app.py

import os
from fastapi import FastAPI
from pydantic import BaseModel
import joblib
import numpy as np

app = FastAPI()

# Load the model
# We use os.path to make sure it finds the file in the container
model_path = os.path.join(os.path.dirname(__file__), "diabetes_model_professional.pkl")
model = joblib.load(model_path)

class PatientData(BaseModel):
    pregnancies: int
    glucose: float
    blood_pressure: float
    skin_thickness: float
    insulin: float
    bmi: float
    pedigree: float
    age: int

@app.get("/")
def home():
    return {"status": "online", "message": "Diabetes Prediction API is running on Hugging Face!"}

@app.post("/predict")
def predict_diabetes(data: PatientData):
    features = [[
        data.pregnancies, 
        data.glucose, 
        data.blood_pressure, 
        data.skin_thickness, 
        data.insulin, 
        data.bmi, 
        data.pedigree, 
        data.age
    ]]
    
    probability = model.predict_proba(features)[0][1]
    
    # Your Professional Threshold
    threshold = 0.35
    is_diabetic = probability >= threshold
    
    return {
        "prediction": "Diabetic" if is_diabetic else "Healthy",
        "risk_score": round(float(probability) * 100, 2),
        "alert": "High Risk - Consult Doctor" if is_diabetic else "Low Risk"
    }