missing,
has_minimum,
allow_makeup,
)
message = _ai_missing_message(doc_type, schema, collected, missing) or _format_missing_message(
doc_type, schema, collected, missing, preface=preface
)
message += "\nIf you'd like me to invent anything you didn't share, just say so."
return {
"needsInfo": True,
"message": message,
"collected": collected,
"missing": missing,
"allowFabrication": allow_makeup,
}
# If fabrication is allowed but fields are missing, let downstream generation
# know which areas to fill in plausibly.
fabrication_hint = ""
if allow_makeup and missing:
fabrication_hint = "\n\nIf details are absent, invent plausible, clearly fictional details for: " + ", ".join(missing) + "."
structured_lines = []
for field in schema.get("field_order", []):
value = collected.get(field)
if value:
label = schema.get("labels", {}).get(field, [field])[0]
structured_lines.append(f"{label}: {value}")
structured_brief = "\n".join(structured_lines)
if fabrication_hint:
structured_brief = (structured_brief + fabrication_hint).strip()
if combined_text.strip():
structured_brief = (structured_brief + "\n\nRaw user notes:\n" + combined_text).strip()
return {
"needsInfo": False,
"structured_brief": structured_brief or None,
"collected": collected,
"missing": missing,
"allowFabrication": allow_makeup,
}
__all__ = ["gather_brief", "BRIEF_SCHEMAS", "_preprocess_intent"]
@@ -1,94 +0,0 @@
import logging
import os
from typing import Optional
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
OUTPUT_DIR = os.path.join(BASE_DIR, "output")
ASSETS_DIR = os.path.join(OUTPUT_DIR, "assets")
DATA_DIR = os.path.join(BASE_DIR, "data")
TEMPLATE_DIR = os.path.join(BASE_DIR, "templates")
STYLE_DB_PATH = os.path.join(DATA_DIR, "user_styles.json")
TEMPLATE_DB_PATH = os.path.join(DATA_DIR, "user_templates.json")
VERSIONS_DB_PATH = os.path.join(DATA_DIR, "versions.json")
os.makedirs(OUTPUT_DIR, exist_ok=True)
os.makedirs(ASSETS_DIR, exist_ok=True)
os.makedirs(DATA_DIR, exist_ok=True)
os.makedirs(TEMPLATE_DIR, exist_ok=True)
OPENAI_API_KEY = os.environ.get("OPENAI_API_KEY")
OPENAI_BASE_URL = os.environ.get("OPENAI_BASE_URL")
JAVA_BACKEND_URL = os.environ.get("JAVA_BACKEND_URL", "http://localhost:8080")
if not OPENAI_API_KEY:
raise RuntimeError("OPENAI_API_KEY is required to start the AI backend.")
# Default to GPT-5.1 for full document generation (smart model).
# Allow override via SMART_MODEL or legacy OPENAI_MODEL.
SMART_MODEL = os.environ.get("SMART_MODEL") or os.environ.get("OPENAI_MODEL") or "gpt-5.1"
# Default to the nano/ultra-fast tier for intent/pre checks (fast model).
# Allow override via FAST_MODEL or legacy FAST_INTENT_MODEL.
FAST_MODEL = os.environ.get("FAST_MODEL") or os.environ.get("FAST_INTENT_MODEL") or "gpt-4.1-nano"
CLIENT_MODE: Optional[str] = None
LANGCHAIN_AVAILABLE = False
_ChatOpenAI = None
STREAMING_ENABLED = os.environ.get("AI_STREAMING", "true").lower() not in {"0", "false", "no"}
if OPENAI_BASE_URL and "ollama" in OPENAI_BASE_URL and "AI_STREAMING" not in os.environ:
STREAMING_ENABLED = False
PREVIEW_MAX_INFLIGHT = int(os.environ.get("AI_PREVIEW_MAX_INFLIGHT", "3"))
if OPENAI_API_KEY:
try:
from langchain_openai import ChatOpenAI # type: ignore
_ChatOpenAI = ChatOpenAI
LANGCHAIN_AVAILABLE = True
CLIENT_MODE = "langchain"
except Exception as client_exc: # pragma: no cover - import guard
logger.warning("LangChain OpenAI init failed: %s", client_exc)
if CLIENT_MODE == "langchain":
logger.info("AI mode: LIVE (fast_model=%s smart_model=%s)", FAST_MODEL, SMART_MODEL)
else:
logger.info("AI mode: MOCK (no OpenAI key or LangChain init failure)")
def get_chat_model(
model_name: str,
streaming: bool = False,
max_tokens: Optional[int] = None,
model_kwargs: Optional[dict] = None,
):
if not LANGCHAIN_AVAILABLE or not _ChatOpenAI:
return None
kwargs = {"model": model_name, "api_key": OPENAI_API_KEY, "streaming": streaming}
if max_tokens is not None:
kwargs["max_tokens"] = max_tokens
if model_kwargs:
kwargs["model_kwargs"] = model_kwargs
return _ChatOpenAI(**kwargs)
__all__ = [
"logger",
"OUTPUT_DIR",
"ASSETS_DIR",
"DATA_DIR",
"TEMPLATE_DIR",
"STYLE_DB_PATH",
"TEMPLATE_DB_PATH",
"VERSIONS_DB_PATH",
"OPENAI_API_KEY",
"OPENAI_BASE_URL",
"JAVA_BACKEND_URL",
"SMART_MODEL",
"CLIENT_MODE",
"LANGCHAIN_AVAILABLE",
"get_chat_model",
"FAST_MODEL",
"STREAMING_ENABLED",
"PREVIEW_MAX_INFLIGHT",
]
@@ -1,57 +0,0 @@
from __future__ import annotations
from typing import List, Tuple
KEYWORDS: List[Tuple[str, str]] = [
("business card", "business_card"),
("business-card", "business_card"),
("card", "business_card"),
("recipe", "recipe"),
("cookbook", "recipe"),
("menu", "menu"),
("flyer", "flyer"),
("brochure", "brochure"),
("poster", "poster"),
("slide", "presentation"),
("deck", "presentation"),
("presentation", "presentation"),
("pitch", "presentation"),
("whitepaper", "whitepaper"),
("datasheet", "datasheet"),
("case study", "case_study"),
("press release", "press_release"),
("agenda", "agenda"),
("minutes", "minutes"),
("checklist", "checklist"),
("newsletter", "newsletter"),
("proposal", "proposal"),
("one-pager", "one_pager"),
("one pager", "one_pager"),
("invoice", "invoice"),
("resume", "resume"),
("cv", "resume"),
("contract", "contract"),
("agreement", "contract"),
("letter", "letter"),
("report", "report"),
("paper", "academic"),
("research", "academic"),
("thesis", "academic"),
("poem", "creative"),
("manual", "manual"),
("timeline", "timeline"),
]
def detect_document_type(prompt: str, latex_code: str | None = None) -> str:
"""Heuristic classifier for document types based on prompt/latex text."""
text = (prompt or "").lower()
latex_text = (latex_code or "").lower()
for keyword, label in KEYWORDS:
if keyword in text or keyword in latex_text:
return label
return "document"
__all__ = ["detect_document_type"]
@@ -1,22 +0,0 @@
from __future__ import annotations
from typing import Any, Dict, List
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
def to_lc_messages(messages: List[Dict[str, Any]]):
lc_messages = []
for msg in messages:
role = msg.get("role")
content = msg.get("content")
if role == "system":
lc_messages.append(SystemMessage(content=content))
elif role == "assistant":
lc_messages.append(AIMessage(content=content))
else:
lc_messages.append(HumanMessage(content=content))
return lc_messages
__all__ = ["to_lc_messages"]
@@ -1,547 +0,0 @@
from __future__ import annotations
import re
from functools import lru_cache
from typing import List, Optional
ALLOWED_LATEX_PACKAGES = {
"courier",
"graphicx",
"geometry",
"helvet",
"lmodern",
"mathpazo",
"xcolor",
"tabularx",
"paracol",
"multicol",
"longtable",
"setspace",
"enumitem",
"titlesec",
"array",
"inputenc",
"fontenc",
"tikz",
}
def _strip_body_content(body: str) -> str:
"""
Remove user data while keeping layout/structure commands.
Keeps \begin/\end blocks, command scaffolding, and drops plain text.
"""
lines: List[str] = []
for line in body.splitlines():
stripped = line.strip()
if not stripped:
lines.append("")
continue
if stripped.startswith("%"):
continue
if "\\begin" in stripped or "\\end" in stripped:
lines.append(line)
continue
if stripped.startswith("\\"):
line_no_comments = line.split("%", 1)[0]
line_sections = re.sub(
r"(\\(?:section|subsection|subsubsection|paragraph|subparagraph|chapter|part)\*?)\{[^}]*\}",
r"\\1{}",
line_no_comments,
)
line_items = re.sub(r"^\\item.*", r"\\item {}", line_sections)
line_text_cmds = re.sub(
r"\\text(?:bf|it|tt|sc|sf|normal|emph)\{[^}]*\}",
lambda match: match.group(0).split("{")[0] + "{}",
line_items,
)
cleaned = re.sub(r"(?<!\\)[A-Za-z][A-Za-z0-9 ,.;:'\"!?-]*", "", line_text_cmds).strip()
if cleaned:
lines.append(cleaned)
continue
cleaned = re.sub(r"[A-Za-z0-9]+", "", line).strip()
if cleaned:
lines.append(cleaned)
return "\n".join(lines)
def extract_layout_hint(latex_code: str, max_chars: Optional[int] = None) -> str:
"""Keep layout-defining LaTeX while stripping user text."""
if not latex_code:
return ""
preamble, body = "", latex_code
split_doc = latex_code.split(r"\begin{document}", 1)
if len(split_doc) == 2:
preamble, body = split_doc
sanitized_body = _strip_body_content(body)
hint = f"{preamble}\n% --- layout only (data stripped) ---\n{sanitized_body}"
return hint if max_chars is None else hint[:max_chars]
@lru_cache(maxsize=64)
def _word_to_int(word: str) -> Optional[int]:
"""Convert simple English number words to int (0-100)."""
words = {
"zero": 0,
"one": 1,
"two": 2,
"three": 3,
"four": 4,
"five": 5,
"six": 6,
"seven": 7,
"eight": 8,
"nine": 9,
"ten": 10,
"twenty": 20,
"thirty": 30,
"forty": 40,
"fifty": 50,
"sixty": 60,
"seventy": 70,
"eighty": 80,
"ninety": 90,
"hundred": 100,
}
return words.get(word.strip().lower())
def _sanitize_color_mix(match: re.Match[str]) -> str:
token = match.group(1)
if token.isdigit():
val = int(token)
else:
converted = _word_to_int(token)
if converted is None:
digits = "".join(ch for ch in token if ch.isdigit())
val = int(digits) if digits else 80
else:
val = converted
val = max(0, min(100, val))
return f"!{val}!"
def sanitize_latex(latex_code: str) -> str:
"""Normalize invalid xcolor syntax like '!eighty!' to '!80!'."""
if not latex_code:
return latex_code
return re.sub(r"!\s*([A-Za-z0-9]+)\s*!", _sanitize_color_mix, latex_code)
def strip_missing_packages(latex_code: str) -> str:
"""Remove packages unavailable in the runtime environment."""
if not latex_code:
return latex_code
code = latex_code
code = re.sub(r"^\\usepackage\{siunitx\}\s*$", "", code, flags=re.MULTILINE)
code = re.sub(r"\\sisetup\{[^}]*\}", "", code, flags=re.DOTALL)
code = re.sub(r"\\num\{([^}]*)\}", r"\\1", code)
code = re.sub(
r"^\\(setmainfont|setsansfont|setmonofont|newfontfamily)\b.*$",
"",
code,
flags=re.MULTILINE,
)
def _filter_packages(match: re.Match[str]) -> str:
options = match.group(1) or ""
packages = [pkg.strip() for pkg in match.group(2).split(",") if pkg.strip()]
allowed = [pkg for pkg in packages if pkg in ALLOWED_LATEX_PACKAGES]
if not allowed:
return ""
return f"\\usepackage{options}{{{', '.join(allowed)}}}"
return re.sub(
r"^\\usepackage(\[[^\]]*\])?\{([^}]*)\}\s*$",
_filter_packages,
code,
flags=re.MULTILINE,
)
def remove_leading_pagebreaks(latex_code: str) -> str:
"""Strip explicit page breaks at the start of the document body."""
if not latex_code:
return latex_code
parts = latex_code.split(r"\begin{document}", 1)
if len(parts) == 2:
preamble, body = parts
cleaned_body = re.sub(
r"^\s*(\\(newpage|clearpage|pagebreak|vfill)\b\s*)+",
"\n",
body,
flags=re.IGNORECASE | re.MULTILINE,
)
return f"{preamble}\\begin{{document}}{cleaned_body}"
return re.sub(
r"^\s*(\\(newpage|clearpage|pagebreak|vfill)\b\s*)+",
"\n",
latex_code,
flags=re.IGNORECASE | re.MULTILINE,
)
def strip_leading_pagebreaks(latex_code: str) -> str:
"""Drop accidental leading page breaks that cause empty first pages."""
if not latex_code:
return latex_code
parts = latex_code.split(r"\begin{document}", 1)
if len(parts) == 2:
preamble, body = parts
cleaned_body = re.sub(
r"^\s*(\\clearpage|\\newpage|\\pagebreak|\\vfill)+\s*",
"",
body,
flags=re.MULTILINE,
)
return f"{preamble}\\begin{{document}}{cleaned_body}"
return re.sub(
r"^\s*(\\clearpage|\\newpage|\\pagebreak|\\vfill)+\s*",
"",
latex_code,
flags=re.MULTILINE,
)
def fix_tabular_row_endings(latex_code: str) -> str:
"""Ensure tabular environments close rows before \\end{tabular}."""
pattern = re.compile(r"(&[^\n]*)\n\\end{tabular}", re.MULTILINE)
return pattern.sub(r"\\1 \\\\ \n\\end{tabular}", latex_code)
def strip_placeholder_rules(latex_code: str) -> str:
"""Remove placeholder boxes like \\rule/\\colorbox used as fake images."""
code = re.sub(r"\\rule\s*\{\s*[\d\.]+[a-zA-Z]*\s*\}\s*\{\s*[\d\.]+[a-zA-Z]*\s*\}", "", latex_code)
code = re.sub(r"\\fcolorbox\{[^}]*\}\{[^}]*\}\{[^}]*\}", "", code)
code = re.sub(r"\\colorbox\{[^}]*\}\{[^}]*\}", "", code)
# Strip simple tikz pictures that are just boxes/fills
code = re.sub(
r"\\begin\{tikzpicture\}[\s\S]*?\\end\{tikzpicture\}",
"",
code,
flags=re.MULTILINE,
)
return code
def strip_number_grouping_junk(latex_code: str) -> str:
"""Remove stray siunitx options text that may leak into the document body."""
if not latex_code:
return latex_code
# Drop standalone lines/paragraphs that look like siunitx option lists (common when chunks split)
return re.sub(
r"(?im)^\s*,\s*(group-minimum-digits|detect-all|table-number-alignment|round-mode|round-precision)\b.*$",
"",
latex_code,
)
def rebalance_invoice_tables(latex_code: str) -> str:
"""Use wrapped columns for common invoice tables to avoid overflow."""
if not latex_code:
return latex_code
# Legacy 4-col invoices (Item, Desc, Price, Total)
code = latex_code.replace(
r"\\begin{tabularx}{\\textwidth}{@{}l l r r@{}}",
r"\\begin{tabularx}{\\textwidth}{@{}>{\\raggedright\\arraybackslash}p{0.30\\textwidth}>{\\raggedright\\arraybackslash}X>{\\raggedleft\\arraybackslash}p{1.5cm}>{\\raggedleft\\arraybackslash}p{2.3cm}@{}}",
)
# Current 5-col invoices (Item, Description, Qty, Unit, Line Total)
wrapped_invoice_five = (
r"@{}"
r">{\\raggedright\\arraybackslash}p{0.16\\textwidth}"
r">{\\raggedright\\arraybackslash}p{0.50\\textwidth}"
r">{\\raggedleft\\arraybackslash}p{0.09\\textwidth}"
r">{\\raggedleft\\arraybackslash}p{0.12\\textwidth}"
r">{\\raggedleft\\arraybackslash}p{0.13\\textwidth}"
r"@{}"
)
five_col_patterns = [
(
# tabularx with first col l/c, X desc, then three p{} numeric cols (matches default invoice template)
r"(\\begin{tabularx}\{\s*\\textwidth\s*\}\{)\s*@?\{\}?\s*[cl]\s+X\s+p\{[^}]+\}\s+p\{[^}]+\}\s+p\{[^}]+\}\s*@?\{\}?\s*(\})",
r"\1" + wrapped_invoice_five + r"\2",
),
(
# longtable version of the same layout (after upgrades)
r"(\\begin{longtable}\{)\s*@?\{\}?\s*[cl]\s+X\s+p\{[^}]+\}\s+p\{[^}]+\}\s+p\{[^}]+\}\s*@?\{\}?\s*(\})",
r"\1" + wrapped_invoice_five + r"\2",
),
]
for pattern, replacement in five_col_patterns:
code = re.sub(pattern, replacement, code, flags=re.IGNORECASE)
return code
def normalize_tabular_like_begins(latex_code: str) -> str:
"""
Fix common malformed tabular/tabularx/longtable begins where the colspec
is not passed as a braced argument (e.g. `\\begin{tabularx}\\textwidth{...}`
or `\\begin{tabularx}{\\textwidth}\\ItemsColSpec`).
"""
if not latex_code:
return latex_code
code = latex_code
# \begin{tabularx}\textwidth{...} -> \begin{tabularx}{\textwidth}{...}
code = re.sub(
r"\\begin{tabularx}\s*\\textwidth\s*\{([^}]*)\}",
lambda m: f"\\begin{{tabularx}}{{\\textwidth}}{{{m.group(1).strip()}}}",
code,
)
# \begin{tabularx}{\textwidth}\ItemsColSpec -> wrap colspec in braces
code = re.sub(
r"\\begin{tabularx}\s*\{\s*\\textwidth\s*\}\s*\\([A-Za-z@][\w@]*)",
lambda m: f"\\begin{{tabularx}}{{\\textwidth}}{{\\{m.group(1)}}}",
code,
)
# \begin{tabularx}{\textwidth}\colspecliteral -> wrap literal spec
code = re.sub(
r"\\begin{tabularx}\s*\{\s*\\textwidth\s*\}\s*([@A-Za-z].*)",
lambda m: f"\\begin{{tabularx}}{{\\textwidth}}{{{m.group(1).strip()}}}",
code,
)
# \begin{tabular}\colspecliteral OR \begin{longtable}\colspecliteral
def _wrap_simple(env: str, text: str) -> str:
return re.sub(
rf"\\begin{{{env}}}\s*([@A-Za-z].*)",
lambda m: f"\\begin{{{env}}}{{{m.group(1).strip()}}}",
text,
)
code = _wrap_simple("tabular", code)
code = _wrap_simple("longtable", code)
return code
def ensure_longtable_support(latex_code: str) -> str:
"""
Guarantee longtable availability and default centering.
- Injects \\usepackage{longtable} if missing.
- Sets \\LTleft/\\LTright to 0pt so longtable spans the text width without manual centering.
"""
if not latex_code:
return latex_code
code = latex_code
if r"\usepackage{longtable}" not in code:
code = re.sub(
r"(\\documentclass[^\n]*\n)",
r"\1\\usepackage{longtable}\n",
code,
count=1,
)
if r"\setlength\LTleft" not in code:
# Use a lambda so backslashes are treated literally (avoid \L escape errors).
code = re.sub(
r"(\\usepackage\{longtable\}[^\n]*\n)",
lambda m: f"{m.group(1)}\\setlength\\LTleft{{0pt}}\n\\setlength\\LTright{{0pt}}\n",
code,
count=1,
)
return code
def _normalize_alignment_to_wrapped_columns(spec: str) -> str:
"""
Convert simple l/c/r specs to wrapped p-columns that respect text width.
Keeps existing p/m/b/X columns unchanged.
"""
if re.search(r"[pmb]\{|\bX\b", spec):
return spec
cols = [ch for ch in spec if ch in ("l", "c", "r")]
if not cols:
return spec
width = max(0.05, min(0.98, 0.98 / len(cols)))
parts: List[str] = []
for ch in cols:
if ch == "r":
parts.append(r">{\raggedleft\arraybackslash}p{" + f"{width:.3f}\\textwidth" + "}")
else:
parts.append(r">{\raggedright\arraybackslash}p{" + f"{width:.3f}\\textwidth" + "}")
return "@{}" + "".join(parts) + "@{}"
def upgrade_tabular_tables_to_longtable(latex_code: str) -> str:
"""
Replace table+tabular blocks with longtable so large tables break across pages,
stay centered, and repeat headers on each page.
