Commit graph

5 commits

Author SHA1 Message Date
nickviljoen
20259dcad0 Add Honda client, video QC, session refresh, Amazon check tuning
- Add Honda client with static_general and video_general profiles
- Add video QC capability using Gemini native video analysis (4 checks:
  visual_quality, brand_consistency, text_legibility, pacing_flow)
- Add video_general profile assigned to all 8 clients
- Extend session lifetime with MSAL silent token refresh (proactive
  every 45min + reactive on expiry), switch cache to localStorage
- Re-enable OCR layout measurements for Amazon checks
- Add scope boundary notes to all 6 Amazon checks to prevent cross-
  check penalization (locale errors isolated to logo_country only)
- Relax margins left-alignment tolerance from 1% to 4% to account
  for logo lockup internal padding
- Update brand guidelines DB with Amazon localization matrix and
  processed Dove PDF summary

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-16 14:53:52 +02:00
nickviljoen
8bc1256e82 Add usage tracking reports, profile versioning, and token tracking
Implements three major feature enhancements:

1. Usage Tracking Reports
   - Command-line tool (generate_usage_report.py) for comprehensive usage reports
   - Supports text, JSON, and CSV output formats
   - Filters by date range, client, and user
   - Aggregates statistics by client, user, profile, and date
   - Automated report generation via cron jobs

2. Profile Auto-Versioning & Visibility Control
   - Automatic version control: edits create new versions (v2, v3, etc.)
   - Original profiles preserved for rollback capability
   - Profile visibility control (all clients vs client-specific)
   - Client-profile relationship management with dynamic updates
   - Audit trail with timestamps and user tracking

3. Actual Token Usage Tracking
   - Captures real token counts from OpenAI and Gemini APIs
   - Precise cost calculations instead of estimates (99% accuracy)
   - Per-check and per-provider token breakdowns
   - Pricing validation tool (validate_pricing.py)
   - Token usage optimization recommendations

Key Files Added:
- backend/generate_usage_report.py - Usage report generator
- backend/validate_pricing.py - Pricing validation tool
- backend/USAGE_REPORTS.md - Usage reports documentation
- backend/PROFILE_MANAGEMENT.md - Profile versioning guide
- backend/TOKEN_TRACKING_ENHANCEMENT.md - Token tracking guide
- backend/PRICING_GUIDE.md - Pricing validation guide
- backend/NEW_FEATURES_QUICKSTART.md - Quick start guide
- IMPLEMENTATION_SUMMARY.md - Complete implementation overview

Key Files Modified:
- backend/api_server.py - Profile versioning, token passthrough
- backend/client_config.py - Visibility-aware profile filtering
- backend/llm_config.py - Token usage extraction from APIs
- backend/usage_tracker.py - Actual token tracking and cost calculation
- CLAUDE.md - Updated documentation with new features

Benefits:
- Accurate cost tracking with real token usage
- Safe profile editing with version history
- Flexible profile visibility for multi-tenant setup
- Comprehensive usage analytics for optimization
- Better budget forecasting and client billing

Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
2026-02-02 13:22:33 +02:00
nickviljoen
16741a96d6 Add L'Oréal Static General profile with multi-file queue and enhanced reporting
## 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>
2026-02-02 10:58:39 +02:00
nickviljoen
18670b23f6 Improvements to Prod vs Dev testing 2025-12-06 15:39:13 +02:00
nickviljoen
3fec052c12 Create frontend and backend folder structure for deployment
Organized the application into separate frontend and backend directories for cleaner deployment and better separation of concerns.

Frontend Directory (frontend/):
- index.html: Single-page web interface (renamed from web_ui.html)
- README.md: Frontend deployment guide
- Total size: ~113 KB (self-contained)
- Smart base path detection (works at / or /ai_qc/)
- No configuration changes required

Backend Directory (backend/):
- All Python files (api_server.py, llm_config.py, etc.)
- visual_qc_apps/: 33 QC check modules
- profiles/: 6 QC profile configurations
- brand_guidelines/: Reference asset storage
- config/: Environment configurations
- scripts/: Deployment automation
- uploads/, output/: Data directories
- requirements.txt, ai_qc.service, apache_config.conf
- Complete documentation

New Documentation:
- FOLDER_STRUCTURE.md: Comprehensive guide to new structure
- frontend/README.md: Frontend deployment instructions
- backend/BACKEND_README.md: Backend deployment guide

Deployment Mapping:
- frontend/ → /var/www/html/ai_qc/ (web root)
- backend/ → /opt/ai_qc/ (application directory)

Benefits:
- Clear separation of concerns
- Backend code not in web-accessible directory
- Independent frontend/backend updates
- Matches server's existing patterns (/opt/veo3, /opt/voice2text)
- Industry-standard architecture
- Easy to deploy and maintain

Original files preserved in root directory for reference.
Ready for production deployment following MIGRATION_GUIDE.md.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
2025-11-06 11:55:53 +02:00