Adds Tesseract-based OCR pre-processing that computes pixel-level text
positions, margins, spacing, and alignment before LLM analysis. This
enables detection of subtle layout differences that vision models miss
(e.g. 2.8% vs 6.4% headline margin, 83px vs 39px date gap).
OCR measurements injected into 10 checks across all client profiles:
- Amazon: margins, typography, headline_layout
- Static General: element_alignment, safety_area, visual_hierarchy_general,
text_readability_general, text_edge_clearance
- L'Oreal: text_readability
- Diageo/Unilever KV: visual_hierarchy
Non-blocking: if Tesseract is unavailable, checks run with visual
estimation only. Production requires: sudo apt install tesseract-ocr
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
## New Features
### L'Oréal Static General Profile
- Created new profile with 3 checks optimized for digital marketing assets
- Even weighting (33.3% each) for 100-point scoring scale
- Removed print-specific requirements (3m viewing distance)
- Focus on marketing text vs product packaging distinction
### Multi-File Queue System (web_ui.html)
- Added file queue functionality for batch processing
- Users can now upload and process multiple files simultaneously
- Queue displays file status (pending, analyzing, complete, error)
- Individual file removal and queue clearing options
- Progress tracking for batch operations
### New General QC Checks
1. background_contrast_general
- Optimized for digital assets (no distance requirements)
- Checks logo, product, and marketing text contrast
- Detects overlapping and blending issues
- Provides element-by-element breakdown
2. text_readability_general
- Focus on marketing text only (excludes product packaging)
- Checks for overlapping elements
- Digital readability optimization
- Specific issue identification
3. language_consistency (enhanced)
- Better distinction between marketing and packaging text
- Detailed language detection and reporting
- Lists specific text analyzed
### Usage Tracking System
- Added usage_tracker.py for analysis logging
- Tracks user activity, profile usage, and costs
- Daily log files in JSONL format
- Cost estimation per LLM provider
## Bug Fixes
### Authentication & User Management
- Fixed Flask 'g' import missing issue
- Fixed user info access in background threads
- Pass user_info to threads instead of accessing g.user
- Improved error handling for usage logging
### HTML Report Generation
- Fixed missing analysis details in reports
- Now extracts and displays all JSON fields properly
- Shows comprehensive breakdowns:
- Analysis details
- Elements checked (logo, product, text)
- Marketing text found
- Issues identified
- Specific recommendations
- No more blank "Pass/Fail" results
### Scoring System
- Fixed usage_tracker to handle dict of check results (not list)
- Better handling of model_used field variations
- Skip non-dict check results gracefully
## Configuration Changes
### Model Versions (llm_config.py)
- Fixed invalid GPT-4.1 model ID to gpt-4o
- Added Gemini 3 Pro beta model option
- AVAILABLE_MODELS dict for UI selection
- Model version override support
### Profile Updates
- Static General: 3 checks, total weight 10.0
- Each check: text_readability_general (3.33), background_contrast_general (3.33), language_consistency (3.34)
- Maximum score: 100 points
## Technical Improvements
- Enhanced prompt engineering for consistent LLM outputs
- Mandatory detailed explanations in all checks
- Structured JSON responses with comprehensive fields
- Better error messages and fallback handling
- Client configuration support (client_config.py)
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