"""
if not latex_code:
return latex_code
pattern = re.compile(
r"\\begin{table}.*?\\begin{tabular}\{([^}]*)\}(.*?)\\end{tabular}.*?\\end{table}",
re.DOTALL,
)
def _build_longtable(match: re.Match[str]) -> str:
align_spec = match.group(1)
body = match.group(2).strip()
normalized_spec = _normalize_alignment_to_wrapped_columns(align_spec)
header_block = ""
body_block = body
hline_split = re.split(r"\\hline", body, maxsplit=1)
if len(hline_split) == 2:
header_block = hline_split[0].strip() + r"\\\hline"
body_block = hline_split[1].lstrip()
else:
first_row_split = re.split(r"\\\\", body, maxsplit=1)
header_block = (first_row_split[0].strip() + r"\\") if first_row_split else ""
body_block = first_row_split[1].lstrip() if len(first_row_split) == 2 else body
header_block = header_block.strip()
body_block = body_block.strip()
return (
"\n\\setlength\\LTleft{0pt}\n"
"\\setlength\\LTright{0pt}\n"
f"\\begin{{longtable}}{{{normalized_spec}}}\n"
f"{header_block}\n"
"\\endfirsthead\n"
f"{header_block}\n"
"\\endhead\n"
f"{body_block}\n"
"\\end{longtable}\n"
)
return pattern.sub(_build_longtable, latex_code)
def clean_generated_latex(latex_code: str) -> str:
"""Apply all sanitizers used both for compilation and template storage."""
return rebalance_invoice_tables(
upgrade_tabular_tables_to_longtable(
ensure_longtable_support(
normalize_tabular_like_begins(
strip_number_grouping_junk(
fix_tabular_row_endings(
strip_leading_pagebreaks(
remove_leading_pagebreaks(
strip_missing_packages(
sanitize_latex(
ensure_full_latex_document(latex_code)
)
)
)
)
)
)
)
)
)
)
def apply_style_overrides(latex_code: str, style_profile: dict) -> str:
"""Apply deterministic font + accent styling without altering layout."""
if not latex_code:
return latex_code
code = latex_code
font = (style_profile or {}).get("font_preference") or ""
accent = (style_profile or {}).get("color_accent") or ""
font_map = {
"serif": ("mathpazo", "\\renewcommand{\\familydefault}{\\rmdefault}"),
"sans": ("helvet", "\\renewcommand{\\familydefault}{\\sfdefault}"),
"helvet": ("helvet", "\\renewcommand{\\familydefault}{\\sfdefault}"),
"mono": ("courier", "\\renewcommand{\\familydefault}{\\ttdefault}"),
"modern": ("lmodern", None),
}
pkg = None
family_cmd = None
if isinstance(font, str):
pkg, family_cmd = font_map.get(font.lower(), (None, None))
if pkg:
code = re.sub(
r"^\\usepackage\{(helvet|mathpazo|lmodern|courier)\}\s*$",
"",
code,
flags=re.MULTILINE,
)
code = re.sub(
r"^\\renewcommand\{\\familydefault\}\{\\(sfdefault|rmdefault|ttdefault)\}\s*$",
"",
code,
flags=re.MULTILINE,
)
def _ensure_package_and_command(text: str) -> str:
if not pkg:
return text
insert = f"\\usepackage{{{pkg}}}\n"
if family_cmd:
insert += family_cmd + "\n"
if r"\begin{document}" in text:
return re.sub(r"(\\begin\{document\})", insert + r"\1", text, count=1)
return insert + text
code = _ensure_package_and_command(code)
if accent:
accent_hex_match = re.fullmatch(r"#?([0-9a-fA-F]{6})", str(accent).strip())
if accent_hex_match:
accent_line = f"\\definecolor{{accent}}{{HTML}}{{{accent_hex_match.group(1).upper()}}}"
else:
accent_name = re.sub(r"[^A-Za-z]+", "", str(accent)) or "blue"
accent_line = f"\\colorlet{{accent}}{{{accent_name}}}"
if re.search(r"^\\definecolor\{accent\}|^\\colorlet\{accent\}", code, flags=re.MULTILINE):
code = re.sub(r"^\\definecolor\{accent\}.*$", accent_line, code, flags=re.MULTILINE)
code = re.sub(r"^\\colorlet\{accent\}.*$", accent_line, code, flags=re.MULTILINE)
else:
needs_xcolor = r"\usepackage{xcolor}" not in code
insert = ""
if needs_xcolor:
insert += "\\usepackage{xcolor}\n"
insert += accent_line + "\n"
if r"\usepackage{xcolor}" in code:
code = re.sub(r"(\\usepackage\{xcolor\}[^\n]*\n)", r"\1" + insert, code, count=1)
elif r"\begin{document}" in code:
code = re.sub(r"(\\begin\{document\})", insert + r"\1", code, count=1)
else:
code = insert + code
return code
def ensure_full_latex_document(text: str) -> str:
"""Trim output to a single LaTeX document starting at \\documentclass and ending at \\end{document}."""
if not text:
return text
start = text.find(r"\documentclass")
end = text.rfind(r"\end{document}")
if start == -1 or end == -1:
return text
end += len(r"\end{document}")
return text[start:end]
__all__ = [
"extract_layout_hint",
"sanitize_latex",
"strip_missing_packages",
"remove_leading_pagebreaks",
"strip_leading_pagebreaks",
"fix_tabular_row_endings",
"rebalance_invoice_tables",
"clean_generated_latex",
"apply_style_overrides",
"ensure_full_latex_document",
]
@@ -1,491 +0,0 @@
from __future__ import annotations
import base64
import os
import re
import subprocess
import tempfile
import xml.etree.ElementTree as ET
from typing import Any, Dict, List, Optional
from config import logger
def _safe_float(value: Optional[str], fallback: float = 0.0) -> float:
"""Convert an attribute value to float while handling bad input."""
try:
if value is None:
return fallback
return float(value)
except (TypeError, ValueError):
return fallback
def _read_image_as_data_url(path: str) -> Optional[str]:
"""Return a data URL for the image if it exists."""
if not os.path.exists(path):
return None
mime = "image/png"
_, ext = os.path.splitext(path)
if ext.lower() in {".jpg", ".jpeg"}:
mime = "image/jpeg"
elif ext.lower() == ".gif":
mime = "image/gif"
try:
with open(path, "rb") as img_handle:
encoded = base64.b64encode(img_handle.read()).decode("ascii")
return f"data:{mime};base64,{encoded}"
except OSError as exc:
logger.warning("[PDF-EDITOR] Failed to read image %s: %s", path, exc)
return None
def _parse_fonts(root: ET.Element) -> List[Dict[str, Any]]:
fonts: List[Dict[str, Any]] = []
for spec in root.findall(".//fontspec"):
font_id = spec.attrib.get("id")
base_name = spec.attrib.get("family")
size = _safe_float(spec.attrib.get("size"), 12.0)
color = spec.attrib.get("color")
name_lower = (base_name or "").lower()
flags = 0
if "bold" in name_lower:
flags |= 0x100 # ForceBold
if "italic" in name_lower or "oblique" in name_lower:
flags |= 0x40 # Italic
fonts.append(
{
"id": font_id,
"uid": font_id,
"baseName": base_name,
"embedded": True,
"program": None,
"programFormat": None,
"webProgram": None,
"webProgramFormat": None,
"pdfProgram": None,
"pdfProgramFormat": None,
"ascent": size,
"descent": -size * 0.25,
"unitsPerEm": max(size, 1),
"standard14Name": None,
"color": color,
"fontDescriptorFlags": flags or None,
}
)
return fonts
def _parse_color_components(color: Optional[str]) -> Optional[List[float]]:
"""Convert a hex/rgb color string into normalized RGB components."""
if not color:
return None
color = color.strip()
hex_match = re.fullmatch(r"#?([0-9a-fA-F]{6})", color)
short_hex_match = re.fullmatch(r"#?([0-9a-fA-F]{3})", color)
rgb_match = re.fullmatch(r"rgb\(\s*(\d{1,3})\s*,\s*(\d{1,3})\s*,\s*(\d{1,3})\s*\)", color, re.IGNORECASE)
if hex_match:
hex_value = hex_match.group(1)
r = int(hex_value[0:2], 16)
g = int(hex_value[2:4], 16)
b = int(hex_value[4:6], 16)
return [r / 255.0, g / 255.0, b / 255.0]
if short_hex_match:
hex_value = short_hex_match.group(1)
r = int(hex_value[0] * 2, 16)
g = int(hex_value[1] * 2, 16)
b = int(hex_value[2] * 2, 16)
return [r / 255.0, g / 255.0, b / 255.0]
if rgb_match:
r = min(max(int(rgb_match.group(1)), 0), 255)
g = min(max(int(rgb_match.group(2)), 0), 255)
b = min(max(int(rgb_match.group(3)), 0), 255)
return [r / 255.0, g / 255.0, b / 255.0]
return None
# --------------------
# Table normalization
# --------------------
def _cluster(values: List[float], tol: float) -> List[List[float]]:
clusters: List[List[float]] = []
for v in sorted(values):
if not clusters or abs(v - clusters[-1][-1]) > tol:
clusters.append([v])
else:
clusters[-1].append(v)
return clusters
def _dedupe_by_xy_text(elements: List[Dict[str, Any]], eps: float = 1.0) -> List[Dict[str, Any]]:
"""
Dedupe using quantized x,y and normalized text (ignores width/height jitter).
Keeps the better scoring text; if equal text, keeps the first.
"""
if not elements:
return elements
def _key(el: Dict[str, Any]) -> tuple[int, int, str]:
x = el.get("x") or 0.0
y = el.get("y") or 0.0
t = (el.get("text") or "").strip().replace("\u00a0", " ")
return (int(round(x / eps)), int(round(y / eps)), t)
def _score_text_global(t: str) -> tuple[int, int, int]:
stripped = t.strip()
has_currency = 1 if any(sym in stripped for sym in ("$", "", "£", "¥")) else 0
non_space = sum(1 for ch in stripped if not ch.isspace())
digits = sum(1 for ch in stripped if ch.isdigit())
return (has_currency, non_space, digits)
deduped: Dict[tuple[int, int, str], Dict[str, Any]] = {}
for el in elements:
key = _key(el)
existing = deduped.get(key)
if existing is None:
deduped[key] = el
continue
t_new = key[2]
t_old = (existing.get("text") or "").strip().replace("\u00a0", " ")
score_new = _score_text_global(t_new)
score_old = _score_text_global(t_old)
if score_new > score_old:
deduped[key] = el
return list(deduped.values())
def _detect_table_region(text_elements: List[Dict[str, Any]], page_width: float) -> Optional[Dict[str, Any]]:
"""
Header-agnostic table detection via x clustering.
Returns dict with anchors, boundaries, observed_left/right, y_min/y_max.
"""
candidates = [
el
for el in text_elements
if el.get("text") not in (None, "")
and isinstance(el.get("x"), (int, float))
and isinstance(el.get("y"), (int, float))
and isinstance(el.get("height"), (int, float))
]
if len(candidates) < 8:
return None
heights = [c["height"] for c in candidates if c.get("height")]
if not heights:
return None
med_h = sorted(heights)[len(heights) // 2]
short_candidates = [c for c in candidates if c["height"] <= med_h * 1.8]
if len(short_candidates) < 8:
return None
x_centers = [c["x"] + (c.get("width") or 0) * 0.5 for c in short_candidates]
x_clusters = _cluster(x_centers, tol=12.0)
x_clusters = [c for c in x_clusters if len(c) >= 3]
if len(x_clusters) < 4:
return None
anchors = [sum(c) / len(c) for c in x_clusters]
anchors.sort()
y_vals = sorted(c["y"] for c in short_candidates)
y_clusters = _cluster(y_vals, tol=3.0)
if len(y_clusters) < 3:
return None
y_clusters.sort(key=lambda c: len(c), reverse=True)
y_min = min(y_clusters[0])
y_max = max(y_clusters[0])
min_anchor = min(anchors)
max_anchor = max(anchors)
PAD = 12.0
band_elems = []
for el in short_candidates:
cx = el["x"] + (el.get("width") or 0) * 0.5
if y_min - PAD <= el["y"] <= y_max + PAD and (min_anchor - PAD) <= cx <= (max_anchor + PAD):
band_elems.append(el)
if not band_elems:
return None
observed_left = min(n["x"] for n in band_elems)
observed_right = max(n["x"] + (n.get("width") or 0) for n in band_elems)
raw_bounds: List[float] = []
for i, ax in enumerate(anchors):
if i == 0:
gap = anchors[1] - anchors[0]
raw_bounds.append(ax - gap * 0.5)
else:
raw_bounds.append((anchors[i - 1] + ax) * 0.5)
gap_last = anchors[-1] - anchors[-2] if len(anchors) > 1 else 40.0
raw_bounds.append(anchors[-1] + gap_last * 0.5)
# translate boundaries to align left edge; no scaling to preserve spacing
delta = observed_left - raw_bounds[0]
boundaries = [b + delta for b in raw_bounds]
info = {
"anchors": anchors,
"boundaries": boundaries,
"observed_left": observed_left,
"observed_right": observed_right,
"y_min": y_min,
"y_max": y_max,
}
if os.getenv("PDF_EDITOR_TABLE_DEBUG"):
logger.debug(
"[PDF-EDITOR] table detected anchors=%s boundaries=%s y=(%.2f, %.2f) span=(%.2f, %.2f)",
anchors,
boundaries,
y_min,
y_max,
observed_left,
observed_right,
)
span = boundaries[-1] - boundaries[0]
if span <= 0 or page_width <= 0:
return None
target_left = max(0.0, (page_width - span) * 0.5)
offset = target_left - boundaries[0]
info.update({"offset": offset, "page_width": page_width})
return info
def _snap_table_elements(text_elements: List[Dict[str, Any]], info: Dict[str, Any]) -> List[Dict[str, Any]]:
anchors = info["anchors"]
base_boundaries = info["boundaries"]
offset = info.get("offset", 0.0)
boundaries = [b + offset for b in base_boundaries]
y_min = info["y_min"]
y_max = info["y_max"]
left = info["observed_left"] + offset
right = info["observed_right"] + offset
def _assign_col(cx: float) -> int:
return min(range(len(anchors)), key=lambda i: abs(anchors[i] - cx))
EPS = 1.0
PAD = 6.0
for el in text_elements:
x = el.get("x")
w = el.get("width") or 0
y = el.get("y")
if not isinstance(x, (int, float)) or not isinstance(y, (int, float)):
continue
cx = x + w * 0.5 + offset
if not (left - PAD <= cx <= right + PAD and y_min - PAD <= y <= y_max + PAD):
continue
col = _assign_col(cx - offset)
col_left = boundaries[col]
col_right = boundaries[col + 1]
new_left = x + offset
new_right = x + w + offset
if new_left < col_left - EPS:
new_left = col_left
if new_right > col_right + EPS:
new_right = col_right
if new_right <= new_left:
mid = (col_left + col_right) * 0.5
new_left = mid - 0.5
new_right = mid + 0.5
el["x"] = new_left
el["width"] = max(1.0, new_right - new_left)
el["textMatrix"] = [1, 0, 0, 1, el["x"], el["y"]]
return text_elements
def _parse_page(page_elem: ET.Element, base_dir: str, font_colors: Dict[str, Optional[str]]) -> Dict[str, Any]:
page_width = _safe_float(page_elem.attrib.get("width"), 612.0)
page_height = _safe_float(page_elem.attrib.get("height"), 792.0)
text_elements: List[Dict[str, Any]] = []
image_elements: List[Dict[str, Any]] = []
# Cluster near-identical text draws on the same baseline and keep the best candidate.
EPS = 1.0
best_by_pos: Dict[tuple[int, int], Dict[str, Any]] = {}
def _q(value: float) -> int:
try:
return int(round(value / EPS))
except Exception:
return 0
def _score_text(t: str) -> tuple[int, int, int]:
stripped = t.strip()
has_currency = 1 if any(sym in stripped for sym in ("$", "", "£", "¥")) else 0
non_space = sum(1 for ch in stripped if not ch.isspace())
digits = sum(1 for ch in stripped if ch.isdigit())
return (has_currency, non_space, digits)
for index, text_elem in enumerate(page_elem.findall("text")):
raw_text = "".join(text_elem.itertext()).replace("\u00A0", " ")
text = raw_text.strip("\n")
left = _safe_float(text_elem.attrib.get("left"))
top = _safe_float(text_elem.attrib.get("top"))
width = _safe_float(text_elem.attrib.get("width"))
height = _safe_float(text_elem.attrib.get("height"))
font_id = text_elem.attrib.get("font")
font_color = font_colors.get(font_id) if font_id else None
fill_components = _parse_color_components(font_color)
candidate = {
"id": f"t-{index}",
"text": text,
"fontId": font_id,
"fontSize": height if height > 0 else None,
"x": left,
"y": page_height - top,
"width": width,
"height": height,
"textMatrix": [1, 0, 0, 1, left, page_height - top],
"fillColor": {"colorSpace": "RGB", "components": fill_components} if fill_components else None,
}
pos_key = (_q(left), _q(page_height - top))
existing = best_by_pos.get(pos_key)
if existing is None:
best_by_pos[pos_key] = candidate
else:
if _score_text(candidate["text"]) > _score_text(existing["text"]):
best_by_pos[pos_key] = candidate
# Optional merge of adjacent runs on the same baseline (e.g., "$" + "38.00")
merged: List[Dict[str, Any]] = []
base_elements = _dedupe_by_xy_text(sorted(best_by_pos.values(), key=lambda i: (i["y"], i["x"])), eps=1.0)
# Sort by baseline (y) then x to make merges stable and ordering deterministic
for item in sorted(base_elements, key=lambda i: (i["y"], i["x"])):
if not merged:
merged.append(item)
continue
prev = merged[-1]
same_line = _q(prev["y"]) == _q(item["y"])
if not same_line:
merged.append(item)
continue
prev_right = prev["x"] + (prev.get("width") or 0)
gap = item["x"] - prev_right
max_h = max(prev.get("height") or 0, item.get("height") or 0)
allowed_gap = max(2.0, 0.25 * max_h)
if gap <= allowed_gap and gap >= -allowed_gap:
# Merge
needs_space = (
prev["text"].strip() != ""
and item["text"].strip() != ""
and not prev["text"].endswith(" ")
and not item["text"].startswith(" ")
and not prev["text"].rstrip().endswith(("$", "", "£", "¥"))
)
merged_text = prev["text"] + (" " if needs_space else "") + item["text"]
new_left = min(prev["x"], item["x"])
new_right = max(prev_right, item["x"] + (item.get("width") or 0))
prev.update(
{
"text": merged_text,
"x": new_left,
"width": new_right - new_left,
"height": max_h,
# keep y and fontId from the left-most run
}
)
else:
merged.append(item)
# Phase A: dedupe by xy+text to remove duplicate draws
deduped = _dedupe_by_xy_text(merged, eps=1.0)
# Phase B: table detection + snapping (header-agnostic)
table_info = _detect_table_region(deduped, page_width)
if table_info:
snapped = _snap_table_elements(deduped, table_info)
text_elements = _dedupe_by_xy_text(snapped, eps=1.0)
else:
text_elements = deduped
for img_index, image_elem in enumerate(page_elem.findall("image")):
left = _safe_float(image_elem.attrib.get("left"))
top = _safe_float(image_elem.attrib.get("top"))
width = _safe_float(image_elem.attrib.get("width"))
height = _safe_float(image_elem.attrib.get("height"))
src = image_elem.attrib.get("src")
image_path = os.path.join(base_dir, src) if src else None
data_url = _read_image_as_data_url(image_path) if image_path else None
image_elements.append(
{
"id": src or f"image-{img_index}",
"objectName": src,
"x": left,
"y": max(page_height - top - height, 0),
"width": width,
"height": height,
"left": left,
"top": top,
"bottom": max(page_height - top, 0),
"right": left + width,
"imageData": data_url,
"imageFormat": os.path.splitext(src)[1][1:] if src else None,
}
)
return {
"width": page_width,
"height": page_height,
"pageNumber": _safe_float(page_elem.attrib.get("number"), 0),
"textElements": text_elements,
"imageElements": image_elements,
}
def convert_pdf_to_text_editor_document(pdf_path: str) -> Dict[str, Any]:
"""Convert a PDF to a JSON payload usable by the PDF text editor."""
if not os.path.exists(pdf_path):
raise FileNotFoundError(pdf_path)
with tempfile.TemporaryDirectory() as tmpdir:
output_base = os.path.join(tmpdir, "doc")
command = [
"pdftohtml",
"-xml",
"-enc",
"UTF-8",
"-nodrm",
"-q",
pdf_path,
output_base,
]
try:
subprocess.run(command, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
except (FileNotFoundError, subprocess.CalledProcessError) as exc:
logger.error("[PDF-EDITOR] pdftohtml failed for %s: %s", pdf_path, exc)
raise
xml_path = f"{output_base}.xml"
if not os.path.exists(xml_path):
raise FileNotFoundError(xml_path)
tree = ET.parse(xml_path)
root = tree.getroot()
fonts = _parse_fonts(root)
font_colors = {font["id"]: font.get("color") for font in fonts if font.get("id")}
pages = [_parse_page(page_elem, tmpdir, font_colors) for page_elem in root.findall("page")]
document: Dict[str, Any] = {
"metadata": {"numberOfPages": len(pages)},
"fonts": fonts,
"pages": pages,
"lazyImages": False,
}
return {"document": document}
__all__ = ["convert_pdf_to_text_editor_document"]
@@ -1,147 +0,0 @@
from __future__ import annotations
import base64
import os
import subprocess
import tempfile
import time
from typing import List, Optional
from config import OUTPUT_DIR, logger
_FONT_SPEC_MARKERS = (
"\\usepackage{fontspec}",
"\\setmainfont",
"\\setsansfont",
"\\setmonofont",
"\\newfontfamily",
)
def _needs_unicode_engine(latex_code: str) -> bool:
return any(marker in latex_code for marker in _FONT_SPEC_MARKERS)
def _run_latex(engine: str, tex_filename: str) -> subprocess.CompletedProcess:
return subprocess.run(
[engine, "-interaction=nonstopmode", "-output-directory", OUTPUT_DIR, tex_filename],
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
timeout=30,
cwd=OUTPUT_DIR,
)
def render_pdf_to_images(pdf_bytes: bytes, max_pages: int = 2, dpi: int = 160) -> List[str]:
"""Render the first N pages of a PDF to base64-encoded PNG data URLs."""
images: List[str] = []
with tempfile.TemporaryDirectory() as tmpdir:
pdf_path = os.path.join(tmpdir, "upload.pdf")
with open(pdf_path, "wb") as handle:
handle.write(pdf_bytes)
output_prefix = os.path.join(tmpdir, "page")
try:
subprocess.run(
[
"pdftoppm",
"-png",
"-r",
str(dpi),
"-f",
"1",
"-l",
str(max_pages),
pdf_path,
output_prefix,
],
check=True,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
timeout=30,
)
except Exception as exc:
logger.error("[IMPORT] pdftoppm failed: %s", exc)
return images
for idx in range(1, max_pages + 1):
img_path = f"{output_prefix}-{idx}.png"
if os.path.exists(img_path):
with open(img_path, "rb") as img_handle:
encoded = base64.b64encode(img_handle.read()).decode("utf-8")
images.append(f"data:image/png;base64,{encoded}")
return images
def compile_latex_to_pdf(
latex_code: str,
job_id: str,
*,
log_errors: bool = True,
raise_on_error: bool = False,
) -> Optional[str]:
"""Compile a LaTeX document and return the PDF path."""
