Lifted JWT-cookie auth pattern from the AI QC sibling project:
core/auth/middleware.py validates Azure AD JWTs and stores them in
an httpOnly cookie (hm_aiqc_auth_token). Tenant membership is
enforced by JWTValidator's tid check, which is sufficient for the
tenant-wide access policy chosen for this project.
templates/login.html now drives an MSAL.js popup that POSTs the
ID token to /auth/login. base.html exposes Azure config to all
pages so the logout button can also clear the MSAL session.
app.py's @before_request now checks the JWT cookie and exposes
g.user; modules read user identity via core.auth.current_user_email
so usage logs and created_by columns now record the signed-in
user's email rather than a session value.
Legacy username/password code removed: top-level auth_middleware.py,
jwt_validator.py, deploy/generate_password.py.
A. Excel upload — /campaigns/pricing/upload now accepts .xlsx/.xls
alongside .pdf. File picker in the campaigns UI matches.
B. Deterministic Excel parser (openpyxl, no LLM) — looks for H&M-style
mastersheets:
- 'MPC Prices' sheet -> flat list of {product_id, language, country,
price, currency, product_name} entries (this is the gold mine).
- Regional sheets (AME/CEU/EEU/...) -> formatted prices per locale
used to derive currency symbol, position, decimal/thousands
separators. Skips OLD/COPY sheets.
Verified against the attached 1013A mastersheet: 448 price entries
across 7 products x 74 locales, 139 locale format entries.
Parser lives in modules/campaigns/pricing_parser.py alongside the
existing PDF path (which now also returns the structured form with
empty _prices).
New lookup shape stored in PricingReference.parsed_data_json:
{"_format": {"en-US": {currency_code, symbol, position, ...}, ...},
"_prices": [{product_id, language, country, price, currency,
product_name}, ...]}
Legacy flat {"<code>": {...}} is still recognised (treated as _format
only) for backwards compatibility with the legacy global JSON import.
Model helpers added:
- PricingReference.get_format_map()
- PricingReference.get_prices()
to_dict() now reports price_count alongside entry_count.
C. Upgraded price_currency_check.py — when a pricing reference with
_prices is attached, the check runs a deterministic comparison:
detected price(s) -> normalize (_normalize_price handles '$49.99',
'39,99 €', 'CHF 49.95', '1.234,56', 'Rs. 2,799', '13 995 Ft', '349,-',
'0.999.000'...) -> compare with tol=0.005 against the expected
per-locale rows. LLM-based campaign-sheet fallback only runs if no
_prices are present (legacy PDF reference or has_pricing campaign
presentation).
D. Video QC price check — new _run_price_check step in the executor.
Parses filename (Market_lang_CampaignNum_... -> 'lang-Market' locale),
detects prices across frames via the same Gemini/GPT-4o path the
other checks use, then deterministic-validates against the attached
pricing reference. Skipped if no pricing ref, unknown locale, GEN/CEN
markets, or no price visible in video.
Overall video score now uses weighted mean of active (non-skipped)
checks (visual_quality w=50, censorship w=50, price_currency w=30)
instead of the hardcoded 50/50 split — so skipping any one check
falls through cleanly.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The "Global Pricing Reference" is no longer a single file at
storage/reference/global_pricing.json. Pricing references are now
first-class DB rows (PricingReference model), uploadable as a library
in the Campaigns tab and selectable per-run alongside the campaign
presentation dropdown on the HM QC and Video QC configure pages.
New:
- core/models/pricing_reference.py — PricingReference model: id, name,
pdf_filename, pdf_path, parsed_content, parsed_data_json, status,
created_at/by. get_lookup() deserializes parsed_data_json; to_dict()
powers the dropdown API.
- /campaigns/pricing/upload — creates a PricingReference row, saves PDF
under storage/pricing_references/<id>/, kicks off background parse.
- /campaigns/pricing/<id> DELETE, /campaigns/api/pricing/list,
/campaigns/api/pricing/status/<id>.
- Campaigns index: "Pricing References" table card (mirrors the
presentations card) + upload form with optional name field.
