import mlflow
import joblib
import pandas as pd
from fastapi import FastAPI

MLFLOW_URI = "https://remdev-ai-projet-06-mlops-mlflow.hf.space"
RUN_ID = "f669674a10e84d1199134ab88d64be9b"

mlflow.set_tracking_uri(MLFLOW_URI)

# Télécharger les fichiers
model_path = mlflow.artifacts.download_artifacts(
    run_id=RUN_ID,
    artifact_path="credit_model.joblib"
)
threshold_path = mlflow.artifacts.download_artifacts(
    run_id=RUN_ID,
    artifact_path="optimal_threshold.txt"
)

# Charger
model = joblib.load(model_path)
with open(threshold_path) as f:
    threshold = float(f.read())

app = FastAPI()

@app.post("/predict")
def predict(payload: dict):
    df = pd.DataFrame([payload])
    proba = model.predict_proba(df)[0,1]
    decision = int(proba >= threshold)

    return {
        "probability_default": float(proba),
        "threshold": threshold,
        "decision": decision
    }