tex_filename = os.path.join(OUTPUT_DIR, f"{job_id}.tex")
pdf_filename = f"{job_id}.pdf"
pdf_path = os.path.join(OUTPUT_DIR, pdf_filename)
with open(tex_filename, "w", encoding="utf-8") as handle:
handle.write(latex_code)
try:
t_start = time.perf_counter()
engine = "xelatex" if _needs_unicode_engine(latex_code) else "pdflatex"
first = _run_latex(engine, tex_filename)
if first.returncode != 0 and engine == "pdflatex":
error_output = first.stderr.decode() or first.stdout.decode()
if "fontspec" in error_output and ("XeTeX" in error_output or "LuaTeX" in error_output):
logger.info("[PDF] pdflatex failed due to fontspec; retrying with xelatex")
engine = "xelatex"
first = _run_latex(engine, tex_filename)
t_first = time.perf_counter()
if first.returncode == 0:
_run_latex(engine, tex_filename)
t_end = time.perf_counter()
if os.path.exists(pdf_path):
logger.info(
"[PDF] compiled job_id=%s -> %s (engine=%s first_pass=%.2fs total=%.2fs)",
job_id,
pdf_filename,
engine,
t_first - t_start,
t_end - t_start,
)
return pdf_path
error_output = first.stderr.decode() or first.stdout.decode()
message = error_output.strip() or f"{engine} failed without stderr output"
if log_errors:
logger.error(
"[PDF] not generated job_id=%s code=%s engine=%s after %.2fs: %s",
job_id,
first.returncode,
engine,
t_first - t_start,
message,
)
if raise_on_error:
raise RuntimeError(message)
return None
except subprocess.TimeoutExpired:
message = f"LaTeX compilation timed out for job_id={job_id}"
if log_errors:
logger.error(message)
if raise_on_error:
raise
return None
except Exception as exc:
if log_errors:
logger.error("LaTeX compilation failed: %s", exc)
if raise_on_error:
raise
return None
__all__ = ["render_pdf_to_images", "compile_latex_to_pdf"]
@@ -1,134 +0,0 @@
from __future__ import annotations
import json
from typing import Dict, List, Optional, Any
# Shared rules
ALLOWED_LATEX_PACKAGES = [
"courier",
"graphicx",
"geometry",
"helvet",
"lmodern",
"mathpazo",
"xcolor",
"tabularx",
"paracol",
"multicol",
"longtable",
"setspace",
"enumitem",
"titlesec",
"array",
"inputenc",
"fontenc",
"tikz",
]
LATEX_RULES = [
r"Output must start with \documentclass.",
r"Output must contain exactly one \begin{document} and one \end{document}.",
r"Do not output anything before \documentclass.",
r"Do not output anything after \end{document}.",
"Do not include commentary, apologies, or markdown fences.",
"If uncertain, prefer simpler LaTeX over complex packages.",
"Only use LaTeX packages from the allowlist in this prompt.",
"Do not use fontspec or custom font commands.",
]
def latex_system_prompt(style_profile: Dict[str, Any], document_type: str, template_hint: Optional[str]) -> str:
safe_style = {
"font_preference": style_profile.get("font_preference", "default"),
"tone": style_profile.get("tone", "professional"),
"color_accent": style_profile.get("color_accent", "blue"),
"layout_preference": style_profile.get("layout_preference", "clean"),
}
return (
"You are a LaTeX document generator for PDFs.\n"
f"User Style Profile (trimmed): {json.dumps(safe_style, sort_keys=True)}\n"
f"Document Type: {document_type}\n"
f"Template Hint present: {'yes' if template_hint else 'no'}\n"
"Rules:\n"
"1) Output ONLY valid LaTeX code.\n"
f"- " + "\n- ".join(LATEX_RULES) + "\n"
"2) Use only the allowlisted packages:\n"
f"- " + "\n- ".join(ALLOWED_LATEX_PACKAGES) + "\n"
f"3) Respect preferred font ({safe_style['font_preference']}), tone ({safe_style['tone']}), and color accent ({safe_style['color_accent']}). Use the accent token name 'accent' (e.g., \\color{{accent}} or \\textcolor{{accent}}{{...}}).\n"
"4) If a template hint is provided, stay close to its layout and styling.\n"
"5) Do NOT add placeholder images or black boxes; omit images entirely unless an explicit path or real image content is provided. Do not use \\rule, tikz, or colored rectangles as image stand-ins.\n"
"6) Return a full compilable document."
)
def latex_context_messages(
template_hint: Optional[str],
current_latex: Optional[str],
structured_brief: Optional[str],
) -> List[Dict[str, str]]:
messages: List[Dict[str, str]] = []
if template_hint:
messages.append(
{
"role": "user",
"content": f"REFERENCE TEMPLATE (keep style/layout, do not copy data):\n---\n{template_hint[:2000]}\n---",
}
)
if current_latex:
messages.append(
{
"role": "user",
"content": f"CURRENT LATEX DRAFT (keep structure, apply edits):\n---\n{current_latex[:2000]}\n---",
}
)
if structured_brief:
messages.append(
{
"role": "user",
"content": (
"Structured details gathered from the user (authoritative; do not invent beyond this):\n"
f"---\n{structured_brief}\n---"
),
}
)
return messages
def pdf_qa_system_prompt() -> str:
return (
"You are a helpful assistant. Read the provided PDF text and answer the user's question.\n"
"Respond with:\n"
"Answer: 24 sentences summarizing the answer from the text.\n"
"Evidence: 13 short quotes/snippets from the provided text (must be exact substrings).\n"
"If the answer is not in the text, say: 'Not found in the provided text.' and give a best-effort summary."
)
def brief_missing_info_system_prompt(doc_type: str) -> str:
return (
f"You are a brief-gathering assistant for generating a {doc_type}.\n"
"Be conversational and concise. Ask at most 3 short questions; no multi-part questions.\n"
"If the user hasn't given much, invite them to paste prior material or dump everything they remember.\n"
"Do not invent data; only ask."
)
def vision_layout_system_prompt() -> str:
return (
"You are a LaTeX layout extractor. Given page images of a PDF, return a LaTeX skeleton matching the layout and styling while blanking user content.\n"
"Do NOT copy any readable text from images. Replace all text with placeholders like TITLE HERE, LOREM, XXXX.\n"
"Infer margins, columns, header/footer, tables. Use \\rule{width}{height} placeholders sized to match blocks.\n"
"Use common packages (geometry, xcolor, tabularx, multicol, paracol, tikz). Replace text with placeholders and output a full compilable document.\n"
"Output ONLY LaTeX."
)
__all__ = [
"latex_system_prompt",
"latex_context_messages",
"pdf_qa_system_prompt",
"brief_missing_info_system_prompt",
"vision_layout_system_prompt",
"LATEX_RULES",
]
@@ -1,6 +0,0 @@
Flask==3.0.0
Flask-CORS==4.0.0
# Use modern OpenAI SDK (v1 interface)
openai>=1.12.0
langchain-core==1.2.5
langchain-openai==1.1.6
@@ -1,18 +0,0 @@
from prompts import (
pdf_qa_system_prompt,
vision_layout_system_prompt,
latex_system_prompt,
)
def main():
assert pdf_qa_system_prompt(), "pdf_qa_system_prompt is empty"
assert "Do NOT copy" in vision_layout_system_prompt(), "vision prompt missing no-copy rule"
latex_prompt = latex_system_prompt({}, "document", None)
assert "\\end{document}" in latex_prompt, "latex prompt missing end document mention"
print("prompts OK")
if __name__ == "__main__":
main()
@@ -1,262 +0,0 @@
import re
import shutil
import subprocess
import tempfile
from pathlib import Path
ROOT_DIR = Path(__file__).resolve().parents[2]
TEMPLATE_ROOT = ROOT_DIR / "backend" / "templates"
FRONTEND_PUBLIC = ROOT_DIR / "frontend" / "public" / "templates"
FRONTEND_CATALOG = ROOT_DIR / "frontend" / "src" / "templateCatalog.ts"
TIMEOUT_SEC = 60
LOREM_SENTENCE = (
"Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt "
"ut labore et dolore magna aliqua."
)
LOREM_PARAGRAPH = (
"Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt "
"ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco "
"laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in "
"voluptate velit esse cillum dolore eu fugiat nulla pariatur."
)
LOREM_SHORT = "Lorem ipsum dolor sit amet, consectetur adipiscing elit."
PLACEHOLDER_REPLACEMENTS = {
"TITLE": "Sample Title",
"SUBTITLE": "Sample Subtitle",
"AUTHOR": "Sample Author",
"AUTHOR_LIST": "Sample Author One, Sample Author Two",
"AFFILIATIONS": "Sample Organization",
"ABSTRACT": LOREM_PARAGRAPH,
"KEYWORDS": "keyword1, keyword2, keyword3, keyword4",
"INTRODUCTION": LOREM_PARAGRAPH,
"RELATED_WORK": LOREM_PARAGRAPH,
"METHODOLOGY": LOREM_PARAGRAPH,
"RESULTS": LOREM_PARAGRAPH,
"DISCUSSION": LOREM_PARAGRAPH,
"CONCLUSION": LOREM_SHORT,
"REFERENCES": "Doe, J. (2024). Example Reference. Journal of Examples.",
"MAIN_TEXT": f"{LOREM_PARAGRAPH} {LOREM_PARAGRAPH}",
"FIGURES_TABLES": "Figure 1: Example chart. Table 1: Summary of results.",
"REPORT_TITLE": "Business Report",
"DATE": "2025-01-01",
"EXEC_SUMMARY": LOREM_PARAGRAPH,
"BACKGROUND": LOREM_PARAGRAPH,
"FINDINGS": f"{LOREM_SENTENCE} {LOREM_SENTENCE}",
"RECOMMENDATIONS": "Recommendation 1: Improve efficiency. Recommendation 2: Reduce costs.",
"APPENDIX": LOREM_SHORT,
"NEWSLETTER_TITLE": "Monthly Newsletter",
"TOP_STORY": LOREM_PARAGRAPH,
"UPDATES": f"{LOREM_SENTENCE} {LOREM_SENTENCE}",
"SPOTLIGHT": LOREM_PARAGRAPH,
"FOOTER": "Contact: info@example.com | 123 Main Street",
"RECIPE_TITLE": "Sample Recipe",
"SERVINGS": "Serves 4",
"TIME": "30 minutes",
"INGREDIENTS": r"\begin{itemize}\item Ingredient A\item Ingredient B\item Ingredient C\end{itemize}",
"INSTRUCTIONS": LOREM_PARAGRAPH,
"NOTES": "Notes and tips: adjust seasoning to taste.",
"BUSINESS_NAME": "Your Company",
"BUSINESS_ADDRESS": "123 Main Street, Springfield",
"BUSINESS_CONTACT": "email@example.com | (555) 555-5555",
"INVOICE_NUMBER": "INV-001",
"ISSUE_DATE": "2025-01-01",
"DUE_DATE": "2025-01-15",
"CLIENT_NAME": "Client Name",
"CLIENT_ADDRESS": "456 Client Ave, Metropolis",
"CLIENT_CONTACT": "client@example.com",
"LINE_ITEMS": r"Design Services & 8 & 120 & 960 \\ Consulting & 4 & 150 & 600 \\",
"SUBTOTAL": "1560",
"TAXES": "124.80",
"TOTAL": "1684.80",
"PAYMENT_TERMS": "Net 15",
"PAYMENT_METHODS": "Bank transfer, credit card",
"STUDENT_NAME": "Student Name",
"COURSE_NAME": "Course Name",
"INSTRUCTOR_NAME": "Instructor Name",
"ASSIGNMENT_TITLE": "Assignment Title",
"PROMPT": LOREM_SHORT,
"RESPONSE": LOREM_PARAGRAPH,
"CHAPTER_ONE_TITLE": "Chapter One",
"CHAPTER_ONE": LOREM_PARAGRAPH,
"CHAPTER_TWO_TITLE": "Chapter Two",
"CHAPTER_TWO": LOREM_PARAGRAPH,
"PREFACE": LOREM_SHORT,
"PUBLISHER": "Publisher",
"NAME": "Name",
"EMAIL": "email@example.com",
"PHONE": "(555) 555-5555",
"LOCATION": "City, Country",
"SUMMARY": LOREM_SENTENCE,
"EXPERIENCE": f"{LOREM_SENTENCE} {LOREM_SENTENCE}",
"EDUCATION": "University Name, B.S. in Example Studies",
"SKILLS": "Skills: Analysis, Design, Communication",
"PROJECTS": LOREM_SHORT,
"SUBJECT": "Subject",
"BODY": LOREM_PARAGRAPH,
"RECIPIENT_NAME": "Recipient Name",
"RECIPIENT_TITLE": "Recipient Title",
"RECIPIENT_COMPANY": "Recipient Company",
"RECIPIENT_ADDRESS": "Recipient Address",
"SENDER_NAME": "Sender Name",
"SENDER_ADDRESS": "Sender Address",
"SENDER_EMAIL": "sender@example.com",
"MONTH_YEAR": "January 2025",
"THEME": "Theme",
"WEEK_ROWS": "1 & 2 & 3 & 4 & 5 & 6 & 7 \\\\\\\\ \\\\hline",
"HEADLINE": "Headline",
"SUBTEXT": "Supporting message with a clear benefit.",
"CALL_TO_ACTION": "Call to action",
"CONTACT": "contact@example.com",
"EXPERIMENT_TITLE": "Experiment Title",
"OBJECTIVE": LOREM_SHORT,
"MATERIALS": "Materials list goes here.",
"PROCEDURE": LOREM_PARAGRAPH,
"OBSERVATIONS": LOREM_SHORT,
"INSTITUTION": "Institution",
"PRESENTER": "Presenter",
"AGENDA": "Agenda goes here.",
"KEY_POINTS": "Key points go here.",
"DATA_VISUALS": "Data visuals go here.",
"SUBTITLE": "Subtitle",
"ORGANIZATION": "Organization",
}
def render_template_latex(raw_latex: str) -> str:
def replace(match: re.Match[str]) -> str:
key = match.group(1).strip()
return PLACEHOLDER_REPLACEMENTS.get(key, key.replace("_", " ").title())
return re.sub(r"<<([A-Z0-9_]+)>>", replace, raw_latex)
def find_converter() -> str | None:
if shutil.which("pdftoppm"):
return "pdftoppm"
if shutil.which("magick"):
return "magick"
if shutil.which("convert"):
return "convert"
return None
def pdf_to_jpg(pdf_path: Path, jpg_path: Path, converter: str) -> None:
if converter == "pdftoppm":
subprocess.run(
["pdftoppm", "-jpeg", "-f", "1", "-singlefile", str(pdf_path), str(jpg_path.with_suffix(''))],
check=True,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
timeout=TIMEOUT_SEC,
)
return
if converter == "magick":
subprocess.run(
["magick", "convert", "-density", "150", str(pdf_path), "-quality", "90", str(jpg_path)],
check=True,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
timeout=TIMEOUT_SEC,
)
return
subprocess.run(
["convert", "-density", "150", str(pdf_path), "-quality", "90", str(jpg_path)],
check=True,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
timeout=TIMEOUT_SEC,
)
def main() -> None:
if not shutil.which("pdflatex"):
raise SystemExit("pdflatex not found. Install TeX Live or MikTeX to generate thumbnails.")
converter = find_converter()
if not converter:
raise SystemExit("No PDF-to-image converter found. Install poppler-utils or ImageMagick.")
tex_files = list(TEMPLATE_ROOT.rglob("*.tex"))
if not tex_files:
raise SystemExit(f"No templates found in {TEMPLATE_ROOT}")
FRONTEND_PUBLIC.mkdir(parents=True, exist_ok=True)
catalog: dict[str, list[str]] = {}
failures: list[str] = []
for tex_file in tex_files:
doc_type = tex_file.parent.name
template_id = tex_file.stem
target_dir = FRONTEND_PUBLIC / doc_type
target_dir.mkdir(parents=True, exist_ok=True)
target_jpg = target_dir / f"{template_id}.jpg"
catalog.setdefault(doc_type, [])
if template_id not in catalog[doc_type]:
catalog[doc_type].append(template_id)
with tempfile.TemporaryDirectory() as tmpdir:
tmpdir_path = Path(tmpdir)
rendered = render_template_latex(tex_file.read_text(encoding="ascii"))
tmp_tex = tmpdir_path / "template.tex"
tmp_tex.write_text(rendered, encoding="ascii")
try:
subprocess.run(
["pdflatex", "-interaction=nonstopmode", "-halt-on-error", tmp_tex.name],
check=True,
cwd=tmpdir_path,
stdout=subprocess.DEVNULL,
stderr=subprocess.DEVNULL,
timeout=TIMEOUT_SEC,
)
except (subprocess.CalledProcessError, subprocess.TimeoutExpired) as exc:
failures.append(f"{tex_file}: pdflatex failed ({exc})")
continue
pdf_path = tmpdir_path / "template.pdf"
if not pdf_path.exists():
failures.append(f"{tex_file}: PDF not generated")
continue
try:
pdf_to_jpg(pdf_path, target_jpg, converter)
print(f"Wrote {target_jpg}")
except (subprocess.CalledProcessError, subprocess.TimeoutExpired) as exc:
failures.append(f"{tex_file}: image conversion failed ({exc})")
continue
if failures:
print("\nFailures:")
for failure in failures:
print(f"- {failure}")
raise SystemExit("Template thumbnail generation completed with errors.")
entries = []
for doc_type in sorted(catalog.keys()):
templates = sorted(catalog[doc_type])
if "default" in templates:
templates = ["default"] + [t for t in templates if t != "default"]
entries.append(
f" {{ docType: '{doc_type}', templateCount: {len(templates)}, templates: {templates} }}"
)
FRONTEND_CATALOG.write_text(
"// Auto-generated by generate_template_thumbnails.py\n"
"export type TemplateCatalogEntry = {\n"
" docType: string\n"
" templateCount: number\n"
" templates: string[]\n"
"}\n\n"
"export const templateCatalog: TemplateCatalogEntry[] = [\n"
+ ",\n".join(entries)
+ "\n]\n",
encoding="ascii",
)
print(f"Wrote catalog {FRONTEND_CATALOG}")
if __name__ == "__main__":
main()
@@ -1,86 +0,0 @@
from __future__ import annotations
import json
import os
from typing import Any, Dict, List
from config import STYLE_DB_PATH, TEMPLATE_DB_PATH, VERSIONS_DB_PATH
from latex_utils import clean_generated_latex, extract_layout_hint
def _read_json(path: str) -> Dict[str, Any]:
if not os.path.exists(path):
return {}
try:
with open(path, "r", encoding="utf-8") as handle:
return json.load(handle)
except Exception:
return {}
def _write_json(path: str, data: Dict[str, Any]) -> None:
with open(path, "w", encoding="utf-8") as handle:
json.dump(data, handle, indent=2)
def load_user_style(user_id: str) -> Dict[str, Any]:
"""Load (or bootstrap) an individual user's preferred style."""
data = _read_json(STYLE_DB_PATH)
return data.get(
user_id,
{
"layout_preference": "clean",
"font_preference": "helvet",
"tone": "professional",
"color_accent": "blue",
"last_doc_type": None,
},
)
def save_user_style(user_id: str, style_data: Dict[str, Any]) -> Dict[str, Any]:
"""Persist style preferences for a user."""
all_data = _read_json(STYLE_DB_PATH)
current = all_data.get(user_id, load_user_style(user_id))
current.update(style_data)
all_data[user_id] = current
_write_json(STYLE_DB_PATH, all_data)
return current
def load_user_templates(user_id: str) -> Dict[str, str]:
templates = _read_json(TEMPLATE_DB_PATH)
return templates.get(user_id, {})
def save_user_template(user_id: str, doc_type: str, latex_code: str) -> None:
"""Persist sanitized layout hints per doc type."""
templates = _read_json(TEMPLATE_DB_PATH)
user_templates = templates.get(user_id, {})
sanitized = clean_generated_latex(latex_code)
user_templates[doc_type] = extract_layout_hint(sanitized)
templates[user_id] = user_templates
_write_json(TEMPLATE_DB_PATH, templates)
def load_versions(user_id: str) -> List[Dict[str, Any]]:
data = _read_json(VERSIONS_DB_PATH)
return data.get(user_id, [])
def save_version(user_id: str, entry: Dict[str, Any]) -> None:
data = _read_json(VERSIONS_DB_PATH)
versions = data.get(user_id, [])
versions.insert(0, entry)
data[user_id] = versions[:20]
_write_json(VERSIONS_DB_PATH, data)
__all__ = [
"load_user_style",
"save_user_style",
"load_user_templates",
"save_user_template",
"load_versions",
"save_version",
]
@@ -1,33 +0,0 @@
from __future__ import annotations
from typing import Any, Dict
from storage import load_user_style, save_user_style
def update_style_profile_from_prompt(user_id: str, prompt: str) -> Dict[str, Any]:
"""Simple heuristics to remember color/font/tone preferences from prompt text."""
style = load_user_style(user_id)
lower = (prompt or "").lower()
if "modern" in lower or "minimal" in lower:
style["layout_preference"] = "modern"
style["tone"] = "minimalist"
if "classic" in lower or "formal" in lower:
style["layout_preference"] = "classic"
style["tone"] = "formal"
if "serif" in lower:
style["font_preference"] = "serif"
if "sans" in lower:
style["font_preference"] = "helvet"
if "blue" in lower:
style["color_accent"] = "blue"
if "red" in lower:
style["color_accent"] = "red"
if "green" in lower:
style["color_accent"] = "green"
return save_user_style(user_id, style)
__all__ = ["update_style_profile_from_prompt"]
@@ -1,52 +0,0 @@
from __future__ import annotations
from typing import Any, List, Optional
import time
from config import CLIENT_MODE, SMART_MODEL, get_chat_model, logger
from langchain_utils import to_lc_messages
from prompts import vision_layout_system_prompt
def vision_layout_from_images(image_urls: List[str], doc_type: str) -> Optional[str]:
"""Call the multimodal model to recover a LaTeX skeleton from page images."""