Changed:
- pricing_parser: parse_pricing_pdf_to_dict returns (dict, raw_text);
new parse_pricing_reference(id) runs the parse against a DB row and
sets status to ready/error. Legacy file-based path removed.
- QCExecutor and VideoQCExecutor accept pricing_reference_id; load the
row into context['pricing_reference']={id, name, lookup}.
- BatchQCExecutor and BatchVideoQCExecutor thread pricing_reference_id
through to per-file executors.
- price_currency_check._validate_currency reads context instead of the
disk file; returns 'skipped_no_reference' if no ref attached.
- HM QC + Video QC /execute and /execute/batch routes pass
pricing_reference_id from the JSON payload.
- Configure templates for HM QC and Video QC add a second dropdown
"Pricing Reference (Optional)" loaded from /campaigns/api/pricing/list.
Backwards compatibility:
- app.py: on startup, if storage/reference/global_pricing.json exists
and the pricing_references table is empty, import it as a
"Default (legacy global)" PricingReference row so existing installs
keep a valid reference attached (user can pick it at configure time).
- config.py: retains GLOBAL_PRICING_{PDF,JSON}_PATH for the legacy
importer; adds PRICING_REF_STORAGE_PATH for the new per-row storage.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
UsageLog now records input_tokens and output_tokens separately and costs
each side at its real rate. The old single 'blended' rate underpriced
input-heavy workloads (vision/QC) and overpriced output-heavy ones.
COST_PER_MILLION_TOKENS rebuilt against the live OpenAI, Gemini and
Anthropic pricing pages (GPT-5.4 family, GPT-4.x, o4-mini; Gemini 2.5
Pro/Flash/Flash-Lite + 1.5 legacy; Claude 4.7/4.6/4.5 + 3.x legacy).
Unknown models now warn instead of silently defaulting to $5/1M.
Adds idempotent ALTER TABLE migration on startup so existing SQLite DBs
pick up the new columns. Dashboard + API surface the input/output split.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
- Accept .xlsx/.xls uploads alongside PDFs in campaigns module
- New parse_campaign_excel() in services.py using openpyxl
- Converts all sheets to structured text (headers + rows) for LLM use
- Upload form now accepts both PDF and Excel files
- Added openpyxl to requirements.txt
Workflow: upload campaign presentation (PDF) + media plan (Excel with
has_pricing checked) for the same campaign ID. The price check will
use the Excel data to validate actual prices per country.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Global pricing parser now explicitly extracts format only (symbol,
position, separators) — ignores actual price values in the reference doc
- Executors load ALL ready documents for a campaign (not just the latest),
combining their content — supports guidelines + media plan side by side
- Campaign context now separates pricing_content (from has_pricing docs)
from general parsed_content (all docs combined)
- Price check uses pricing_content specifically for actual price validation
- Report header shows document count (e.g., "1022B - AW25 Display (2 docs) + pricing")
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
- Fix background parsing thread: pass app reference explicitly instead of
trying to access current_app inside the thread (was silently failing)
- Add progress bar with animated stages during upload and parsing
- Add data-id/data-status attributes to table rows for auto-polling
- On page load, automatically poll any pending/parsing rows and update
their status badges in-place (fixes stale "Pending" on tab return)
- Immediately inject new row into table after upload so user sees it
without needing to refresh
- Remove broken _parse_pricing_background function
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Introduces a new Campaigns module for uploading campaign presentation PDFs
that QC checks reference to validate assets against campaign-specific
guidelines (typography, layout, copy, pricing format). Also adds a global
pricing reference system that maps country codes to currency symbols and
formats for deterministic price/currency validation.
- New CampaignPresentation model + campaigns blueprint with CRUD routes
- PDF parsing via LlamaParse (text + multimodal page images)
- Global pricing PDF parsed into structured JSON lookup
- Campaign context injected into both image and video QC executors
- Quality checks enhanced with campaign guidelines in LLM prompts
- Price/currency check uses global pricing lookup (saves an LLM call)
- Campaign dropdown added to HM QC and Video QC configure pages
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
