Commit graph

4 commits

Author SHA1 Message Date
nickviljoen
6592c38b0a Add per-user client access control and admin management
Default-deny access model with admin grant/revoke via new User Access
tab. /api/clients filters by user grants; client-scoped endpoints
enforce access server-side. Admin role and client grants persist in
user_access.json with audit trail in usage logs.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-04-22 12:33:09 +02:00
nickviljoen
06a6562a92 Admin as full page, track user logins, fix reporting bugs
- Convert admin panel from popup modal to dedicated full page section
- Log user_login events on auth status check to track all visitors
- Admin user list now shows all users (login visitors + analysts)
- Fix cost field name (total_cost_usd vs estimated_cost_usd fallback)
- Round scores to 2 decimal places in reporting tables
- Reset reporting dashboard when switching clients
- Clear stale data on client switch before loading new client

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-26 13:03:52 +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