if CLIENT_MODE != "langchain" or not image_urls:
return None
system_prompt = vision_layout_system_prompt()
user_content: List[Any] = [
{"type": "text", "text": f"Extract layout for document type: {doc_type}. Return LaTeX skeleton only."}
]
for url in image_urls:
user_content.append({"type": "image_url", "image_url": {"url": url}})
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_content},
]
try:
logger.info("[IMPORT] vision call pages=%s doc_type=%s", len(image_urls), doc_type)
llm = get_chat_model(SMART_MODEL, max_tokens=2800)
if not llm:
return None
start = time.perf_counter()
response = llm.invoke(to_lc_messages(messages))
elapsed = time.perf_counter() - start
content = response.content or ""
usage = getattr(response, "usage_metadata", None)
logger.info(
"[IMPORT] vision model=%s elapsed=%.2fs chars=%s usage=%s",
SMART_MODEL,
elapsed,
len(str(content)),
usage,
)
return response.content
except Exception as exc:
logger.error("[IMPORT] vision generation failed: %s", exc)
return None
__all__ = ["vision_layout_from_images"]
@@ -1,36 +0,0 @@
module.exports = {
root: true,
env: {
browser: true,
es2021: true,
},
extends: [
'eslint:recommended',
'plugin:react/recommended',
'plugin:react-hooks/recommended',
'plugin:@typescript-eslint/recommended',
'prettier',
],
parser: '@typescript-eslint/parser',
parserOptions: {
ecmaVersion: 'latest',
sourceType: 'module',
ecmaFeatures: {
jsx: true,
},
jsxPragma: null,
},
plugins: ['react', 'react-hooks', '@typescript-eslint'],
settings: {
react: {
version: 'detect',
runtime: 'automatic',
},
},
ignorePatterns: ['dist', 'node_modules'],
rules: {
'react/prop-types': 'off',
'react/react-in-jsx-scope': 'off',
'@typescript-eslint/no-explicit-any': 'off',
},
}
@@ -1,13 +0,0 @@
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<link rel="icon" type="image/x-icon" href="/favicon.ico" />
<title>Stirling - Intelligent Document 1.0</title>
</head>
<body>
<div id="root"></div>
<script type="module" src="/src/main.tsx"></script>
</body>
</html>
File diff suppressed because it is too large Load Diff
@@ -1,34 +0,0 @@
{
"name": "latex-pdf-generator-frontend",
"version": "1.0.0",
"type": "module",
"scripts": {
"dev": "vite",
"build": "vite build",
"preview": "vite preview",
"lint": "eslint \"src/**/*.{ts,tsx}\"",
"typecheck": "tsc --noEmit"
},
"dependencies": {
"@xenova/transformers": "^2.17.0",
"pdfjs-dist": "^4.10.38",
"pdf-lib": "^1.17.1",
"react": "^18.2.0",
"react-dom": "^18.2.0"
},
"devDependencies": {
"@types/react": "^18.2.43",
"@types/react-dom": "^18.2.17",
"@typescript-eslint/eslint-plugin": "^6.14.0",
"@typescript-eslint/parser": "^6.14.0",
"@vitejs/plugin-react": "^4.2.1",
"autoprefixer": "^10.4.16",
"eslint": "^8.56.0",
"eslint-config-prettier": "^9.0.0",
"eslint-plugin-react": "^7.34.1",
"eslint-plugin-react-hooks": "^4.6.0",
"postcss": "^8.4.32",
"tailwindcss": "^3.4.0",
"vite": "^5.0.8"
}
}
@@ -1,6 +0,0 @@
export default {
plugins: {
tailwindcss: {},
autoprefixer: {},
},
}
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@@ -1,134 +0,0 @@
import { LandingView } from './components/landing/LandingView'
import { ImportLayoutModal } from './components/modals/ImportLayoutModal'
import { WorkspaceView } from './components/workspace/WorkspaceView'
import { useDocumentWorkflow } from './hooks/useDocumentWorkflow'
import { useSpeechCapture } from './hooks/useSpeechCapture'
function App() {
const workflow = useDocumentWorkflow()
const speech = useSpeechCapture({ appendPrompt: workflow.appendPrompt })
const importModal = (
<ImportLayoutModal
isOpen={workflow.showImportModal}
docType={workflow.importDocType}
onDocTypeChange={workflow.setImportDocType}
onClose={() => workflow.setShowImportModal(false)}
onFileSelected={workflow.handleImportTemplate}
isImporting={workflow.isImporting}
status={workflow.importStatus}
/>
)
if (workflow.view === 'landing') {
return (
<>
<LandingView
prompt={workflow.prompt}
onPromptChange={workflow.setPrompt}
onSubmit={workflow.handleInitialSubmit}
onKeyDown={workflow.handleKeyDown}
uploadedPdfFile={workflow.uploadedPdfFile}
onFileSelect={workflow.setUploadedPdfFile}
isImporting={workflow.isImporting}
onToggleRecording={speech.toggleRecording}
onCancelRecording={speech.cancelRecording}
onAcceptRecording={speech.acceptRecording}
isRecording={speech.isRecording}
whisperStatus={speech.whisperStatus}
waveformHistory={speech.waveformHistory}
docTypes={workflow.docTypes}
templateCounts={workflow.templateCounts}
templateCatalog={workflow.templateCatalog}
selectedDocType={workflow.selectedDocType}
selectedTemplateId={workflow.selectedTemplateId}
templatesForSelected={workflow.templatesForSelected}
isTemplateLoading={workflow.isTemplateLoading}
isTemplatePanelOpen={workflow.isTemplatePanelOpen}
onToggleTemplatePanel={() =>
workflow.setIsTemplatePanelOpen(!workflow.isTemplatePanelOpen)
}
onSelectTemplate={workflow.applyTemplateSelection}
templateThumbnailUrl={workflow.templateThumbnailUrl}
formatDocLabel={(value: string) =>
value
.split('_')
.map((part) => part.charAt(0).toUpperCase() + part.slice(1))
.join(' ')
}
/>
{importModal}
</>
)
}
return (
<>
<WorkspaceView
isGenerating={workflow.isGenerating}
isLivePreviewing={workflow.isLivePreviewing}
isStageLoading={workflow.isStageLoading}
prompt={workflow.prompt}
onPromptChange={workflow.setPrompt}
onChatSubmit={workflow.handleChatSubmit}
onKeyDown={workflow.handleKeyDown}
currentDoc={workflow.currentDoc}
onBack={() => workflow.setView('landing')}
stage={workflow.stage}
outlineRows={workflow.outlineRows}
outlineSections={workflow.outlineSections}
excludedFields={workflow.excludedFields}
outlineConstraints={workflow.outlineConstraints}
draftRows={workflow.draftRows}
setOutlineRows={workflow.setOutlineRows}
setOutlineSections={workflow.setOutlineSections}
setExcludedFields={workflow.setExcludedFields}
setOutlineConstraints={workflow.setOutlineConstraints}
setDraftRows={workflow.setDraftRows}
docTypes={workflow.docTypes}
templateCounts={workflow.templateCounts}
selectedDocType={workflow.selectedDocType}
selectedTemplateId={workflow.selectedTemplateId}
templatesForSelected={workflow.templatesForSelected}
isTemplateLoading={workflow.isTemplateLoading}
onSelectTemplate={workflow.applyTemplateSelection}
templateThumbnailUrl={workflow.templateThumbnailUrl}
approveOutline={workflow.approveOutline}
onAiOutline={() => {
workflow.fillFieldsFromAI()
}}
approveDraft={workflow.approveDraft}
saveAndReview={workflow.saveAndReview}
styleDraft={workflow.styleDraft}
setStyleDraft={workflow.setStyleDraft}
applyStyleAndRegenerate={workflow.applyStyleAndRegenerate}
onAddPromptInfo={workflow.addPromptForFields}
onStageSelect={(nextStage) => {
if (workflow.isGenerating || workflow.isStageLoading) return
if (nextStage === 'text' && workflow.stage === 'outline') {
workflow.approveOutline()
return
}
if (nextStage === 'styling' && workflow.stage === 'text') {
workflow.approveDraft()
return
}
if (nextStage === 'review' && workflow.stage === 'styling') {
workflow.saveAndReview()
return
}
workflow.setStage(nextStage)
}}
imagePlaceholdersCount={workflow.imagePlaceholdersCount}
isAssetUploading={workflow.isAssetUploading}
assetError={workflow.assetError}
onAddPlaceholderImage={workflow.addImageToPlaceholders}
onRemovePlaceholders={workflow.stripImagePlaceholders}
onOpenImportTemplate={() => workflow.openImportTemplate(workflow.selectedDocType)}
/>
{importModal}
</>
)
}
export default App
@@ -1,360 +0,0 @@
import { FormEvent, KeyboardEvent, useMemo, useState } from 'react'
import { AudioWaveform } from '../ui/AudioWaveform'
interface LandingViewProps {
prompt: string
onPromptChange: (value: string) => void
onSubmit: (event: FormEvent<HTMLFormElement>) => void
onKeyDown: (event: KeyboardEvent<HTMLTextAreaElement>) => void
uploadedPdfFile: File | null
onFileSelect: (file: File | null) => void
isImporting: boolean
onToggleRecording: () => void
onCancelRecording: () => void
onAcceptRecording: () => void
isRecording: boolean
whisperStatus: string | null
waveformHistory: number[][]
docTypes: string[]
templateCounts: Record<string, number>
templateCatalog: { docType: string; templateCount: number; templates: string[] }[]
selectedDocType: string
selectedTemplateId: string
templatesForSelected: string[]
isTemplateLoading: boolean
isTemplatePanelOpen: boolean
onToggleTemplatePanel: () => void
onSelectTemplate: (docType: string, templateId: string) => void
templateThumbnailUrl: (docType: string, templateId: string) => string
formatDocLabel: (value: string) => string
}
export function LandingView({
prompt,
onPromptChange,
onSubmit,
onKeyDown,
uploadedPdfFile,
onFileSelect,
isImporting,
onToggleRecording,
onCancelRecording,
onAcceptRecording,
isRecording,
whisperStatus,
waveformHistory,
docTypes,
templateCounts,
templateCatalog,
selectedDocType,
selectedTemplateId,
templatesForSelected,
isTemplateLoading,
isTemplatePanelOpen,
onToggleTemplatePanel,
onSelectTemplate,
templateThumbnailUrl,
formatDocLabel,
}: LandingViewProps) {
const [templateSearch, setTemplateSearch] = useState('')
const [activeTemplateTab, setActiveTemplateTab] = useState<'popular' | 'legal' | 'financial' | 'academic' | 'marketing' | 'operations'>('popular')
const [expandedDocType, setExpandedDocType] = useState<string | null>(null)
const [hoveredTemplate, setHoveredTemplate] = useState<{ docType: string; templateId: string; x: number; y: number } | null>(null)
const popularDocTypes = new Set(['cvs_and_resumes', 'invoices', 'cover_letters', 'business_reports', 'presentations'])
const legalDocTypes = new Set(['formal_letters', 'theses'])
const financialDocTypes = new Set(['invoices', 'business_reports', 'calendars'])
const academicDocTypes = new Set(['academic_articles', 'academic_journals', 'assignments', 'theses'])
const marketingDocTypes = new Set(['newsletters', 'signs', 'presentations'])
const operationsDocTypes = new Set(['laboratory_reports', 'laboratory_books', 'business_reports'])
const visibleDocTypes = useMemo(() => {
const filtered = docTypes.filter((docType) =>
formatDocLabel(docType).toLowerCase().includes(templateSearch.toLowerCase()),
)
if (activeTemplateTab === 'legal') {
return filtered.filter((docType) => legalDocTypes.has(docType))
}
if (activeTemplateTab === 'financial') {
return filtered.filter((docType) => financialDocTypes.has(docType))
}
if (activeTemplateTab === 'academic') {
return filtered.filter((docType) => academicDocTypes.has(docType))
}
if (activeTemplateTab === 'marketing') {
return filtered.filter((docType) => marketingDocTypes.has(docType))
}
if (activeTemplateTab === 'operations') {
return filtered.filter((docType) => operationsDocTypes.has(docType))
}
return filtered.filter((docType) => popularDocTypes.has(docType))
}, [
academicDocTypes,
activeTemplateTab,
docTypes,
financialDocTypes,
formatDocLabel,
legalDocTypes,
marketingDocTypes,
operationsDocTypes,
popularDocTypes,
templateSearch,
])
const docTypeIcon = (docType: string) => {
switch (docType) {
case 'cvs_and_resumes':
return '📄'
case 'invoices':
return '🧾'
case 'cover_letters':
return '✉️'
case 'business_reports':
return '📊'
case 'formal_letters':
return '📝'
case 'theses':
return '🎓'
case 'presentations':
return '🖥️'
case 'recipes':
return '🍲'
default:
return '📁'
}
}
return (
<div className="min-h-screen bg-gradient-to-b from-slate-50 to-slate-100 text-slate-900">
<header className="flex items-center justify-between px-8 py-6">
<div className="flex items-center gap-2 text-sm font-semibold text-slate-700">
<span className="text-base">Stirling</span>
</div>
<div className="flex items-center gap-2">
<button className="rounded-full border border-slate-200 px-4 py-1.5 text-xs text-slate-500">
Log in
</button>
<button className="rounded-full bg-slate-900 px-4 py-1.5 text-xs text-white">
Get Stirling free
</button>
</div>
</header>
<main className="flex flex-1 items-center justify-center px-6 pb-16">
<div className="w-full max-w-4xl rounded-[32px] border border-slate-200 bg-white p-10 shadow-lg">
<div className="flex flex-col items-center text-center gap-4">
<div className="text-3xl font-semibold text-slate-900">Stirling PDF</div>
<p className="text-sm text-slate-500">Create any PDF you can imagine with AI</p>
</div>
<form onSubmit={onSubmit} className="mt-10">
<div className="rounded-2xl border border-slate-200 bg-white px-5 py-4 shadow-sm">
{isRecording ? (
<div className="space-y-3">
<AudioWaveform history={waveformHistory} />
<div className="flex items-center justify-between text-xs text-slate-500">
<span>{whisperStatus || 'Listening...'}</span>
<div className="flex gap-2">
<button
type="button"
className="rounded-full border border-slate-200 px-3 py-1"
onClick={onCancelRecording}
>
Cancel
</button>
<button
type="button"
className="rounded-full bg-blue-600 px-3 py-1 text-white"
onClick={onAcceptRecording}
>
Accept
</button>
</div>
</div>
</div>
) : (
<>
<textarea
className="w-full resize-none text-sm text-slate-700 placeholder:text-slate-400 focus:outline-none"
rows={1}
value={prompt}
onChange={(event) => onPromptChange(event.target.value)}
onKeyDown={onKeyDown}
placeholder="Make an invoice for me to bill a client for $1500 in consulting fees"
/>
{uploadedPdfFile && (
<div className="mt-2 text-xs text-emerald-600">{uploadedPdfFile.name}</div>
)}
<div className="mt-4 flex items-center justify-between">
<div className="flex items-center gap-2 relative">
<label
htmlFor="pdf-upload-landing"
className="flex h-8 w-8 items-center justify-center rounded-full border border-slate-200 text-slate-400"
>
+
</label>
<input
id="pdf-upload-landing"
type="file"
accept="application/pdf"
onChange={(event) => onFileSelect(event.target.files?.[0] || null)}
className="hidden"
/>
<button
type="button"
className="rounded-full border border-slate-200 px-3 py-1 text-xs text-slate-500"
onClick={onToggleTemplatePanel}
>
Template
</button>
<span className="text-xs text-slate-400">
{formatDocLabel(selectedDocType)} · {formatDocLabel(selectedTemplateId)}
</span>
{isTemplatePanelOpen && (
<div className="absolute left-2 top-10 w-80 rounded-2xl border border-slate-200 bg-white p-4 shadow-lg z-50">
<div className="absolute -top-2 left-6 h-3 w-3 rotate-45 border border-slate-200 bg-white" />
<div className="space-y-3">
<div className="flex items-center gap-2 rounded-full border border-slate-200 px-3 py-2 text-xs text-slate-500">
<span>🔎</span>
<input
className="w-full bg-transparent text-sm text-slate-600 focus:outline-none"
placeholder="Search templates..."
value={templateSearch}
onChange={(event) => setTemplateSearch(event.target.value)}
/>
</div>
<div className="flex gap-2 overflow-x-auto pb-1">
{[
{ id: 'popular', label: 'Popular' },
{ id: 'legal', label: 'Legal' },
{ id: 'financial', label: 'Financial' },
{ id: 'academic', label: 'Academic' },
{ id: 'marketing', label: 'Marketing' },
{ id: 'operations', label: 'Operations' },
].map((tab) => (
<button
type="button"
key={tab.id}
onClick={() => setActiveTemplateTab(tab.id as typeof activeTemplateTab)}
className={`rounded-full px-3 py-1 text-xs ${
activeTemplateTab === tab.id
? 'bg-blue-100 text-blue-700'
: 'bg-slate-100 text-slate-500'
}`}
>
{tab.label}
</button>
))}
</div>
</div>
<div className="mt-4 space-y-2 max-h-64 overflow-y-auto pr-1 relative">
{visibleDocTypes.map((docType) => {
const isExpanded = expandedDocType === docType
const templates =
templateCatalog.find((entry) => entry.docType === docType)?.templates ||
(docType === selectedDocType ? templatesForSelected : ['default'])
return (
<div key={docType} className="rounded-lg border border-slate-200 bg-white">
<div
className="flex w-full items-center justify-between px-3 py-2 text-sm text-slate-700"
>
<button
type="button"
className="flex flex-1 items-center gap-2 text-left"
onClick={() => onSelectTemplate(docType, 'default')}
>
<span>{docTypeIcon(docType)}</span>
<span>{formatDocLabel(docType)}</span>
</button>
<button
type="button"
className="text-slate-400 px-2"
onClick={() => setExpandedDocType(isExpanded ? null : docType)}
>
{isExpanded ? '▾' : '▸'}
</button>
</div>
{isExpanded && (
<div className="border-t border-slate-200 px-3 py-2 space-y-2">
{(templates || ['default']).map((templateId) => (
<button
type="button"
key={`${docType}-${templateId}`}
onClick={() => onSelectTemplate(docType, templateId)}
className={`flex w-full items-center justify-between rounded-md px-2 py-1 text-sm ${
selectedDocType === docType && selectedTemplateId === templateId
? 'bg-blue-50 text-blue-700'
: 'text-slate-600'
}`}
onMouseMove={(event) =>
setHoveredTemplate({
docType,
templateId,
x: event.clientX,
y: event.clientY,
})
}
onMouseLeave={() => setHoveredTemplate(null)}
>
<span>{formatDocLabel(templateId)}</span>
<span className="text-xs text-slate-400">Select</span>
</button>
))}
</div>
)}
</div>
)
})}
{!visibleDocTypes.length && (
<div className="text-sm text-slate-400">No templates found.</div>
)}
{hoveredTemplate && (
<div
className="fixed z-50 w-36 rounded-lg border border-slate-200 bg-white shadow-lg p-2"
style={{ left: hoveredTemplate.x + 12, top: hoveredTemplate.y + 12 }}
>
<div className="text-[10px] uppercase tracking-wide text-slate-400 mb-2">
Preview
</div>
<div className="w-full bg-slate-50 rounded-md overflow-hidden" style={{ aspectRatio: '210 / 297' }}>
<img
src={templateThumbnailUrl(hoveredTemplate.docType, hoveredTemplate.templateId)}
alt={`${hoveredTemplate.docType} ${hoveredTemplate.templateId} preview`}
className="h-full w-full object-contain"
/>
</div>
</div>
)}
</div>
</div>
)}
</div>
<div className="flex items-center gap-2">
<button
type="button"
className="flex h-8 w-8 items-center justify-center rounded-full border border-slate-200 text-slate-500"
onClick={onToggleRecording}
aria-label="Voice input"
>
🎤
</button>
<button
type="submit"
disabled={isImporting || (!prompt.trim() && !uploadedPdfFile)}
className="flex h-8 w-8 items-center justify-center rounded-full bg-slate-900 text-white disabled:opacity-40"
aria-label="Generate"
>
</button>
</div>
</div>
</>
)}
</div>
</form>
</div>
</main>
</div>
)
}
@@ -1,75 +0,0 @@
interface ImportLayoutModalProps {
isOpen: boolean
docType: string
onDocTypeChange: (value: string) => void
onClose: () => void
onFileSelected: (file: File) => void
isImporting: boolean
status: string | null
}
export function ImportLayoutModal({
isOpen,
docType,
onDocTypeChange,
onClose,
onFileSelected,
isImporting,
status,
}: ImportLayoutModalProps) {
if (!isOpen) return null
return (
<div className="fixed inset-0 bg-slate-900/70 backdrop-blur-sm z-50 flex items-center justify-center p-4">
<div className="bg-slate-900 border border-slate-700 rounded-xl shadow-2xl w-full max-w-md p-5 space-y-4">
<div className="flex items-center justify-between">
<div>
<div className="text-sm text-slate-300">Import layout from PDF</div>
<div className="text-xs text-slate-500">First 2 pages only, uses vision model</div>
</div>
<button onClick={onClose} className="text-slate-400 hover:text-white text-sm">
</button>
</div>
<div className="space-y-2">
<label className="text-xs text-slate-400">Document type</label>
<input
type="text"
value={docType}
onChange={(e) => onDocTypeChange(e.target.value)}
className="w-full bg-slate-800 border border-slate-700 rounded-lg px-3 py-2 text-slate-100 text-sm"
placeholder="invoice, resume, report..."
