docs/BACKEND_MIGRATION_LOG.md

BACKEND_MIGRATION_LOG.md

📌 Overview

This document tracks the migration of the inference logic from a monolithic Streamlit app to a modular, testable FastAPI backend for the Polymer AI Aging Prediction System

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✅ Completed Work

1. Initial Setup

• Installed fastapi, uvicorn, and set up basic FastAPI app in main.py.

2. Modular Inference Utilities

• Moved load_model() and run_inference() into backend/inference_utils.py.
• Separated model configuration for Figure2CNN and ResNet1D.
• Applied proper preprocessing (resampling, normalization) inside run_inference().

3. API Endpoint

• /infer route accepts JSON payloads with model_name and spectrum.
• Returns: full prediction dictionary with class index, logits, and label map.

4. Validation + Testing

• Tested manually in Python REPL.

• Tested via curl:

  curl -X POST  -H "Content-Type: application/json" -d @backend/test_payload.json
  

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🛠 Fixes & Breakpoints Resolved

• ✅ Fixed incorrect model path ("models/" → "outputs/")
• ✅ Corrected unpacking bug in main.py → now returns full result dict
• ✅ Replaced invalid tolist() call on string-typed logits
• ✅ Manually verified output from CLI and curl

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🧪 Next Focus: Robustness Testing

• Invalid model_name handling
• Short/empty spectrum validation
• ResNet model loading test
• JSON schema validation for input
• Unit tests via pytest or integration test runner

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🔄 Future Enhancements

• Modular model registry (for adding more model classes easily)
• Add OpenAPI schema and example payloads for documentation
• Enable batch inference or upload support