Backend: thread on_fallback callback through analysis chain
(gemini_service → agents → analysis_service → handlers). The handler
sends a 'model_fallback' WebSocket message exactly once per analysis
when the primary model is unavailable.
Frontend: handle 'model_fallback' WS message and show a dismissible
yellow toast at the bottom of the screen with an 8-second auto-dismiss.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Previously, proof metadata collected during upload was only used for database
persistence. Now it flows through the entire analysis pipeline so agents can
tailor their feedback to the specific channel and format being reviewed.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When a subsequent revision of a proof is uploaded, the analysis now takes
place in context of the previous version's results. The system identifies:
- Resolved issues: fixed in the new revision
- Outstanding issues: still present from previous version
- New issues: introduced in the new revision
Key changes:
- Add resolvedIssues, outstandingIssues, newIssues fields to SubReview
- Add PreviousReviewContext model for passing previous review data
- Update all specialist agents to accept previous_review context
- Extend GeminiService with include_revision_fields parameter
- Add get_latest_version_review() repository method
- Update LeadAgent to synthesize cross-version context in summary
- Fetch previous analysis in WebSocket handler for revisions
First version analysis continues to work exactly as before with revision
fields set to null.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Run all 4 specialist agents (Legal, Brand, Channel Best Practices,
Channel Tech Specs) concurrently instead of sequentially. This reduces
total analysis time to roughly the duration of the slowest agent rather
than the sum of all agent times.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Remove Tone Agent (tone is now part of Brand specs)
- Split Channel Agent into Channel Best Practices Agent and Channel Tech Specs Agent
- Convert Legal Agent from stub to full Gemini-powered implementation
- Add new prompt files for channel_best_practices.md, channel_tech_specs.md, legal.md
- Update ReferenceDocsService with new methods for loading specs
- Update schemas and analysis service to use new agent structure
- Update all frontend components to use new agent names and properties
- Update mock data in Projects.tsx and Campaigns.tsx
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Add brand field to AnalyzeProofOptions interface and WebSocket message
- Pass campaign's brandGuidelines to analyzeProof in App.tsx (upload & retry)
- Extract brand from WebSocket message in handlers.py and pass to analysis
- Update AnalysisService.analyze_proof to accept brand parameter
- Refactor BrandAgent to dynamically select brand spec based on brand param
- Add get_barclays_brand_spec() method to ReferenceDocsService (placeholder)
The brand agent now uses the appropriate specification (Barclaycard spec or
Barclays spec when available) based on the campaign's brandGuidelines setting.
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
PDFs are now converted to PNG images at 200 DPI before being sent to
Gemini for analysis. This fixes the unreliable iframe-based PDF preview
and ensures all pages are properly analyzed.
- Add PyMuPDF dependency for PDF rasterization
- Create pdf_service.py with rasterize() and get_page_count()
- Update agent interfaces to accept list of images for multi-page support
- Add analyze_with_images() to Gemini service for multi-image analysis
- Return rasterized PDF pages via WebSocket for frontend display
- Add page navigation UI for multi-page PDFs in preview components
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
