app.py

import joblib
import pandas as pd
from fastapi import FastAPI
from pydantic import BaseModel

pipe = joblib.load("diabetes_model.pkl")

# Columns from Nb3 (Pima Indians Diabetes)
FEATURE_COLUMNS = [
    "pregnancies","glucose","blood_pressure","skin_thickness",
    "insulin","bmi","diabetes_pedigree","age"
]

class Patient(BaseModel):
    pregnancies: float
    glucose: float
    blood_pressure: float
    skin_thickness: float
    insulin: float
    bmi: float
    diabetes_pedigree: float
    age: float

app = FastAPI(title="Diabetes Risk API", version="1.0.0")

@app.post("/predict")
def predict(p: Patient):
    # Build a 1-row DataFrame with correct column names
    row = pd.DataFrame([p.model_dump()])[FEATURE_COLUMNS]
    yhat = int(pipe.predict(row)[0])

    proba = None
    try:
        proba = float(pipe.predict_proba(row)[0][1])
    except Exception:
        pass

    return {"prediction": yhat, "positive_probability": proba}