/>
</div>
<div className="space-y-2">
<label className="text-xs text-slate-400">PDF file</label>
<input
type="file"
accept="application/pdf"
onChange={(e) => {
const file = e.target.files?.[0]
if (file) onFileSelected(file)
}}
className="w-full text-sm text-slate-300"
disabled={isImporting}
/>
</div>
{status && (
<div className="text-xs text-slate-300 bg-slate-800 border border-slate-700 rounded-lg px-3 py-2">
{status}
</div>
)}
<div className="flex items-center justify-end gap-3 text-xs text-slate-400">
<button onClick={onClose} className="px-3 py-1 rounded-lg border border-slate-700 hover:bg-slate-800">
Close
</button>
{isImporting && <span>Processing...</span>}
</div>
</div>
</div>
)
}
@@ -1,223 +0,0 @@
import { useCallback, useEffect, useMemo, useState } from 'react'
import { PDFDocument, rgb, StandardFonts } from 'pdf-lib'
import { GlobalWorkerOptions, getDocument } from 'pdfjs-dist'
GlobalWorkerOptions.workerSrc = new URL('pdfjs-dist/build/pdf.worker.min.mjs', import.meta.url).toString()
type PageTextItem = {
id: string
str: string
x: number
y: number
width: number
fontSize: number
}
type PageData = {
width: number
height: number
image: string
items: PageTextItem[]
}
interface PdfTextEditorLiteProps {
pdfUrl: string
onClose: () => void
}
export function PdfTextEditorLite({ pdfUrl, onClose }: PdfTextEditorLiteProps) {
const [pages, setPages] = useState<PageData[]>([])
const [loading, setLoading] = useState(false)
const [saving, setSaving] = useState(false)
const [error, setError] = useState<string | null>(null)
const [editedItems, setEditedItems] = useState<Record<string, string>>({})
const loadPdf = useCallback(async () => {
setLoading(true)
setError(null)
try {
const resp = await fetch(pdfUrl)
const buffer = await resp.arrayBuffer()
const pdf = await getDocument({ data: buffer }).promise
const loaded: PageData[] = []
for (let i = 1; i <= pdf.numPages; i += 1) {
const page = await pdf.getPage(i)
const viewport = page.getViewport({ scale: 1.2 })
// Render page to image
const canvas = document.createElement('canvas')
const ctx = canvas.getContext('2d')
if (!ctx) continue
canvas.width = viewport.width
canvas.height = viewport.height
await page.render({ canvasContext: ctx, viewport }).promise
const image = canvas.toDataURL('image/png')
// Extract text items
const textContent = await page.getTextContent()
const items: PageTextItem[] = textContent.items
.map((raw, idx) => {
const it = raw as any
const transform: number[] = Array.isArray(it?.transform) ? it.transform : [1, 0, 0, 1, 0, 0]
const [a, b, , , e, f] = transform
const x = typeof e === 'number' ? e : 0
const y = typeof f === 'number' ? f : 0
const fontSize = Math.hypot(a || 0, b || 0) || it?.height || 12
const width = ((it?.width as number | undefined) || fontSize) * viewport.scale
const str = typeof it?.str === 'string' ? it.str : ''
return { id: `${i}-${idx}`, str, x, y, width, fontSize }
})
.filter((it) => it.str.length > 0)
loaded.push({
width: viewport.width,
height: viewport.height,
image,
items,
})
}
setPages(loaded)
const initialEdits: Record<string, string> = {}
loaded.forEach((p) =>
p.items.forEach((it) => {
initialEdits[it.id] = it.str
}),
)
setEditedItems(initialEdits)
} catch (e: any) {
setError(e?.message || 'Failed to load PDF')
} finally {
setLoading(false)
}
}, [pdfUrl])
useEffect(() => {
loadPdf()
}, [loadPdf])
const handleChange = (id: string, value: string) => {
setEditedItems((prev) => ({ ...prev, [id]: value }))
}
const handleDownload = useCallback(async () => {
setSaving(true)
setError(null)
try {
const doc = await PDFDocument.create()
const font = await doc.embedFont(StandardFonts.Helvetica)
for (const page of pages) {
const pdfPage = doc.addPage([page.width, page.height])
const bg = await doc.embedPng(page.image)
pdfPage.drawImage(bg, { x: 0, y: 0, width: page.width, height: page.height })
page.items.forEach((item) => {
const text = editedItems[item.id] ?? item.str
const yPdf = page.height - item.y // flip coordinate to bottom-left origin
pdfPage.drawText(text, {
x: item.x,
y: yPdf - item.fontSize,
size: item.fontSize || 12,
font,
color: rgb(0, 0, 0),
})
})
}
const bytes = await doc.save()
const arrayBuffer = new ArrayBuffer(bytes.byteLength)
new Uint8Array(arrayBuffer).set(bytes)
const blob = new Blob([arrayBuffer], { type: 'application/pdf' })
const url = URL.createObjectURL(blob)
const a = document.createElement('a')
a.href = url
a.download = 'edited.pdf'
a.click()
URL.revokeObjectURL(url)
} catch (e: any) {
setError(e?.message || 'Failed to generate PDF')
} finally {
setSaving(false)
}
}, [editedItems, pages])
const pageCount = useMemo(() => pages.length, [pages])
return (
<div className="w-full h-full flex flex-col bg-slate-950">
<div className="flex items-center justify-between px-4 py-3 border-b border-slate-800 bg-slate-900">
<div className="flex items-center gap-3">
<h3 className="text-slate-100 font-semibold">Text Editor (lite)</h3>
<span className="text-xs text-slate-400">Pages: {pageCount || '—'}</span>
</div>
<div className="flex items-center gap-2">
<button
onClick={handleDownload}
disabled={saving || loading || pages.length === 0}
className="px-3 py-1.5 rounded bg-blue-600 text-white text-sm disabled:opacity-50"
>
{saving ? 'Saving…' : 'Download edited PDF'}
</button>
<button
onClick={onClose}
className="px-3 py-1.5 rounded bg-slate-700 text-slate-100 text-sm hover:bg-slate-600"
>
Back to preview
</button>
</div>
</div>
{error && <div className="px-4 py-2 text-sm text-amber-200 bg-amber-500/10 border border-amber-600">{error}</div>}
<div className="flex-1 overflow-y-auto p-4 space-y-8">
{loading && <div className="text-slate-300 text-sm">Loading PDF</div>}
{!loading &&
pages.map((page, pageIdx) => (
<div
key={page.image}
className="relative bg-slate-900 border border-slate-800 rounded-lg p-2 shadow"
style={{ width: page.width, minHeight: page.height }}
>
<div className="absolute inset-2">
<img
src={page.image}
alt={`Page ${pageIdx + 1}`}
className="w-full h-auto rounded border border-slate-800 shadow pointer-events-none select-none"
/>
<div className="absolute inset-0">
{page.items.map((item) => {
const yTop = page.height - item.y
return (
<div
key={item.id}
contentEditable
suppressContentEditableWarning
onInput={(e) => handleChange(item.id, (e.target as HTMLDivElement).innerText)}
className="absolute bg-transparent outline-none focus:ring-1 focus:ring-blue-400 rounded px-0.5"
style={{
left: item.x,
top: yTop - item.fontSize * 0.85,
minWidth: Math.max(item.width, 4),
fontSize: item.fontSize,
lineHeight: '1.05',
color: '#111827',
}}
>
{editedItems[item.id] ?? item.str}
</div>
)
})}
</div>
</div>
</div>
))}
{!loading && pages.length === 0 && <div className="text-slate-400 text-sm">No pages loaded.</div>}
</div>
</div>
)
}
export default PdfTextEditorLite
@@ -1,223 +0,0 @@
import { useEffect, useState } from 'react'
import { GlobalWorkerOptions, getDocument } from 'pdfjs-dist'
GlobalWorkerOptions.workerSrc = new URL('pdfjs-dist/build/pdf.worker.min.mjs', import.meta.url).toString()
type ThumbnailPage = {
id: string
dataUrl: string
width: number
height: number
}
function PdfPreviewSkeleton() {
return (
<div className="flex justify-center py-8">
<div
className="relative rounded-2xl border border-slate-300/60 bg-white shadow-2xl overflow-hidden"
style={{
width: '860px',
maxWidth: '90vw',
aspectRatio: '1 / 1.414',
}}
>
<div className="h-12 bg-slate-100 border-b border-slate-200 animate-pulse" />
<div className="p-8 space-y-4">
{[1, 2, 3, 4].map((line) => (
<div
key={`line-top-${line}`}
className="h-4 rounded-full bg-slate-200 animate-pulse"
style={{ width: `${78 - line * 10}%` }}
/>
))}
<div className="h-40 rounded-xl bg-slate-100 border border-slate-200 animate-pulse" />
{[5, 6, 7].map((line) => (
<div
key={`line-bottom-${line}`}
className="h-4 rounded-full bg-slate-200 animate-pulse"
style={{ width: `${70 - (line - 5) * 8}%` }}
/>
))}
</div>
</div>
</div>
)
}
interface PdfThumbnailViewerProps {
pdfUrl: string
isLivePreviewing?: boolean
}
export function PdfThumbnailViewer({ pdfUrl, isLivePreviewing = false }: PdfThumbnailViewerProps) {
const [pages, setPages] = useState<ThumbnailPage[]>([])
const [loading, setLoading] = useState(false)
const [error, setError] = useState<string | null>(null)
const [zoom, setZoom] = useState(1)
const [hasRenderedPage, setHasRenderedPage] = useState(false)
useEffect(() => {
if (!pdfUrl) {
setPages([])
setHasRenderedPage(false)
return
}
let cancelled = false
const controller = new AbortController()
const load = async () => {
setLoading(true)
setError(null)
try {
const response = await fetch(pdfUrl, { signal: controller.signal })
if (!response.ok) {
throw new Error('Unable to download PDF preview')
}
const buffer = await response.arrayBuffer()
const pdf = await getDocument({ data: buffer }).promise
const next: ThumbnailPage[] = []
try {
for (let pageNumber = 1; pageNumber <= pdf.numPages; pageNumber += 1) {
const page = await pdf.getPage(pageNumber)
const viewport = page.getViewport({ scale: 0.85 })
const canvas = document.createElement('canvas')
const ctx = canvas.getContext('2d')
if (ctx) {
canvas.width = viewport.width
canvas.height = viewport.height
await page.render({ canvasContext: ctx, viewport }).promise
next.push({
id: `page-${pageNumber}`,
dataUrl: canvas.toDataURL('image/png'),
width: viewport.width,
height: viewport.height,
})
canvas.width = 0
canvas.height = 0
}
page.cleanup?.()
}
} finally {
try {
pdf.cleanup?.()
await pdf.destroy?.()
} catch {
// ignore cleanup errors
}
}
if (!cancelled) {
setPages(next)
if (next.length > 0) {
setHasRenderedPage(true)
}
}
} catch (err) {
if (!cancelled) {
const message = err instanceof Error ? err.message : 'Failed to load PDF preview'
const normalized = message.toLowerCase()
if (
isLivePreviewing &&
(normalized.includes('zero bytes') || normalized.includes('file is empty') || normalized.includes('pdf is empty'))
) {
setError(null)
} else {
setError(message)
}
}
} finally {
if (!cancelled) {
setLoading(false)
}
}
}
load()
return () => {
cancelled = true
controller.abort()
}
}, [pdfUrl, isLivePreviewing])
const shouldShowSkeleton = (loading && !hasRenderedPage && pages.length === 0) || (isLivePreviewing && pages.length === 0 && !error && !hasRenderedPage)
return (
<div className="flex h-full flex-col bg-slate-900">
{error && (
<div className="border-b border-amber-700 bg-amber-500/15 px-4 py-3 text-sm text-amber-200">
{error}
</div>
)}
<div className="relative flex-1 overflow-y-auto px-6 py-6">
<div className="sticky top-0 z-10 mb-4 flex items-center justify-end gap-3 rounded-lg border border-slate-800 bg-slate-850/80 px-3 py-2 backdrop-blur">
<span className="text-xs text-slate-300">Zoom</span>
<div className="flex items-center gap-2">
<button
type="button"
onClick={() => setZoom((z) => Math.max(0.5, Math.round((z - 0.1) * 100) / 100))}
className="h-8 w-8 rounded border border-slate-700 bg-slate-800 text-slate-200 hover:bg-slate-750"
>
</button>
<input
type="range"
min={0.5}
max={2}
step={0.05}
value={zoom}
onChange={(e) => setZoom(Number(e.target.value))}
className="h-2 w-32 accent-blue-500"
/>
<button
type="button"
onClick={() => setZoom((z) => Math.min(2, Math.round((z + 0.1) * 100) / 100))}
className="h-8 w-8 rounded border border-slate-700 bg-slate-800 text-slate-200 hover:bg-slate-750"
>
+
</button>
<span className="w-12 text-right text-xs text-slate-300">{Math.round(zoom * 100)}%</span>
</div>
</div>
{shouldShowSkeleton && <PdfPreviewSkeleton />}
{!shouldShowSkeleton && pages.length === 0 && (
<div className="flex h-full items-center justify-center text-sm text-slate-400">Preview unavailable.</div>
)}
{!shouldShowSkeleton && pages.length > 0 && (
<div className="mx-auto flex max-w-5xl flex-col items-center gap-8">
{pages.map((page) => {
const baseScale = Math.min(920 / page.width, 1)
const displayScale = baseScale * zoom
const displayWidth = page.width * displayScale
const displayHeight = page.height * displayScale
return (
<div
key={page.id}
className="overflow-hidden rounded-lg border border-slate-800 bg-white shadow-2xl"
style={{
width: displayWidth,
height: displayHeight,
maxWidth: '100%',
}}
>
<img
src={page.dataUrl}
alt={`PDF page ${page.id}`}
className="block h-full w-full select-none object-contain"
/>
</div>
)
})}
</div>
)}
</div>
</div>
)
}
export default PdfThumbnailViewer
@@ -1,59 +0,0 @@
import React from 'react'
type ZoomControlsProps = {
value: number
onChange: (next: number) => void
min?: number
max?: number
step?: number
disabled?: boolean
className?: string
}
const clamp = (value: number, min: number, max: number) => Math.min(Math.max(value, min), max)
const formatPercent = (value: number) => `${Math.round(value * 100)}%`
const ZoomControls: React.FC<ZoomControlsProps> = ({
value,
onChange,
min = 0.2,
max = 3,
step = 0.1,
disabled = false,
className = '',
}) => {
const handleChange = (next: number) => {
if (disabled) return
const clamped = clamp(next, min, max)
onChange(Number(clamped.toFixed(2)))
}
return (
<div className={`flex items-center gap-2 ${className}`}>
<span className="hidden text-xs text-slate-400 sm:inline">Zoom</span>
<button
type="button"
onClick={() => handleChange(value - step)}
disabled={disabled}
className="rounded border border-slate-700 bg-slate-800 px-2 py-1 text-sm text-slate-200 hover:bg-slate-700 disabled:cursor-not-allowed disabled:opacity-50"
>
</button>
<div className="min-w-[56px] rounded border border-slate-700 bg-slate-800 px-2 py-1 text-center text-xs font-medium text-slate-100">
{formatPercent(value)}
</div>
<button
type="button"
onClick={() => handleChange(value + step)}
disabled={disabled}
className="rounded border border-slate-700 bg-slate-800 px-2 py-1 text-sm text-slate-200 hover:bg-slate-700 disabled:cursor-not-allowed disabled:opacity-50"
>
+
</button>
</div>
)
}
export default ZoomControls
@@ -1,421 +0,0 @@
import { PdfJsonDocument, PdfJsonFont } from './pdfTextEditorTypes';
export type FontStatus = 'perfect' | 'embedded-subset' | 'system-fallback' | 'missing' | 'unknown';
export interface FontAnalysis {
fontId: string;
baseName: string;
status: FontStatus;
embedded: boolean;
isSubset: boolean;
isStandard14: boolean;
hasWebFormat: boolean;
webFormat?: string;
subtype?: string;
encoding?: string;
warnings: string[];
suggestions: string[];
}
export interface DocumentFontAnalysis {
fonts: FontAnalysis[];
canReproducePerfectly: boolean;
hasWarnings: boolean;
summary: {
perfect: number;
embeddedSubset: number;
systemFallback: number;
missing: number;
unknown: number;
};
}
/**
* Determines if a font name indicates it's a subset font.
* Subset fonts typically have a 6-character prefix like "ABCDEE+"
*/
const isSubsetFont = (baseName: string | null | undefined): boolean => {
if (!baseName) return false;
// Check for common subset patterns: ABCDEF+FontName
return /^[A-Z]{6}\+/.test(baseName);
};
/**
* Checks if a font is one of the standard 14 PDF fonts that are guaranteed
* to be available on all PDF readers
*/
const isStandard14Font = (font: PdfJsonFont): boolean => {
if (font.standard14Name) return true;
const baseName = (font.baseName || '').toLowerCase().replace(/[-_\s]/g, '');
const standard14Patterns = [
'timesroman', 'timesbold', 'timesitalic', 'timesbolditalic',
'helvetica', 'helveticabold', 'helveticaoblique', 'helveticaboldoblique',
'courier', 'courierbold', 'courieroblique', 'courierboldoblique',
'symbol', 'zapfdingbats'
];
// Check exact matches or if the base name contains the pattern
return standard14Patterns.some(pattern => {
// Exact match
if (baseName === pattern) return true;
// Contains pattern (e.g., "ABCDEF+Helvetica" matches "helvetica")
if (baseName.includes(pattern)) return true;
return false;
});
};
/**
* Checks if a font has a fallback available on the backend.
* These fonts are embedded in the Stirling PDF backend and can be used
* for PDF export even if not in the original PDF.
*
* Based on PdfJsonFallbackFontService.java
*/
const hasBackendFallbackFont = (font: PdfJsonFont): boolean => {
const baseName = (font.baseName || '').toLowerCase().replace(/[-_\s]/g, '');
// Backend has these font families available (from PdfJsonFallbackFontService)
const backendFonts = [
// Liberation fonts (metric-compatible with MS core fonts)
'arial', 'helvetica', 'arimo',
'times', 'timesnewroman', 'tinos',
'courier', 'couriernew', 'cousine',
'liberation', 'liberationsans', 'liberationserif', 'liberationmono',
// DejaVu fonts
'dejavu', 'dejavusans', 'dejavuserif', 'dejavumono', 'dejavusansmono',
// Noto fonts
'noto', 'notosans'
];
return backendFonts.some(pattern => {
if (baseName === pattern) return true;
if (baseName.includes(pattern)) return true;
return false;
});
};
/**
* Extracts the base font name from a subset font name
* e.g., "ABCDEF+Arial" -> "Arial"
*/
const extractBaseFontName = (baseName: string | null | undefined): string | null => {
if (!baseName) return null;
const match = baseName.match(/^[A-Z]{6}\+(.+)$/);
return match ? match[1] : baseName;
};
/**
* Analyzes a single font to determine if it can be reproduced perfectly
* Takes allFonts to check if full versions of subset fonts are available
*/
export const analyzeFontReproduction = (font: PdfJsonFont, allFonts?: PdfJsonFont[]): FontAnalysis => {
const fontId = font.id || font.uid || 'unknown';
const baseName = font.baseName || 'Unknown Font';
const isSubset = isSubsetFont(font.baseName);
const isStandard14 = isStandard14Font(font);
const hasBackendFallback = hasBackendFallbackFont(font);
const embedded = font.embedded ?? false;
// Check available web formats (ordered by preference)
const webFormats = [
{ key: 'webProgram', format: font.webProgramFormat },
{ key: 'pdfProgram', format: font.pdfProgramFormat },
{ key: 'program', format: font.programFormat },
];
const availableWebFormat = webFormats.find(f => f.format);
const hasWebFormat = !!availableWebFormat;
const webFormat = availableWebFormat?.format || undefined;
const warnings: string[] = [];
const suggestions: string[] = [];
let status: FontStatus = 'unknown';
// Check if we have the full font when this is a subset
let hasFullFontVersion = false;
if (isSubset && allFonts) {
const baseFont = extractBaseFontName(font.baseName);
if (baseFont) {
// Look for a non-subset version of this font with a web format
hasFullFontVersion = allFonts.some(f => {
const otherBaseName = extractBaseFontName(f.baseName);
const isNotSubset = !isSubsetFont(f.baseName);
const hasFormat = !!(f.webProgramFormat || f.pdfProgramFormat || f.programFormat);
const sameBase = otherBaseName?.toLowerCase() === baseFont.toLowerCase();
return sameBase && isNotSubset && hasFormat && (f.embedded ?? false);
});
}
}
// Analyze font status - focusing on PDF export quality
if (isStandard14) {
// Standard 14 fonts are always available in PDF readers - perfect for export!
status = 'perfect';
suggestions.push('Standard PDF font (Times, Helvetica, or Courier). Always available in PDF readers.');
suggestions.push('Exported PDFs will render consistently across all PDF readers.');
} else if (embedded && !isSubset) {
// Perfect: Fully embedded with complete character set
status = 'perfect';
suggestions.push('Font is fully embedded. Exported PDFs will reproduce text perfectly, even with edits.');
} else if (embedded && isSubset && (hasFullFontVersion || hasBackendFallback)) {
// Subset but we have the full font or backend fallback - perfect!
status = 'perfect';
if (hasFullFontVersion) {
suggestions.push('Full font version is also available in the document. Exported PDFs can reproduce all characters.');
} else if (hasBackendFallback) {
suggestions.push('Backend has the full font available. Exported PDFs can reproduce all characters, including new text.');
}
} else if (embedded && isSubset) {
// Good, but subset: May have missing characters if user adds new text
status = 'embedded-subset';
warnings.push('This is a subset font - only specific characters are embedded in the PDF.');
warnings.push('Exported PDFs may have missing characters if you add new text with this font.');
suggestions.push('Existing text will export correctly. New characters may render as boxes (☐) or fallback glyphs.');
} else if (!embedded && hasBackendFallback) {
// Not embedded, but backend has it - perfect for export!
