app.py

from fastapi import FastAPI, Request, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from tensorflow.keras.models import load_model
import numpy as np

# ---------- Load the Trained Model ----------
MODEL_PATH = "cancer_classifier.h5"
model = load_model(MODEL_PATH)

# ---------- Initialize FastAPI ----------
app = FastAPI(title="GenAI Health Insight API", version="2.0")

# ---------- Enable CORS (so your friend’s frontend can call it) ----------
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Or set ["https://yourfrontend.vercel.app"] for extra safety
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# ---------- Root Endpoint ----------
@app.get("/")
def home():
    return {"message": "Welcome to GenAI Health Insight API 🚀"}

# ---------- Prediction Endpoint ----------
@app.post("/predict")
async def predict(request: Request):
    """
    Input example (JSON):
    {
        "features": [value1, value2, ..., valueN]
    }
    """
    data = await request.json()
    input_data = data.get("features")

    if input_data is None:
        raise HTTPException(status_code=400, detail="Missing 'features' in request body")
    
    # Prepare and predict
    input_array = np.array(input_data).reshape(1, -1)
    prediction = model.predict(input_array)
    result = float(prediction[0][0])

    return {"prediction": result, "status": "success"}

# ---------- Health Check Endpoint ----------
@app.get("/health")
def health():
    return {"status": "healthy"}