Daily Technical Work Report

Project: AnanaCare Face Validator
Date: February 23, 2026
Status: Operational (Green)
Environments: Railway (Backend), Vercel (Frontend)

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1. Model Asset Recovery and Flatbuffer Correction

Summary:
The backend system encountered a critical startup failure due to invalid model assets. The error, ValueError: The model is not a valid Flatbuffer buffer, was traced to improper handling of large binary model files by Git LFS. Instead of the required 14.2MB binary files, only 132-byte pointer files were present, rendering the MediaPipe neural network models unusable.

Actions Taken:

• De-initialized Git LFS locally to prevent further pointer file issues.
• Re-indexed the weights/ directory to ensure all model files were recognized.
• Committed the raw binary .task and .onnx model files directly to the repository using standard Git, bypassing LFS.

Outcome:
The FastAPI backend now successfully loads all required models during the application lifespan event, restoring full inference capability.

Additional Notes:

• Verified model integrity post-commit.
• Documented the process for future asset management.

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2. Frontend Migration: Streamlit to Svelte 5

Summary:
The legacy Streamlit-based Python frontend was migrated to a modern, production-ready Svelte 5 application. This transition was aimed at improving UI responsiveness, maintainability, and user experience.

Key Implementation Details:

• Developed a reactive UI leveraging Svelte’s $state for real-time feedback and state management.
• Integrated FormData for efficient binary image upload handling.
• Utilized URL.createObjectURL to provide instant local image previews, enhancing user interaction.
• Implemented a health-check polling mechanism to continuously monitor backend availability upon frontend mount.

Outcome:

• Achieved a significant reduction in UI overhead.
• Delivered a faster, more professional, and scalable testing interface for production use.

Additional Enhancements:

• Improved error handling and user notifications.
• Modularized frontend components for easier future expansion.

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3. Cross-Origin Resource Sharing (CORS) Issue Resolution

Summary:
The frontend experienced a TypeError: Failed to fetch despite the backend returning 200 OK responses. Investigation revealed that the browser’s security policy was blocking requests due to missing CORS headers, specifically Access-Control-Allow-Origin.

Actions Taken:

• Integrated CORSMiddleware into the FastAPI backend to manage cross-origin requests.
• Critically, reordered middleware registration to ensure CORS handling occurs before API routers are mounted.
• Sanitized and validated allowed origin strings to precisely match the Vercel deployment URL, ensuring secure and functional cross-domain communication.

Outcome:

• Established reliable, end-to-end communication between the Vercel frontend and Railway backend.
• Resolved all browser-side fetch errors related to CORS.

Security Considerations:

• Confirmed that only the intended frontend origin is permitted, mitigating potential CORS vulnerabilities.

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4. System Health Monitoring and Logging Improvements

Summary:
To support robust monitoring and automated health checks, several optimizations were implemented in the backend API.

Key Improvements:

• Added a root @app.get("/") endpoint to eliminate 404 errors during automated health pings from Vercel and Railway.
• Enhanced the /health endpoint to support both GET and OPTIONS methods, improving compatibility with various monitoring tools.

Current System Metrics:

• Backend Latency: ~2.0s - 4.5s for complete face and landmark analysis.
• Success Rate: 100% on valid CORS handshakes and health checks.

Additional Logging:

• Expanded log detail for error tracing and performance monitoring.

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Next Steps & Recommendations

1. Model Optimization:
   Current logs indicate GPU support is not available. Recommend explicitly setting the MediaPipe delegate to CPU to suppress these warnings and ensure consistent performance across environments.
2. Analysis Logic Refinement:
   With stable backend-frontend connectivity, consider refining the Eye Detection and Smile Detection thresholds in app/core/analyze.py to improve detection accuracy and user experience.
3. Performance Review:
   Suggest preparing a detailed summary of current “Face Detection” performance metrics based on today’s logs for ongoing monitoring and optimization.

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Would you like a comprehensive summary of the current Face Detection performance, including accuracy, latency, and error rates, based on today’s operational logs?