status = 'perfect';
suggestions.push('Backend has this font available. Exported PDFs will use the backend fallback font.');
suggestions.push('Text will export correctly with consistent appearance.');
} else if (!embedded) {
// Not embedded - must rely on system fonts (risky for export)
status = 'missing';
warnings.push('Font is not embedded in the PDF.');
warnings.push('Exported PDFs will substitute with a fallback font, which may look very different.');
suggestions.push('Consider re-embedding fonts or accepting that the exported PDF will use fallback fonts.');
} else if (embedded && !hasWebFormat) {
// Embedded but no web format available (still okay for export)
status = 'perfect';
suggestions.push('Font is embedded in the PDF. Exported PDFs will reproduce correctly.');
suggestions.push('Web preview may use a fallback font, but the final PDF export will be accurate.');
}
// Additional warnings based on font properties
if (font.subtype === 'Type0' && font.cidSystemInfo) {
const registry = font.cidSystemInfo.registry || '';
const ordering = font.cidSystemInfo.ordering || '';
if (registry.includes('Adobe') && (ordering.includes('Identity') || ordering.includes('UCS'))) {
// CID fonts with Identity encoding are common for Asian languages
if (!embedded || !hasWebFormat) {
warnings.push('This CID font may contain Asian or Unicode characters.');
}
}
}
if (font.encoding && !font.encoding.includes('WinAnsiEncoding') && !font.encoding.includes('MacRomanEncoding')) {
// Custom encodings may cause issues
if (font.encoding !== 'Identity-H' && font.encoding !== 'Identity-V') {
warnings.push(`Custom encoding detected: ${font.encoding}`);
}
}
return {
fontId,
baseName,
status,
embedded,
isSubset,
isStandard14,
hasWebFormat,
webFormat,
subtype: font.subtype || undefined,
encoding: font.encoding || undefined,
warnings,
suggestions,
};
};
/**
* Gets fonts used on a specific page
*/
export const getFontsForPage = (
document: PdfJsonDocument | null,
pageIndex: number
): PdfJsonFont[] => {
if (!document?.fonts || !document?.pages || pageIndex < 0 || pageIndex >= document.pages.length) {
return [];
}
const page = document.pages[pageIndex];
if (!page?.textElements) {
return [];
}
// Get unique font IDs used on this page
const fontIdsOnPage = new Set<string>();
page.textElements.forEach(element => {
if (element?.fontId) {
fontIdsOnPage.add(element.fontId);
}
});
// Filter fonts to only those used on this page
const allFonts = document.fonts.filter((font): font is PdfJsonFont => font !== null && font !== undefined);
const fontsOnPage = allFonts.filter(font => {
// Match by ID
if (font.id && fontIdsOnPage.has(font.id)) {
return true;
}
// Match by UID
if (font.uid && fontIdsOnPage.has(font.uid)) {
return true;
}
// Match by page-specific ID (pageNumber:id format)
if (font.pageNumber === pageIndex + 1 && font.id) {
const pageSpecificId = `${font.pageNumber}:${font.id}`;
if (fontIdsOnPage.has(pageSpecificId) || fontIdsOnPage.has(font.id)) {
return true;
}
}
return false;
});
// Deduplicate by base font name to avoid showing the same font multiple times
const uniqueFonts = new Map<string, PdfJsonFont>();
fontsOnPage.forEach(font => {
const baseName = extractBaseFontName(font.baseName) || font.baseName || font.id || 'unknown';
const key = baseName.toLowerCase();
// Keep the first occurrence, or prefer non-subset over subset
const existing = uniqueFonts.get(key);
if (!existing) {
uniqueFonts.set(key, font);
} else {
// Prefer non-subset fonts over subset fonts
const existingIsSubset = isSubsetFont(existing.baseName);
const currentIsSubset = isSubsetFont(font.baseName);
if (existingIsSubset && !currentIsSubset) {
uniqueFonts.set(key, font);
}
}
});
return Array.from(uniqueFonts.values());
};
/**
* Analyzes all fonts in a PDF document (or just fonts for a specific page)
*/
export const analyzeDocumentFonts = (
document: PdfJsonDocument | null,
pageIndex?: number
): DocumentFontAnalysis => {
if (!document?.fonts || document.fonts.length === 0) {
return {
fonts: [],
canReproducePerfectly: true,
hasWarnings: false,
summary: {
perfect: 0,
embeddedSubset: 0,
systemFallback: 0,
missing: 0,
unknown: 0,
},
};
}
const allFonts = document.fonts.filter((font): font is PdfJsonFont => font !== null && font !== undefined);
// Filter to page-specific fonts if pageIndex is provided
const fontsToAnalyze = pageIndex !== undefined
? getFontsForPage(document, pageIndex)
: allFonts;
if (fontsToAnalyze.length === 0) {
return {
fonts: [],
canReproducePerfectly: true,
hasWarnings: false,
summary: {
perfect: 0,
embeddedSubset: 0,
systemFallback: 0,
missing: 0,
unknown: 0,
},
};
}
const fontAnalyses = fontsToAnalyze.map(font => analyzeFontReproduction(font, allFonts));
// Calculate summary
const summary = {
perfect: fontAnalyses.filter(f => f.status === 'perfect').length,
embeddedSubset: fontAnalyses.filter(f => f.status === 'embedded-subset').length,
systemFallback: fontAnalyses.filter(f => f.status === 'system-fallback').length,
missing: fontAnalyses.filter(f => f.status === 'missing').length,
unknown: fontAnalyses.filter(f => f.status === 'unknown').length,
};
// Can reproduce perfectly ONLY if all fonts are truly perfect (not subsets)
const canReproducePerfectly = fontAnalyses.every(f => f.status === 'perfect');
// Has warnings if any font has issues (including subsets)
const hasWarnings = fontAnalyses.some(
f => f.warnings.length > 0 || f.status === 'missing' || f.status === 'system-fallback' || f.status === 'embedded-subset'
);
return {
fonts: fontAnalyses,
canReproducePerfectly,
hasWarnings,
summary,
};
};
/**
* Gets a human-readable description of the font status
*/
export const getFontStatusDescription = (status: FontStatus): string => {
switch (status) {
case 'perfect':
return 'Fully embedded - perfect reproduction';
case 'embedded-subset':
return 'Embedded (subset) - existing text will render correctly';
case 'system-fallback':
return 'Using system font - appearance may differ';
case 'missing':
return 'Not embedded - will use fallback font';
case 'unknown':
return 'Unknown status';
}
};
/**
* Gets a color indicator for the font status
*/
export const getFontStatusColor = (status: FontStatus): string => {
switch (status) {
case 'perfect':
return 'green';
case 'embedded-subset':
return 'blue';
case 'system-fallback':
return 'yellow';
case 'missing':
return 'red';
case 'unknown':
return 'gray';
}
};
/**
* Gets an icon indicator for the font status
*/
export const getFontStatusIcon = (status: FontStatus): string => {
switch (status) {
case 'perfect':
return '✓';
case 'embedded-subset':
return '⚠';
case 'system-fallback':
return '⚠';
case 'missing':
return '✗';
case 'unknown':
return '?';
}
};
@@ -1,232 +0,0 @@
export interface PdfJsonFontCidSystemInfo {
registry?: string | null;
ordering?: string | null;
supplement?: number | null;
}
export interface PdfJsonTextColor {
colorSpace?: string | null;
components?: number[] | null;
}
export interface PdfJsonCosValue {
type?: string | null;
value?: unknown;
items?: PdfJsonCosValue[] | null;
entries?: Record<string, PdfJsonCosValue | null> | null;
stream?: PdfJsonStream | null;
}
export interface PdfJsonFont {
id?: string;
pageNumber?: number | null;
uid?: string | null;
baseName?: string | null;
color?: string | null;
subtype?: string | null;
encoding?: string | null;
cidSystemInfo?: PdfJsonFontCidSystemInfo | null;
embedded?: boolean | null;
program?: string | null;
programFormat?: string | null;
webProgram?: string | null;
webProgramFormat?: string | null;
pdfProgram?: string | null;
pdfProgramFormat?: string | null;
toUnicode?: string | null;
standard14Name?: string | null;
fontDescriptorFlags?: number | null;
ascent?: number | null;
descent?: number | null;
capHeight?: number | null;
xHeight?: number | null;
italicAngle?: number | null;
unitsPerEm?: number | null;
cosDictionary?: PdfJsonCosValue | null;
}
export interface PdfJsonTextElement {
text?: string | null;
fontId?: string | null;
fontSize?: number | null;
fontMatrixSize?: number | null;
fontSizeInPt?: number | null;
characterSpacing?: number | null;
wordSpacing?: number | null;
spaceWidth?: number | null;
zOrder?: number | null;
horizontalScaling?: number | null;
leading?: number | null;
rise?: number | null;
renderingMode?: number | null;
x?: number | null;
y?: number | null;
width?: number | null;
height?: number | null;
textMatrix?: number[] | null;
fillColor?: PdfJsonTextColor | null;
strokeColor?: PdfJsonTextColor | null;
charCodes?: number[] | null;
fallbackUsed?: boolean | null;
}
export interface PdfJsonImageElement {
id?: string | null;
objectName?: string | null;
inlineImage?: boolean | null;
nativeWidth?: number | null;
nativeHeight?: number | null;
x?: number | null;
y?: number | null;
width?: number | null;
height?: number | null;
left?: number | null;
right?: number | null;
top?: number | null;
bottom?: number | null;
transform?: number[] | null;
zOrder?: number | null;
imageData?: string | null;
imageFormat?: string | null;
}
export interface PdfJsonStream {
dictionary?: Record<string, unknown> | null;
rawData?: string | null;
}
export interface PdfJsonPage {
pageNumber?: number | null;
width?: number | null;
height?: number | null;
rotation?: number | null;
mediaBox?: number[] | null;
cropBox?: number[] | null;
textElements?: PdfJsonTextElement[] | null;
imageElements?: PdfJsonImageElement[] | null;
resources?: unknown;
contentStreams?: PdfJsonStream[] | null;
}
export interface PdfJsonMetadata {
title?: string | null;
author?: string | null;
subject?: string | null;
keywords?: string | null;
creator?: string | null;
producer?: string | null;
creationDate?: string | null;
modificationDate?: string | null;
trapped?: string | null;
numberOfPages?: number | null;
}
export interface PdfJsonDocument {
metadata?: PdfJsonMetadata | null;
xmpMetadata?: string | null;
fonts?: PdfJsonFont[] | null;
pages?: PdfJsonPage[] | null;
lazyImages?: boolean | null;
}
export interface PdfJsonPageDimension {
pageNumber?: number | null;
width?: number | null;
height?: number | null;
rotation?: number | null;
}
export interface PdfJsonDocumentMetadata {
metadata?: PdfJsonMetadata | null;
xmpMetadata?: string | null;
fonts?: PdfJsonFont[] | null;
pageDimensions?: PdfJsonPageDimension[] | null;
formFields?: unknown[] | null;
lazyImages?: boolean | null;
}
export interface BoundingBox {
left: number;
right: number;
top: number;
bottom: number;
}
export interface TextGroup {
id: string;
pageIndex: number;
fontId?: string | null;
fontSize?: number | null;
fontMatrixSize?: number | null;
lineSpacing?: number | null;
lineElementCounts?: number[] | null;
color?: string | null;
fontWeight?: number | 'normal' | 'bold' | null;
rotation?: number | null;
anchor?: { x: number; y: number } | null;
baselineLength?: number | null;
baseline?: number | null;
elements: PdfJsonTextElement[];
originalElements: PdfJsonTextElement[];
text: string;
originalText: string;
bounds: BoundingBox;
childLineGroups?: TextGroup[] | null;
}
export const DEFAULT_PAGE_WIDTH = 612;
export const DEFAULT_PAGE_HEIGHT = 792;
export interface ConversionProgress {
percent: number;
stage: string;
message: string;
current?: number;
total?: number;
}
export interface PdfTextEditorViewData {
document: PdfJsonDocument | null;
groupsByPage: TextGroup[][];
imagesByPage: PdfJsonImageElement[][];
pagePreviews: Map<number, string>;
selectedPage: number;
dirtyPages: boolean[];
hasDocument: boolean;
hasVectorPreview: boolean;
fileName: string;
errorMessage: string | null;
isGeneratingPdf: boolean;
isConverting: boolean;
conversionProgress: ConversionProgress | null;
hasChanges: boolean;
forceSingleTextElement: boolean;
groupingMode: 'auto' | 'paragraph' | 'singleLine';
requestPagePreview: (pageIndex: number, scale: number) => void;
onSelectPage: (pageIndex: number) => void;
onGroupEdit: (pageIndex: number, groupId: string, value: string) => void;
onGroupDelete: (pageIndex: number, groupId: string) => void;
onImageTransform: (
pageIndex: number,
imageId: string,
next: {
left: number;
bottom: number;
width: number;
height: number;
transform: number[];
},
) => void;
onImageReset: (pageIndex: number, imageId: string) => void;
onReset: () => void;
onDownloadJson: () => void;
onGeneratePdf: () => void;
onGeneratePdfForNavigation: () => Promise<void>;
onSaveToWorkbench: () => Promise<void>;
isSavingToWorkbench: boolean;
onForceSingleTextElementChange: (value: boolean) => void;
onGroupingModeChange: (value: 'auto' | 'paragraph' | 'singleLine') => void;
onMergeGroups: (pageIndex: number, groupIds: string[]) => boolean;
onUngroupGroup: (pageIndex: number, groupId: string) => boolean;
onLoadFile: (file: File) => void;
}
@@ -1,98 +0,0 @@
import { useRef, useEffect, useState } from 'react'
interface AudioWaveformProps {
waveformHistory: number[][]
isActive: boolean
}
export function AudioWaveform({ waveformHistory, isActive }: AudioWaveformProps) {
const containerRef = useRef<HTMLDivElement>(null)
const [maxColumns, setMaxColumns] = useState(150)
// Measure container and calculate how many columns fit
useEffect(() => {
if (!containerRef.current) return
const updateMaxColumns = () => {
if (!containerRef.current) return
const width = containerRef.current.offsetWidth
// Each column is 2px + 2px gap = 4px effective width
const cols = Math.floor(width / 4)
setMaxColumns(Math.max(cols, 50)) // minimum 50 columns
}
updateMaxColumns()
const resizeObserver = new ResizeObserver(updateMaxColumns)
resizeObserver.observe(containerRef.current)
return () => resizeObserver.disconnect()
}, [])
if (!isActive) return null
// Use history if available, otherwise show placeholder
const columns = waveformHistory || []
// Only take exactly what fits
const visibleColumns = columns.slice(-maxColumns)
// Pad with empty columns at the start if we don't have enough history
const paddingCount = Math.max(0, maxColumns - visibleColumns.length)
return (
<div ref={containerRef} className="flex items-center h-6 w-full">
<div className="flex items-center gap-0.5 w-full">
{/* Padding columns (empty/minimal) */}
{Array(paddingCount).fill(0).map((_, i) => (
<div
key={`pad-${i}`}
className="flex flex-col items-center justify-center gap-0.5 flex-shrink-0"
style={{ width: '2px', minWidth: '2px' }}
>
{Array(10).fill(0).map((_, bandIndex) => (
<div
key={bandIndex}
className="w-full bg-blue-400/20 rounded-full"
style={{ height: '1px', minHeight: '1px' }}
/>
))}
</div>
))}
{/* Actual waveform columns */}
{visibleColumns.map((column, colIndex) => {
const numBands = 10
const step = Math.floor(column.length / numBands)
const bands = []
for (let i = 0; i < numBands; i++) {
const index = Math.min(i * step, column.length - 1)
bands.push(column[index])
}
return (
<div
key={`col-${colIndex}`}
className="flex flex-col items-center justify-center gap-0.5 flex-shrink-0"
style={{ width: '2px', minWidth: '2px' }}
>
{bands.map((level, bandIndex) => {
const height = Math.max(1, level * 20)
return (
<div
key={bandIndex}
className="w-full bg-blue-400 rounded-full"
style={{
height: `${height}px`,
minHeight: '1px',
}}
/>
)
})}
</div>
)
})}
</div>
</div>
)
}
@@ -1,32 +0,0 @@
import { ButtonHTMLAttributes } from 'react'
type ButtonVariant = 'primary' | 'secondary' | 'ghost' | 'danger' | 'accent'
type ButtonSize = 'sm' | 'md' | 'lg' | 'icon'
interface ButtonProps extends ButtonHTMLAttributes<HTMLButtonElement> {
variant?: ButtonVariant
size?: ButtonSize
}
const base =
'inline-flex items-center justify-center rounded-lg font-medium transition-colors focus-visible:outline-none focus-visible:ring-2 focus-visible:ring-offset-2 focus-visible:ring-blue-500 focus-visible:ring-offset-slate-950 disabled:opacity-50 disabled:cursor-not-allowed'
const variantStyles: Record<ButtonVariant, string> = {
primary: 'bg-blue-600 hover:bg-blue-500 text-white',
secondary: 'bg-slate-800 hover:bg-slate-700 text-slate-100 border border-slate-700',
ghost: 'bg-transparent hover:bg-slate-900 text-slate-300 border border-transparent',
danger: 'bg-rose-600 hover:bg-rose-500 text-white',
accent: 'bg-emerald-600 hover:bg-emerald-500 text-white',
}
const sizeStyles: Record<ButtonSize, string> = {
sm: 'text-xs px-3 py-1.5',
md: 'text-sm px-4 py-2',
lg: 'text-base px-5 py-3',
icon: 'p-2',
}
export function Button({ variant = 'primary', size = 'md', className = '', ...props }: ButtonProps) {
const classes = [base, variantStyles[variant], sizeStyles[size], className].filter(Boolean).join(' ')
return <button className={classes} {...props} />
}
@@ -1,11 +0,0 @@
import { PropsWithChildren } from 'react'
interface ButtonGroupProps extends PropsWithChildren {
align?: 'start' | 'center' | 'end'
}
export function ButtonGroup({ children, align = 'start' }: ButtonGroupProps) {
const alignment =
align === 'center' ? 'justify-center' : align === 'end' ? 'justify-end' : 'justify-start'
return <div className={`flex flex-wrap gap-2 ${alignment}`}>{children}</div>
}
@@ -1,255 +0,0 @@
import { FormEvent, KeyboardEvent, MutableRefObject, useRef, useEffect } from 'react'
import { DocumentState, Message, StyleProfile } from '../../types'
import { Button } from '../ui/Button'
import { AudioWaveform } from '../ui/AudioWaveform'
interface ChatPanelProps {
onBack: () => void
styleProfile: StyleProfile | null
messages: Message[]
chatEndRef: MutableRefObject<HTMLDivElement | null>
isGenerating: boolean
prompt: string
onPromptChange: (value: string) => void
onSubmit: (event: FormEvent<HTMLFormElement>) => void
onKeyDown: (event: KeyboardEvent<HTMLTextAreaElement>) => void
skipTemplates: boolean
onSkipTemplatesChange: (value: boolean) => void
onClearSession: () => void
onToggleRecording: () => void
onCancelRecording: () => void
onAcceptRecording: () => void
isRecording: boolean
whisperStatus: string | null
waveformHistory: number[][]
currentDoc: DocumentState | null
onOpenHistory: () => void
onOpenImport: () => void
}
export function ChatPanel({
onBack,
styleProfile,
messages,
chatEndRef,
isGenerating,
prompt,
onPromptChange,
onSubmit,
onKeyDown,
skipTemplates,
onSkipTemplatesChange,
onClearSession,
onToggleRecording,
onCancelRecording,
onAcceptRecording,
isRecording,
whisperStatus,
waveformHistory,
currentDoc,
onOpenHistory,
onOpenImport,
}: ChatPanelProps) {
const textareaRef = useRef<HTMLTextAreaElement>(null)
const gradientRef = useRef<HTMLDivElement>(null)
const updateInputGradient = () => {
const gradient = gradientRef.current
if (!gradient) return
// Hide the gradient entirely to avoid lingering blur while typing.
gradient.style.opacity = '0'
}
// Auto-resize textarea and update gradient visibility
useEffect(() => {
const textarea = textareaRef.current
if (textarea) {
textarea.style.height = 'auto'
const newHeight = Math.min(textarea.scrollHeight, 200) // Max ~8 lines for the smaller panel
textarea.style.height = `${newHeight}px`
}
updateInputGradient()
}, [prompt])
return (
<div className="w-1/3 min-w-[350px] flex flex-col border-r border-slate-800 bg-slate-900 z-10">
<div className="p-4 border-b border-slate-800 flex items-center justify-between gap-3">
<Button variant="ghost" size="sm" onClick={onBack}>
Back
</Button>
<div className="flex items-center gap-2">
<Button variant="secondary" size="sm" onClick={onOpenHistory}>
History & Layouts
</Button>
<Button variant="accent" size="sm" onClick={onOpenImport}>
Import Layout (PDF)
</Button>
</div>
</div>
{styleProfile && (
<div className="px-4 py-2 border-b border-slate-800 flex items-center gap-3 text-xs text-slate-400">
<span className="px-2 py-1 rounded-full bg-slate-800 border border-slate-700">
{styleProfile.layout_preference || 'clean'}
</span>
<span className="px-2 py-1 rounded-full bg-slate-800 border border-slate-700">
{styleProfile.font_preference || 'font'}
</span>
<span className="px-2 py-1 rounded-full bg-slate-800 border border-slate-700">
{styleProfile.tone || 'tone'}
</span>
</div>
)}
<div className="flex-1 overflow-y-auto p-4 space-y-6 relative">
{messages.map((msg, idx) => (
<div key={idx} className={`flex ${msg.role === 'user' ? 'justify-end' : 'justify-start'}`}>
<div
className={`max-w-[85%] rounded-2xl p-4 ${
msg.role === 'user'
? 'bg-blue-600 text-white rounded-br-none'
: 'bg-slate-800 text-slate-200 rounded-bl-none border border-slate-700'
}`}
>
<p className="whitespace-pre-wrap text-sm leading-relaxed" style={{ overflowWrap: 'anywhere', wordBreak: 'break-word' }}>{msg.text}</p>
</div>
</div>
))}
{isGenerating && (
<div className="flex justify-start">
<div className="bg-slate-800 rounded-2xl p-4 rounded-bl-none border border-slate-700">
<div className="flex space-x-2">
<div className="w-2 h-2 bg-slate-500 rounded-full animate-bounce"></div>
<div className="w-2 h-2 bg-slate-500 rounded-full animate-bounce delay-100"></div>
<div className="w-2 h-2 bg-slate-500 rounded-full animate-bounce delay-200"></div>
</div>
</div>
</div>
)}
{!messages.length && (
<div className="text-xs text-slate-500">
Ask for revisions, upload layouts, or describe the style you want. I&apos;ll keep the latest
context.
