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

---

## ✅ 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`:

  ```bash
  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