(refactor): clean up unused inference utilities and FastAPI setup

- Removed obsolete inference utilities and FastAPI setup files that were remnants of the Docker environment dismantling.

backend/.gitignore

@@ -1 +0,0 @@
-__pycache__/

backend/inference_utils.py

@@ -1,79 +0,0 @@
-def load_model(name):
-    return "mock_model"
-
-def run_inference(model, spectrum):
-    return {
-        "prediction": "Stubbed Output",
-        "class_index": 0,
-        "logits": [0.0, 1.0],
-        "class_labels": ["Stub", "Output"]
-    }
-
-
-# ---------- ACTUAL MODEL LOADING/INFERENCE CODE ---------------------|
-# import torch
-# import numpy as np
-# from pathlib import Path 
-# from scripts.preprocess_dataset import resample_spectrum
-# from models.figure2_cnn import Figure2CNN
-# from models.resnet_cnn import ResNet1D
-
-# # -- Label Map --
-# LABELS = ["Stable (Unweathered)", "Weathered (Degraded)"]
-
-# # -- Model Paths --
-# MODEL_CONFIG = {
-#     "figure2": {
-#         "class": Figure2CNN,
-#         "path": "outputs/figure2_model.pth"
-#     },
-#     "resnet": {
-#         "class": ResNet1D,
-#         "path": "outputs/resnet_model.pth"
-#     }
-# }
-
-# def load_model(model_name: str):
-#     if model_name not in MODEL_CONFIG:
-#         raise ValueError(f"Unknown model '{model_name}'. Valid options: {list(MODEL_CONFIG.keys())}")
-
-#     config = MODEL_CONFIG[model_name]
-#     model = config["class"]()
-#     state_dict = torch.load(config["path"], map_location=torch.device("cpu"), weights_only=True)
-#     model.load_state_dict(state_dict)
-#     model.eval()
-#     return model
-
-# def run_inference(model, spectrum: list):
-#     # -- Validate Input --
-#     if not isinstance(spectrum, list) or len(spectrum) < 10:
-#         raise ValueError("Spectrum must be a list of floats with reasonable length")
-
-#     # -- Convert to Numpy --
-#     spectrum = np.array(spectrum, dtype=np.float32)
-
-#     # -- Resample --
-#     x_vals = np.arange(len(spectrum))
-#     spectrum = resample_spectrum(x_vals, spectrum, target_len=500)
-
-#     # -- Normalize --
-#     mean = np.mean(spectrum)
-#     std = np.std(spectrum)
-#     if std == 0:
-#         raise ValueError("Standard deviation of spectrum is zero; normalization will fail.")
-#     spectrum = (spectrum - mean) / std
-
-#     # -- To Tensor --
-#     x = torch.tensor(spectrum, dtype=torch.float32).unsqueeze(0).unsqueeze(0)   # Shape (1, 1, 500)
-
-#     with torch.no_grad():
-#         logits = model(x)
-#         pred_index = torch.argmax(logits, dim=1).item()
-
-#     return {
-#         "prediction": LABELS[pred_index],
-#         "class_index": pred_index,
-#         "logits": logits.squeeze().tolist(),
-#         "class_labels": LABELS
-#     }
-# ---------- ACTUAL MODEL LOADING/INFERENCE CODE ---------------------|

backend/main.py

@@ -1,34 +0,0 @@
-# from fastapi import FastAPI, HTTPException
-from fastapi import FastAPI
-from pydantic import BaseModel
-# import torch
-
-# from backend.inference_utils import load_model, run_inference
-
-# -- FastAPI app --
-app = FastAPI()
-
-# -- Input Schema --
-class InferenceRequest(BaseModel):
-    model_name: str
-    spectrum: list[float]
-
-@app.get("/")
-def root():
-    return {"message": "Polymer Aging Inference API is online"}
-
-@app.post("/infer")
-def infer(request: InferenceRequest):
-    return{
-        "prediction": "Stubbed Output",
-        "class_index": 0,
-        "logits": [0.0, 1.0],
-        "class_labels": ["Stub", "Output"],
-    }
-# def infer(request: InferenceRequest):
-#     try:
-#         model = load_model(request.model_name)
-#         result = run_inference(model, request.spectrum)
-#         return result
-#     except Exception as e:
-#         raise HTTPException(status_code=500, detail=str(e)) from e