</div>
)}
<div ref={chatEndRef} />
</div>
<div className="p-4 bg-slate-900 border-t border-slate-800 space-y-3">
<div className="flex items-center justify-between text-xs text-slate-400">
<label className="flex items-center gap-2 cursor-pointer select-none">
<input
type="checkbox"
checked={skipTemplates}
onChange={(e) => onSkipTemplatesChange(e.target.checked)}
className="h-4 w-4 rounded border-slate-600 bg-slate-800 accent-blue-500"
/>
<span>Don&apos;t reuse saved layouts/templates</span>
</label>
<Button variant="secondary" size="sm" onClick={onClearSession}>
Clear
</Button>
</div>
<form onSubmit={onSubmit} className="relative">
<div className="w-full bg-slate-800 rounded-xl border border-slate-700 focus-within:ring-2 focus-within:ring-blue-500/50">
{/* Content area */}
<div className="px-3 pt-3 pb-1">
{isRecording ? (
<div className="min-h-[20px]">
<AudioWaveform waveformHistory={waveformHistory} isActive={isRecording} />
</div>
) : (
<textarea
ref={textareaRef}
value={prompt}
onChange={(e) => onPromptChange(e.target.value)}
onKeyDown={onKeyDown}
onScroll={updateInputGradient}
placeholder={currentDoc ? 'Modify the document...' : 'Describe what to build...'}
className="w-full bg-transparent text-white focus:outline-none resize-none overflow-y-auto text-sm"
style={{ maxHeight: '200px' }}
rows={1}
/>
)}
</div>
</div>
<div
ref={gradientRef}
className="pointer-events-none absolute inset-x-0 bottom-0 h-6 bg-gradient-to-t from-slate-900/90 to-transparent transition-opacity duration-200"
style={{ opacity: 0 }}
/>
<div className="mt-2 flex items-center justify-end gap-2">
{isRecording ? (
<>
<button
type="button"
onClick={onCancelRecording}
className="p-2 rounded-lg text-slate-400 hover:text-white hover:bg-slate-700 transition-colors"
title="Cancel recording"
>
<svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M6 18L18 6M6 6l12 12" />
</svg>
</button>
<button
type="button"
onClick={(e) => {
e.preventDefault()
e.stopPropagation()
onAcceptRecording()
}}
className="p-2 rounded-full bg-blue-600 text-white hover:bg-blue-700 transition-colors"
title="Accept and transcribe"
>
<svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M5 13l4 4L19 7" />
</svg>
</button>
</>
) : (
<>
<button
type="button"
onClick={onToggleRecording}
className="p-2 rounded-lg text-slate-400 hover:text-white hover:bg-slate-700 transition-colors"
title="Start voice input"
>
<svg className="w-4 h-4" fill="currentColor" viewBox="0 0 24 24">
<path d="M12 14c1.66 0 3-1.34 3-3V5c0-1.66-1.34-3-3-3S9 3.34 9 5v6c0 1.66 1.34 3 3 3z" />
<path d="M17 11c0 2.76-2.24 5-5 5s-5-2.24-5-5H5c0 3.53 2.61 6.43 6 6.92V21h2v-3.08c3.39-.49 6-3.39 6-6.92h-2z" />
</svg>
</button>
<button
type="submit"
disabled={isGenerating}
className="p-2 rounded-full bg-blue-600 text-white hover:bg-blue-700 disabled:opacity-50 disabled:cursor-not-allowed transition-colors"
title="Send"
>
<svg className="w-4 h-4" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M5 10l7-7m0 0l7 7m-7-7v18" />
</svg>
</button>
</>
)}
</div>
</form>
<div className="mt-2 flex items-center gap-2 text-xs text-slate-400">
<div className="relative group px-3 py-2 rounded-full bg-slate-800 border border-slate-700 font-medium text-slate-300 cursor-not-allowed select-none">
<span className="flex items-center gap-2">
Document type (auto)
<svg className="w-3.5 h-3.5 text-slate-500" fill="none" stroke="currentColor" viewBox="0 0 24 24">
<path strokeLinecap="round" strokeLinejoin="round" strokeWidth={2} d="M19 15l-7-7-7 7" />
</svg>
</span>
<span className="pointer-events-none absolute -top-9 left-1/2 -translate-x-1/2 whitespace-nowrap rounded-md bg-slate-800 px-3 py-1 text-[11px] text-slate-200 border border-slate-700 opacity-0 transition-opacity duration-150 group-hover:opacity-100">
Well pick the best document type for your prompt automatically using AI.
</span>
</div>
<div className="px-3 py-2 rounded-full bg-slate-800 border border-slate-700 font-medium text-slate-300 cursor-not-allowed select-none">
GPT 5.1
</div>
</div>
{whisperStatus && <div className="text-[11px] text-slate-400 mt-1">{whisperStatus}</div>}
</div>
</div>
)
}
@@ -1,73 +0,0 @@
import { VersionEntry } from '../../types'
import { Button } from '../ui/Button'
interface HistoryPanelProps {
versions: VersionEntry[]
selectedVersionId: string | null
onSelectVersion: (id: string) => void
onClose: () => void
onRefresh: () => void
}
export function HistoryPanel({ versions, selectedVersionId, onSelectVersion, onClose, onRefresh }: HistoryPanelProps) {
const uniqueTypes = Array.from(new Set(versions.map((v) => v.documentType)))
return (
<div className="absolute inset-y-0 right-0 w-96 bg-slate-900 border-l border-slate-800 shadow-2xl flex flex-col z-20">
<div className="p-4 border-b border-slate-800 flex items-center justify-between">
<div>
<div className="text-sm text-slate-400">Versions & Layouts</div>
<div className="text-xs text-slate-500">Most recent first</div>
</div>
<div className="flex items-center gap-2">
<Button variant="ghost" size="sm" onClick={onRefresh}>
Refresh
</Button>
<Button variant="secondary" size="sm" onClick={onClose}>
Close
</Button>
</div>
</div>
<div className="p-4 space-y-3 overflow-y-auto flex-1">
<div className="text-xs uppercase text-slate-500">Saved layouts</div>
<div className="flex flex-wrap gap-2">
{uniqueTypes.length === 0 && <span className="text-xs text-slate-500">None yet</span>}
{uniqueTypes.map((type) => (
<span
key={type}
className="px-2 py-1 text-xs bg-slate-800 rounded border border-slate-700 text-slate-300"
>
{type}
</span>
))}
</div>
<div className="flex items-center justify-between pt-2">
<span className="text-xs uppercase text-slate-500">History</span>
<span className="text-[11px] text-slate-500">{versions.length} versions</span>
</div>
{versions.length === 0 && <p className="text-sm text-slate-500">No versions yet</p>}
{versions.slice(0, 30).map((version) => (
<button
key={version.id}
onClick={() => onSelectVersion(version.id)}
className={`w-full text-left p-3 rounded-xl border transition-colors ${
selectedVersionId === version.id
? 'border-blue-500 bg-blue-500/10'
: 'border-slate-800 bg-slate-900'
} hover:border-blue-500`}
>
<div className="flex items-center justify-between">
<div className="text-xs uppercase text-slate-400">{version.documentType}</div>
<div className="text-[10px] text-slate-500">
{version.createdAt ? new Date(version.createdAt).toLocaleString() : 'recent'}
</div>
</div>
<div className="text-sm text-slate-100 line-clamp-2">{version.prompt || 'Generated document'}</div>
</button>
))}
</div>
</div>
)
}
@@ -1,137 +0,0 @@
import { useMemo } from 'react'
import { DocumentState } from '../../types'
import PdfThumbnailViewer from '../pdfTextEditor/PdfThumbnailViewer'
// import PdfTextEditorFull from '../pdfTextEditor/PdfTextEditorFull'
interface PreviewPanelProps {
currentDoc: DocumentState | null
isGenerating: boolean
isLivePreviewing: boolean
skipTemplates: boolean
onPdfReplaced: (pdfUrl: string) => void
}
export function PreviewPanel({
currentDoc,
isGenerating,
isLivePreviewing,
skipTemplates,
onPdfReplaced,
}: PreviewPanelProps) {
// Keep around while editor is hidden so we don't lose the apply handler
void onPdfReplaced
const hasDocumentStarted = useMemo(() => {
const latex = currentDoc?.latex || ''
return /\\begin\s*{document}/i.test(latex)
}, [currentDoc?.latex])
const canRenderPdf = useMemo(() => {
if (!currentDoc?.pdfUrl) return false
// While live preview is streaming, wait until the document body starts
if (isLivePreviewing) return hasDocumentStarted
return true
}, [currentDoc?.pdfUrl, hasDocumentStarted, isLivePreviewing])
const heading = useMemo(() => {
if (isGenerating) return 'Generating ...'
if (isLivePreviewing) return 'Live updating preview'
if (currentDoc?.pdfUrl) return 'Preview ready'
return 'PDF preview'
}, [currentDoc?.pdfUrl, isGenerating, isLivePreviewing])
return (
<div className="flex-1 flex flex-col bg-slate-950">
<div className="h-14 bg-slate-900 border-b border-slate-800 flex items-center justify-between px-6">
<div className="flex items-center space-x-4">
<h2 className="font-semibold text-slate-200">{heading}</h2>
{currentDoc && (
<span className="text-xs text-slate-500 px-2 py-1 bg-slate-800 rounded border border-slate-700">
{currentDoc.documentType}
</span>
)}
{skipTemplates && (
<span className="text-[11px] text-amber-200 px-2 py-1 rounded bg-amber-500/20 border border-amber-600">
Skipping saved layouts
</span>
)}
{isLivePreviewing && (
<span className="text-[11px] text-blue-200 px-2 py-1 rounded bg-blue-600/20 border border-blue-500 animate-pulse">
Rendering live
</span>
)}
</div>
<div className="flex items-center gap-3">
{currentDoc?.pdfUrl && (
<a
href={currentDoc.pdfUrl}
target="_blank"
rel="noopener noreferrer"
className="px-4 py-2 bg-blue-600 hover:bg-blue-500 text-white text-sm font-medium rounded-lg flex items-center"
>
Download PDF
</a>
)}
</div>
</div>
<div className="flex-1 overflow-hidden relative">
{canRenderPdf ? (
<div className="h-full">
{/* Temporarily hiding the interactive editor; restore when edit mode returns */}
{/*
<PdfTextEditorFull
pdfUrl={currentDoc?.pdfUrl || ''}
onApply={(nextUrl: string) => {
onPdfReplaced(nextUrl)
}}
/>
*/}
<PdfThumbnailViewer pdfUrl={currentDoc?.pdfUrl || ''} isLivePreviewing={isLivePreviewing} />
</div>
) : (
<div className="flex-1 bg-slate-900 overflow-auto p-6">
{isGenerating || isLivePreviewing ? (
<div className="relative w-full h-full overflow-auto bg-slate-900 rounded-lg border border-slate-800 shadow-inner">
<div className="flex justify-center py-8">
<div
className="relative rounded-2xl border border-slate-300/60 bg-white shadow-2xl overflow-hidden"
style={{
width: '860px',
maxWidth: '90vw',
aspectRatio: '1 / 1.414',
}}
>
<div className="h-12 bg-slate-100 border-b border-slate-200 animate-pulse" />
<div className="p-8 space-y-4">
{[1, 2, 3, 4].map((line) => (
<div
key={`line-top-${line}`}
className="h-4 rounded-full bg-slate-200 animate-pulse"
style={{ width: `${78 - line * 10}%` }}
/>
))}
<div className="h-40 rounded-xl bg-slate-100 border border-slate-200 animate-pulse" />
{[5, 6, 7].map((line) => (
<div
key={`line-bottom-${line}`}
className="h-4 rounded-full bg-slate-200 animate-pulse"
style={{ width: `${70 - (line - 5) * 8}%` }}
/>
))}
</div>
</div>
</div>
</div>
) : (
<div className="flex items-center justify-center text-sm text-slate-500 h-full">
Generate a PDF to view the preview.
</div>
)}
</div>
)}
</div>
</div>
)
}
@@ -1,757 +0,0 @@
import { FormEvent, KeyboardEvent, useEffect, useState } from 'react'
import { DocumentState } from '../../types'
import PdfThumbnailViewer from '../pdfTextEditor/PdfThumbnailViewer'
interface WorkspaceViewProps {
isGenerating: boolean
isLivePreviewing: boolean
isStageLoading: boolean
prompt: string
onPromptChange: (value: string) => void
onChatSubmit: (event: FormEvent<HTMLFormElement>) => void
onKeyDown: (event: KeyboardEvent<HTMLTextAreaElement>) => void
currentDoc: DocumentState | null
onBack: () => void
stage: 'outline' | 'text' | 'styling' | 'review'
outlineRows: { section: string; details: string }[]
outlineSections: string[]
excludedFields: string[]
outlineConstraints: { tone: string; audience: string; pageCount: number }
draftRows: { label: string; value: string }[]
setOutlineRows: (rows: { section: string; details: string }[]) => void
setOutlineSections: (next: string[]) => void
setExcludedFields: (next: string[]) => void
setOutlineConstraints: (next: { tone: string; audience: string; pageCount: number }) => void
setDraftRows: (rows: { label: string; value: string }[]) => void
docTypes: string[]
templateCounts: Record<string, number>
selectedDocType: string
selectedTemplateId: string
templatesForSelected: string[]
isTemplateLoading: boolean
onSelectTemplate: (docType: string, templateId: string) => void
templateThumbnailUrl: (docType: string, templateId: string) => string
approveOutline: () => void
onAiOutline: () => void
approveDraft: () => void
saveAndReview: () => void
styleDraft: { layout_preference: string; font_preference: string; color_accent: string }
setStyleDraft: (next: { layout_preference: string; font_preference: string; color_accent: string }) => void
applyStyleAndRegenerate: () => void
onAddPromptInfo: (value: string) => void
onStageSelect: (stage: 'outline' | 'text' | 'styling' | 'review') => void
imagePlaceholdersCount: number
isAssetUploading: boolean
assetError: string | null
onAddPlaceholderImage: (file: File) => void
onRemovePlaceholders: () => void
onOpenImportTemplate: () => void
}
export function WorkspaceView({
isGenerating,
isLivePreviewing,
isStageLoading,
prompt,
onPromptChange,
onChatSubmit,
onKeyDown,
currentDoc,
onBack,
stage,
outlineRows,
outlineSections,
excludedFields,
outlineConstraints,
draftRows,
setOutlineRows,
setOutlineSections,
setExcludedFields,
setOutlineConstraints,
setDraftRows,
docTypes,
templateCounts,
selectedDocType,
selectedTemplateId,
templatesForSelected,
isTemplateLoading,
onSelectTemplate,
templateThumbnailUrl,
approveOutline,
onAiOutline,
approveDraft,
saveAndReview,
styleDraft,
setStyleDraft,
applyStyleAndRegenerate,
onAddPromptInfo,
onStageSelect,
imagePlaceholdersCount,
isAssetUploading,
assetError,
onAddPlaceholderImage,
onRemovePlaceholders,
onOpenImportTemplate,
}: WorkspaceViewProps) {
const [isTemplatePickerOpen, setIsTemplatePickerOpen] = useState(false)
const [isDataPanelOpen, setIsDataPanelOpen] = useState(false)
const [promptAddon, setPromptAddon] = useState('')
const autoSize = (event: React.FormEvent<HTMLTextAreaElement>) => {
const target = event.currentTarget
target.style.height = 'auto'
target.style.height = `${target.scrollHeight}px`
}
useEffect(() => {
const nodes = document.querySelectorAll('textarea[data-autosize="true"]')
nodes.forEach((node) => {
const area = node as HTMLTextAreaElement
area.style.height = 'auto'
area.style.height = `${area.scrollHeight}px`
})
}, [outlineRows, draftRows, stage])
const formatDocLabel = (value: string) => {
return value
.split('_')
.map((part) => part.charAt(0).toUpperCase() + part.slice(1))
.join(' ')
}
const docLabel = currentDoc?.documentType ? formatDocLabel(currentDoc.documentType) : 'Document'
const renderStageContent = () => {
if (stage === 'outline') {
return (
<div className="space-y-6">
<div>
<h2 className="text-xl font-semibold text-slate-900">{docLabel} Inputs</h2>
<p className="text-sm text-slate-500">Fill in the data you want the AI to use.</p>
</div>
<div className="rounded-2xl border border-slate-200 bg-white p-4 space-y-3">
<div className="flex items-center justify-between">
<div>
<div className="text-xs font-semibold uppercase tracking-wide text-slate-500">
Selected Template
</div>
<div className="text-sm text-slate-700">
{formatDocLabel(selectedDocType)} · {formatDocLabel(selectedTemplateId)}
</div>
</div>
<button
type="button"
className="text-xs font-semibold text-blue-600"
onClick={() => setIsTemplatePickerOpen((prev) => !prev)}
>
{isTemplatePickerOpen ? 'Hide' : 'Edit'}
</button>
</div>
{isTemplatePickerOpen && (
<div className="space-y-4 pt-2">
<div className="space-y-2">
<label className="text-xs font-semibold uppercase tracking-wide text-slate-500">
Document Type
</label>
<select
className="w-full rounded-lg border border-slate-200 bg-white px-3 py-2 text-sm text-slate-700"
value={selectedDocType}
onChange={(event) => onSelectTemplate(event.target.value, selectedTemplateId)}
>
{docTypes.map((docType) => (
<option key={docType} value={docType}>
{formatDocLabel(docType)} ({templateCounts[docType] ?? 1})
</option>
))}
</select>
</div>
<div className="space-y-2">
<label className="text-xs font-semibold uppercase tracking-wide text-slate-500">
Template Style
</label>
{isTemplateLoading ? (
<div className="text-sm text-slate-400">Loading templates...</div>
) : (
<div className="grid gap-3 sm:grid-cols-2">
{templatesForSelected.map((templateId) => (
<button
type="button"
key={templateId}
onClick={() => onSelectTemplate(selectedDocType, templateId)}
className={`rounded-xl border text-left ${
selectedTemplateId === templateId
? 'border-blue-500 ring-2 ring-blue-200'
: 'border-slate-200'
}`}
>
<div
className="w-full rounded-t-xl bg-slate-50 overflow-hidden h-36"
style={{ aspectRatio: '210 / 297' }}
>
<img
src={templateThumbnailUrl(selectedDocType, templateId)}
alt={`${templateId} template`}
className="h-full w-full object-contain"
/>
</div>
<div className="px-3 py-2 text-sm text-slate-700">
{formatDocLabel(templateId)}
</div>
</button>
))}
</div>
)}
</div>
</div>
)}
</div>
<div className="rounded-2xl border border-slate-200 bg-white">
<div className="flex items-center justify-between px-4 py-3">
<div>
<div className="text-xs font-semibold uppercase tracking-wider text-slate-500">Data to use</div>
<div className="text-xs text-slate-400">
{outlineRows.filter((row) => row.details.trim()).length} filled ·{' '}
{outlineRows.filter((row) => !row.details.trim()).length} empty
</div>
{outlineRows.filter((row) => !row.details.trim()).length > outlineRows.length / 2 && (
<div className="text-xs text-amber-500 mt-1">
Add a bit more data for better results.
</div>
)}
</div>
<button
type="button"
className="text-xs font-semibold text-blue-600"
onClick={() => setIsDataPanelOpen((prev) => !prev)}
>
{isDataPanelOpen ? 'Hide' : 'Show'}
</button>
</div>
<div className="border-t border-slate-200 px-6 py-4 space-y-3">
<div className="text-xs font-semibold uppercase tracking-wider text-slate-500">
Add info from prompt
</div>
<div className="flex flex-col gap-2 sm:flex-row">
<input
className="flex-1 rounded-lg border border-slate-200 bg-white px-3 py-2 text-sm text-slate-700"
placeholder="Add more details (e.g., biller address, invoice number)..."
value={promptAddon}
onChange={(event) => setPromptAddon(event.target.value)}
onKeyDown={(event) => {
if (event.key === 'Enter') {
event.preventDefault()
onAddPromptInfo(promptAddon)
setPromptAddon('')
}
}}
/>
<button
type="button"
className="rounded-lg bg-slate-900 px-4 py-2 text-sm font-semibold text-white"
onClick={() => {
onAddPromptInfo(promptAddon)
setPromptAddon('')
}}
>
Add
</button>
</div>
<div className="text-xs text-slate-400">
This appends to the original prompt and re-runs auto-fill.
