from fastapi import FastAPI
from pydantic import BaseModel
import pickle
import numpy as np

app = FastAPI()

# Load model at startup
with open("app/model.pkl", "rb") as f:
    model = pickle.load(f)

# Input schema
class InputData(BaseModel):
    features: list[float]

@app.get("/")
def home():
    return {"message": "Logistic Regression API is up!"}

@app.post("/predict")
def predict(data: InputData):
    prediction = model.predict([np.array(data.features)])
    return {"prediction": int(prediction[0])}