Update main.py

main.py

@@ -1,25 +1,8 @@
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-from typing import List
-import numpy as np
-import joblib
+from fastapi import FastAPI, HTTPException, Request
 import os
+from rf_model import score
 
-app = FastAPI(title="Stroke Risk Prediction")
-
-MODEL_PATH = "best_random_forest.pkl"
-_model = None
-
-def get_model():
-    global _model
-    if _model is None:
-        if not os.path.exists(MODEL_PATH):
-            raise FileNotFoundError(f"Missing model file: {MODEL_PATH}")
-        _model = joblib.load(MODEL_PATH)
-    return _model
-
-class PredictRequest(BaseModel):
-    input_data: List[List[float]]
+app = FastAPI(title="Stroke Risk (Pure‑Python RF)")
 
 @app.get("/")
 def root():
@@ -30,14 +13,17 @@ def health():
     return {"status": "healthy"}
 
 @app.post("/predict")
-def predict(req: PredictRequest):
+async def predict(request: Request):
     try:
-        clf = get_model()
-        arr = np.array(req.input_data, dtype=float)
-        preds = clf.predict(arr)
-        return {"predictions": preds.tolist()}
+        body = await request.json()
+        data = body.get("input_data")
+        if not isinstance(data, list):
+            raise ValueError("`input_data` must be a list of rows")
+        # each row is a list of floats
+        preds = [score(row) for row in data]
+        return {"predictions": preds}
     except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+        raise HTTPException(status_code=400, detail=str(e))