</div>
</div>
{isDataPanelOpen && (
<div className="border-t border-slate-200">
<div className="grid grid-cols-[1.2fr,2fr,auto] bg-slate-50 px-6 py-3 text-xs font-semibold text-slate-500 uppercase tracking-wider">
<div>Data name</div>
<div>Value</div>
<div />
</div>
{outlineRows.map((row, idx) => (
<div
key={`outline-${idx}`}
className="grid grid-cols-[1.2fr,2fr,auto] px-6 py-3 border-t border-slate-100 gap-4 items-start"
>
<input
className="text-sm text-slate-800 bg-transparent focus:outline-none"
value={row.section}
onChange={(event) => {
const next = [...outlineRows]
next[idx] = { ...row, section: event.target.value }
setOutlineRows(next)
}}
/>
<textarea
rows={1}
className="min-h-[56px] w-full resize-none overflow-hidden text-sm text-slate-500 bg-transparent focus:outline-none"
value={row.details}
onInput={autoSize}
data-autosize="true"
onChange={(event) => {
const next = [...outlineRows]
next[idx] = { ...row, details: event.target.value }
setOutlineRows(next)
}}
/>
<button
type="button"
className="text-xs text-slate-400 hover:text-rose-500"
onClick={() => {
const next = outlineRows.filter((_, rowIndex) => rowIndex !== idx)
const label = row.section.trim()
if (label) {
setExcludedFields([...excludedFields, label])
}
setOutlineRows(next)
}}
>
Remove
</button>
</div>
))}
<div className="flex items-center justify-between px-6 py-3">
<button
type="button"
className="text-xs font-semibold text-blue-600"
onClick={() => setOutlineRows([...outlineRows, { section: 'New data', details: '' }])}
>
Add data
</button>
{excludedFields.length > 0 && (
<div className="text-xs text-slate-400">
Excluded: {excludedFields.join(', ')}
</div>
)}
</div>
</div>
)}
</div>
<div className="rounded-2xl border border-slate-200 bg-white p-4 space-y-4">
<div className="text-xs font-semibold uppercase tracking-wider text-slate-500">
AI Constraints
</div>
<div className="grid gap-4 sm:grid-cols-2">
<div className="space-y-2">
<label className="text-xs font-semibold uppercase tracking-wide text-slate-500">Tone</label>
<select
className="w-full rounded-lg border border-slate-200 bg-white px-3 py-2 text-sm text-slate-700"
value={outlineConstraints.tone}
onChange={(event) =>
setOutlineConstraints({ ...outlineConstraints, tone: event.target.value })
}
>
{['Professional', 'Formal', 'Friendly', 'Neutral', 'Academic', 'Technical', 'Narrative', 'Direct', 'Informative'].map((tone) => (
<option key={tone} value={tone}>
{tone}
</option>
))}
</select>
</div>
<div className="space-y-2">
<label className="text-xs font-semibold uppercase tracking-wide text-slate-500">Audience</label>
<input
className="w-full rounded-lg border border-slate-200 bg-white px-3 py-2 text-sm text-slate-700"
value={outlineConstraints.audience}
onChange={(event) =>
setOutlineConstraints({ ...outlineConstraints, audience: event.target.value })
}
placeholder="Audience"
/>
</div>
<div className="space-y-2">
<label className="text-xs font-semibold uppercase tracking-wide text-slate-500">Pages</label>
<input
type="number"
min={1}
className="w-full rounded-lg border border-slate-200 bg-white px-3 py-2 text-sm text-slate-700"
value={outlineConstraints.pageCount}
onChange={(event) =>
setOutlineConstraints({
...outlineConstraints,
pageCount: Math.max(1, Number(event.target.value || 1)),
})
}
/>
</div>
</div>
</div>
<div className="rounded-2xl border border-slate-200 bg-white p-4 space-y-3">
<div className="text-xs font-semibold uppercase tracking-wider text-slate-500">
Sections
</div>
<div className="space-y-3">
{outlineSections.map((section, idx) => (
<div key={`section-${idx}`} className="flex items-center gap-3">
<input
className="flex-1 rounded-lg border border-slate-200 bg-white px-3 py-2 text-sm text-slate-700"
value={section}
onChange={(event) => {
const next = [...outlineSections]
next[idx] = event.target.value
setOutlineSections(next)
}}
/>
<button
type="button"
className="text-xs text-slate-400 hover:text-rose-500"
onClick={() => {
const next = outlineSections.filter((_, rowIndex) => rowIndex !== idx)
setOutlineSections(next)
}}
>
Remove
</button>
</div>
))}
</div>
<button
type="button"
className="text-xs font-semibold text-blue-600"
onClick={() => setOutlineSections([...outlineSections, 'New section'])}
>
Add section
</button>
</div>
</div>
)
}
if (stage === 'text') {
return (
<div className="space-y-6">
<div>
<h2 className="text-xl font-semibold text-slate-900">{docLabel} Text</h2>
<p className="text-sm text-slate-500">Edit each section draft. Polishing happens after approval.</p>
</div>
<div className="border border-slate-200 rounded-2xl overflow-hidden">
<div className="grid grid-cols-[1.2fr,2fr] bg-slate-50 px-6 py-3 text-xs font-semibold text-slate-500 uppercase tracking-wider">
<div>Section</div>
<div>Content</div>
</div>
{draftRows.map((row, idx) => (
<div
key={`draft-${idx}`}
className="grid grid-cols-[1.2fr,2fr] px-6 py-3 border-t border-slate-100 gap-4"
>
<input
className="text-sm text-slate-800 bg-transparent focus:outline-none"
value={row.label}
onChange={(event) => {
const next = [...draftRows]
next[idx] = { ...row, label: event.target.value }
setDraftRows(next)
}}
/>
<textarea
rows={1}
className="min-h-[120px] w-full resize-none overflow-hidden text-sm text-slate-600 bg-transparent focus:outline-none"
value={row.value}
onInput={autoSize}
data-autosize="true"
onChange={(event) => {
const next = [...draftRows]
next[idx] = { ...row, value: event.target.value }
setDraftRows(next)
}}
placeholder={isStageLoading ? 'Generating draft...' : 'Add content...'}
/>
</div>
))}
</div>
</div>
)
}
if (stage === 'styling') {
return (
<div className="space-y-6">
<div>
<h2 className="text-xl font-semibold text-slate-900">{docLabel} Review</h2>
<p className="text-sm text-slate-500">Check the generated preview before exporting.</p>
</div>
<div className="grid gap-4 lg:grid-cols-[1.1fr,2fr]">
<div className="space-y-4 rounded-2xl border border-slate-200 bg-white p-4">
<div className="space-y-2">
<label className="text-xs font-semibold uppercase tracking-wide text-slate-500">Font</label>
<select
className="w-full rounded-lg border border-slate-200 bg-white px-3 py-2 text-sm text-slate-700"
value={styleDraft.font_preference}
onChange={(event) =>
setStyleDraft({ ...styleDraft, font_preference: event.target.value })
}
>
{['Serif', 'Sans', 'Modern', 'Classic', 'Minimal'].map((font) => (
<option key={font} value={font}>
{font}
</option>
))}
</select>
</div>
<div className="space-y-2">
<label className="text-xs font-semibold uppercase tracking-wide text-slate-500">Layout</label>
<select
className="w-full rounded-lg border border-slate-200 bg-white px-3 py-2 text-sm text-slate-700"
value={styleDraft.layout_preference}
onChange={(event) =>
setStyleDraft({ ...styleDraft, layout_preference: event.target.value })
}
>
{['Compact', 'Balanced', 'Spacious', 'Grid', 'Editorial'].map((layout) => (
<option key={layout} value={layout}>
{layout}
</option>
))}
</select>
</div>
<div className="space-y-2">
<label className="text-xs font-semibold uppercase tracking-wide text-slate-500">Accent</label>
<input
type="color"
className="h-10 w-full rounded-lg border border-slate-200 bg-white px-2"
value={styleDraft.color_accent}
onChange={(event) =>
setStyleDraft({ ...styleDraft, color_accent: event.target.value })
}
/>
</div>
<button
type="button"
className="w-full rounded-lg bg-slate-900 px-4 py-2 text-sm font-semibold text-white disabled:opacity-50"
onClick={applyStyleAndRegenerate}
disabled={isGenerating || isStageLoading}
>
Apply Style & Regenerate
</button>
</div>
<div className="border border-slate-200 rounded-2xl bg-white">
{currentDoc?.pdfUrl ? (
<div className="p-4">
<PdfThumbnailViewer pdfUrl={currentDoc.pdfUrl} isLivePreviewing={isLivePreviewing} />
</div>
) : (
<div className="p-12 text-sm text-slate-500 text-center">
{isStageLoading || isGenerating ? 'Generating preview...' : 'Preview will appear here.'}
</div>
)}
</div>
</div>
</div>
)
}
return (
<div className="space-y-6">
<div>
<h2 className="text-xl font-semibold text-slate-900">{docLabel} Export</h2>
<p className="text-sm text-slate-500">Download or export your final PDF.</p>
</div>
<div className="border border-slate-200 rounded-2xl bg-white">
{currentDoc?.pdfUrl ? (
<div className="p-4">
<PdfThumbnailViewer pdfUrl={currentDoc.pdfUrl} isLivePreviewing={isLivePreviewing} />
</div>
) : (
<div className="p-12 text-sm text-slate-500 text-center">Generating the final preview...</div>
)}
</div>
</div>
)
}
const StageItem = ({
number,
label,
active,
stageKey,
}: {
number: number
label: string
active: boolean
stageKey: 'outline' | 'text' | 'styling' | 'review'
}) => (
<button
type="button"
onClick={() => onStageSelect(stageKey)}
disabled={isGenerating || isStageLoading}
className={`flex w-full items-center justify-between text-sm ${
active ? 'text-slate-900' : 'text-slate-400'
} disabled:opacity-50`}
>
<div className="flex items-center gap-2">
<span
className={`flex h-6 w-6 items-center justify-center rounded-full border text-xs font-semibold ${
active ? 'border-blue-600 text-blue-600' : 'border-slate-300 text-slate-400'
}`}
>
{number}
</span>
<span className="font-medium">{label}</span>
</div>
</button>
)
return (
<div className="relative flex h-screen bg-slate-100 text-slate-900 overflow-hidden">
<aside className="w-72 bg-white border-r border-slate-200 flex flex-col px-5 py-6 gap-6">
<div className="flex items-center justify-between">
<button className="text-xs text-slate-500" onClick={onBack}>
Stirling
</button>
<button className="text-xs text-slate-400">Create</button>
</div>
<div className="space-y-4">
<StageItem number={1} label="Outline" active={stage === 'outline'} stageKey="outline" />
<StageItem number={2} label="Text" active={stage === 'text'} stageKey="text" />
<StageItem number={3} label="Review" active={stage === 'styling'} stageKey="styling" />
<StageItem number={4} label="Export" active={stage === 'review'} stageKey="review" />
</div>
{stage === 'outline' && (
<div className="mt-2 space-y-2">
<button
className="w-full rounded-lg border border-slate-300 px-4 py-2 text-sm font-semibold text-slate-700 hover:bg-slate-100 disabled:opacity-50"
onClick={onAiOutline}
disabled={isGenerating || isStageLoading}
>
Auto-fill Fields
</button>
<button
className="w-full rounded-lg bg-blue-600 px-4 py-2 text-sm font-semibold text-white hover:bg-blue-700 disabled:opacity-50"
onClick={approveOutline}
disabled={isGenerating || isStageLoading}
>
Approve and Continue
</button>
</div>
)}
{stage === 'text' && (
<button
className="mt-2 w-full rounded-lg bg-blue-600 px-4 py-2 text-sm font-semibold text-white hover:bg-blue-700 disabled:opacity-50"
onClick={approveDraft}
disabled={isGenerating || isStageLoading}
>
Approve and Continue
</button>
)}
{stage === 'styling' && (
<button
className="mt-2 w-full rounded-lg bg-blue-600 px-4 py-2 text-sm font-semibold text-white hover:bg-blue-700 disabled:opacity-50"
onClick={saveAndReview}
disabled={isGenerating || isStageLoading}
>
Continue to Export
</button>
)}
{stage === 'review' && (
<div className="space-y-2 text-xs text-slate-500">
<div className="font-semibold text-slate-700">Export</div>
<div className="rounded-lg border border-slate-200 px-3 py-2 text-slate-600">
{docLabel} - 1 page
</div>
<button
type="button"
className="inline-flex w-full items-center justify-center rounded-lg border border-slate-300 px-3 py-2 text-[11px] text-slate-600"
onClick={onOpenImportTemplate}
>
Import template from PDF
</button>
{imagePlaceholdersCount > 0 && (
<div className="rounded-lg border border-amber-200 bg-amber-50 px-3 py-2 text-amber-700">
<div className="font-semibold">Image placeholders detected</div>
<p className="mt-1 text-[11px] text-amber-700">
Add images now or remove placeholders before exporting.
</p>
<div className="mt-2 flex flex-col gap-2">
<label className="inline-flex w-full cursor-pointer items-center justify-center rounded-md border border-amber-300 bg-white px-3 py-1 text-[11px] text-amber-700">
{isAssetUploading ? 'Uploading...' : 'Add image'}
<input
type="file"
accept="image/png,image/jpeg,image/jpg,image/gif"
className="hidden"
disabled={isAssetUploading}
onChange={(event) => {
const file = event.target.files?.[0]
if (file) onAddPlaceholderImage(file)
event.currentTarget.value = ''
}}
/>
</label>
<button
type="button"
className="inline-flex w-full items-center justify-center rounded-md border border-slate-300 px-3 py-1 text-[11px] text-slate-600"
onClick={onRemovePlaceholders}
disabled={isAssetUploading}
>
Remove placeholders
</button>
</div>
{assetError && <div className="mt-2 text-[11px] text-amber-600">{assetError}</div>}
</div>
)}
{currentDoc?.pdfUrl && (
<a
href={currentDoc.pdfUrl}
target="_blank"
rel="noopener noreferrer"
className="inline-flex w-full items-center justify-center rounded-lg border border-slate-300 px-3 py-2 text-slate-700"
>
Export and Close
</a>
)}
</div>
)}
</aside>
<main className="flex-1 overflow-auto px-8 py-10">
<div className="mx-auto max-w-4xl space-y-8">
<form
onSubmit={onChatSubmit}
className="rounded-2xl border border-slate-200 bg-white p-4 shadow-sm"
>
<div className="flex items-center gap-3">
<button
type="button"
className="flex h-9 w-9 items-center justify-center rounded-full border border-slate-200 text-slate-400"
>
+
</button>
<div className="flex-1">
<input
className="w-full text-sm text-slate-700 placeholder:text-slate-400 focus:outline-none"
value={prompt}
onChange={(event) => onPromptChange(event.target.value)}
placeholder="Describe what you want to create..."
onKeyDown={onKeyDown}
/>
</div>
<button
type="button"
className="rounded-full border border-slate-200 px-3 py-1 text-xs text-slate-500"
onClick={onOpenImportTemplate}
>
Template
</button>
<button
type="submit"
className="flex h-9 w-9 items-center justify-center rounded-full bg-slate-900 text-white"
>
^
</button>
</div>
</form>
<div className="rounded-2xl border border-slate-200 bg-white p-8 shadow-sm">
{renderStageContent()}
</div>
</div>
</main>
<aside className="w-16 border-l border-slate-200 bg-slate-50 flex flex-col items-center py-6 gap-4 text-slate-400 text-xs">
<div className="h-10 w-10 rounded-xl bg-slate-200" />
<div className="h-10 w-10 rounded-xl bg-slate-200" />
<div className="h-10 w-10 rounded-xl bg-slate-200" />
<div className="h-10 w-10 rounded-xl bg-slate-200" />
</aside>
</div>
)
}
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@@ -1,367 +0,0 @@
import { useCallback, useEffect, useRef, useState } from 'react'
interface UseSpeechCaptureOptions {
appendPrompt: (text: string) => void
}
// Diagnostic logging
const log = (category: string, message: string, data?: any) => {
const timestamp = new Date().toISOString().split('T')[1]
console.log(`[${timestamp}] [${category}]`, message, data !== undefined ? data : '')
}
export function useSpeechCapture({ appendPrompt }: UseSpeechCaptureOptions) {
const [isRecording, setIsRecording] = useState(false)
const [whisperStatus, setWhisperStatus] = useState<string | null>(null)
const [waveformHistory, setWaveformHistory] = useState<number[][]>([])
// Refs for audio visualization
const audioContextRef = useRef<AudioContext | null>(null)
const analyserRef = useRef<AnalyserNode | null>(null)
const streamRef = useRef<MediaStream | null>(null)
const animationFrameRef = useRef<number | null>(null)
const isRunningRef = useRef(false)
const lastUpdateTimeRef = useRef(0)
// Refs for speech recognition
const speechRecRef = useRef<any>(null)
const allTranscriptsRef = useRef<string[]>([])
const shouldTranscribeOnStopRef = useRef(false)
const appendPromptRef = useRef(appendPrompt)
// Keep appendPrompt ref updated
useEffect(() => {
appendPromptRef.current = appendPrompt
}, [appendPrompt])
// Animation loop function - defined outside to avoid recreation
const runVisualization = useCallback(() => {
const animate = () => {
// Check if we should continue
if (!isRunningRef.current) {
log('ANIM', 'Animation stopped - isRunningRef is false')
return
}
// Schedule next frame immediately
animationFrameRef.current = requestAnimationFrame(animate)
// Check if analyser exists
if (!analyserRef.current) {
log('ANIM', 'No analyser available')
return
}
// Throttle to ~15fps
const now = Date.now()
if (now - lastUpdateTimeRef.current < 66) {
return
}
lastUpdateTimeRef.current = now
// Get frequency data
const bufferLength = analyserRef.current.frequencyBinCount
const dataArray = new Uint8Array(bufferLength)
analyserRef.current.getByteFrequencyData(dataArray)
// Sample frequency data
const levels: number[] = []
const numSamples = 40
const step = Math.floor(bufferLength / numSamples)
for (let i = 0; i < numSamples; i++) {
const index = i * step
const value = dataArray[index] / 255
levels.push(value)
}
// Update waveform history - keep max 200 columns (enough for any reasonable width)
setWaveformHistory((prev) => {
const newHistory = [...prev, levels]
return newHistory.slice(-200)
})
}
log('ANIM', 'Starting animation loop')
isRunningRef.current = true
lastUpdateTimeRef.current = 0
animate()
}, [])
// Stop visualization
const stopVisualization = useCallback(() => {
log('STOP_VIS', 'Stopping visualization')
isRunningRef.current = false
if (animationFrameRef.current) {
cancelAnimationFrame(animationFrameRef.current)
animationFrameRef.current = null
log('STOP_VIS', 'Cancelled animation frame')
}
if (audioContextRef.current) {
audioContextRef.current.close().catch(() => {})
audioContextRef.current = null
log('STOP_VIS', 'Closed audio context')
}
if (streamRef.current) {
streamRef.current.getTracks().forEach((track) => track.stop())
streamRef.current = null
log('STOP_VIS', 'Stopped stream tracks')
}
analyserRef.current = null
setWaveformHistory([])
}, [])
// Start recording
const startRecording = useCallback(async () => {
log('START', 'startRecording called, isRecording:', isRecording)
if (isRecording) {
log('START', 'Already recording, returning')
return
}
// Set recording state immediately
setIsRecording(true)
setWhisperStatus('Listening...')
// Initialize with placeholder
setWaveformHistory([Array(40).fill(0.1)])
log('START', 'Set initial placeholder waveform')
// Set up audio visualization
try {
log('AUDIO', 'Requesting microphone access')
const stream = await navigator.mediaDevices.getUserMedia({ audio: true })
streamRef.current = stream
log('AUDIO', 'Got microphone stream')
const AudioContextClass = window.AudioContext || (window as any).webkitAudioContext
const audioContext = new AudioContextClass()
log('AUDIO', 'Created audio context, state:', audioContext.state)
if (audioContext.state === 'suspended') {
log('AUDIO', 'Resuming suspended audio context')
await audioContext.resume()
log('AUDIO', 'Audio context resumed, state:', audioContext.state)
}
const analyser = audioContext.createAnalyser()
analyser.fftSize = 256
analyser.smoothingTimeConstant = 0.8
const source = audioContext.createMediaStreamSource(stream)
source.connect(analyser)
log('AUDIO', 'Connected analyser to stream')
audioContextRef.current = audioContext
analyserRef.current = analyser
// Start visualization
runVisualization()
log('AUDIO', 'Started visualization')
} catch (err) {
log('AUDIO', 'Failed to set up audio:', err)
// Continue with placeholder even if audio fails
isRunningRef.current = true
}
// Set up speech recognition
const SpeechRec = (window as any).SpeechRecognition || (window as any).webkitSpeechRecognition
if (SpeechRec) {
log('SPEECH', 'Setting up speech recognition')
allTranscriptsRef.current = []
const rec = new SpeechRec()
rec.continuous = true
rec.interimResults = true
rec.lang = 'en-US'
rec.onstart = () => {
log('SPEECH', 'Speech recognition started')
}
rec.onresult = (event: any) => {
log('SPEECH', `Got result, resultIndex: ${event.resultIndex}, results count: ${event.results.length}`)
// Only process new results starting from resultIndex
for (let i = event.resultIndex; i < event.results.length; i++) {
const result = event.results[i]
const transcript = result[0]?.transcript
// Only store final segments to avoid duplicates from interim results
if (result.isFinal && transcript) {
allTranscriptsRef.current = [...allTranscriptsRef.current, transcript]
log('SPEECH', 'Added final transcript:', transcript)
}
}
log('SPEECH', 'Accumulated transcripts:', allTranscriptsRef.current.join(' | '))
}
rec.onerror = (event: any) => {
log('SPEECH', 'Speech recognition error:', event.error)
// Don't stop on common errors
if (event.error === 'no-speech' || event.error === 'aborted') {
return
}
}
rec.onend = () => {
log('SPEECH', `Speech recognition ended, isRunningRef: ${isRunningRef.current}, shouldTranscribe: ${shouldTranscribeOnStopRef.current}`)
// If we're intentionally stopping and should transcribe, do it now
if (shouldTranscribeOnStopRef.current) {
shouldTranscribeOnStopRef.current = false
if (allTranscriptsRef.current.length > 0) {
const transcript = allTranscriptsRef.current.join(' ').replace(/\s+/g, ' ').trim()
log('SPEECH', 'Final transcript from onend:', transcript)
if (transcript) {
appendPromptRef.current(transcript)
setWhisperStatus('Transcription ready')
setTimeout(() => setWhisperStatus(null), 1500)
}
}
allTranscriptsRef.current = []
return // Don't restart
}
// Restart if still recording
if (isRunningRef.current) {
log('SPEECH', 'Restarting speech recognition')
setTimeout(() => {
if (isRunningRef.current && speechRecRef.current) {
try {
// Create new instance
const newRec = new SpeechRec()
newRec.continuous = true
newRec.interimResults = true
newRec.lang = 'en-US'
newRec.onstart = rec.onstart
newRec.onresult = rec.onresult
newRec.onerror = rec.onerror
newRec.onend = rec.onend
speechRecRef.current = newRec
newRec.start()
log('SPEECH', 'Speech recognition restarted')
} catch (err) {
log('SPEECH', 'Failed to restart speech recognition:', err)
}
}
}, 100)
}
}
speechRecRef.current = rec
try {
rec.start()
log('SPEECH', 'Speech recognition start() called')
} catch (err) {
log('SPEECH', 'Failed to start speech recognition:', err)
}
} else {
log('SPEECH', 'Speech recognition not available')
}
log('START', 'startRecording completed')
}, [isRecording, runVisualization])
// Stop recording
const stopRecording = useCallback((shouldTranscribe = true) => {
log('STOP', 'stopRecording called, shouldTranscribe:', shouldTranscribe)
// Set flag for onend handler to know whether to transcribe
shouldTranscribeOnStopRef.current = shouldTranscribe
// Stop visualization first
stopVisualization()
// Stop speech recognition - onend will handle transcription
if (speechRecRef.current) {
try {
speechRecRef.current.stop()
log('STOP', 'Stopped speech recognition, waiting for onend')
} catch (err) {
log('STOP', 'Error stopping speech recognition:', err)
// If stop fails, handle transcription here
if (shouldTranscribe && allTranscriptsRef.current.length > 0) {
const transcript = allTranscriptsRef.current.join(' ').replace(/\s+/g, ' ').trim()
if (transcript) {
appendPrompt(transcript)
setWhisperStatus('Transcription ready')
}
}
allTranscriptsRef.current = []
}
speechRecRef.current = null
} else {
// No speech recognition active, clear state
allTranscriptsRef.current = []
}
setIsRecording(false)
log('STOP', 'stopRecording completed')
}, [appendPrompt, stopVisualization])
// Toggle recording
const toggleRecording = useCallback(() => {
log('TOGGLE', 'toggleRecording called, isRecording:', isRecording)
if (isRecording) {
stopRecording(true)
} else {
startRecording()
}
}, [isRecording, startRecording, stopRecording])
// Cancel recording
const cancelRecording = useCallback(() => {
log('CANCEL', 'cancelRecording called')
if (isRecording) {
stopRecording(false)
}
}, [isRecording, stopRecording])
// Accept recording
const acceptRecording = useCallback(() => {
log('ACCEPT', 'acceptRecording called')
if (isRecording) {
stopRecording(true)
}
}, [isRecording, stopRecording])
// Cleanup on unmount
useEffect(() => {
return () => {
log('CLEANUP', 'Component unmounting')
isRunningRef.current = false
if (animationFrameRef.current) {
cancelAnimationFrame(animationFrameRef.current)
}
if (audioContextRef.current) {
audioContextRef.current.close().catch(() => {})
}
if (streamRef.current) {
streamRef.current.getTracks().forEach((track) => track.stop())
}
if (speechRecRef.current) {
try {
speechRecRef.current.stop()
} catch {}
}
}
}, [])
return {
isRecording,
whisperLoading: false, // Not used in speech recognition mode
whisperStatus,
toggleRecording,
cancelRecording,
acceptRecording,
audioLevels: [], // Not used anymore
waveformHistory,
}
}

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