import os
import pickle
import numpy as np
from flask import Flask, request, jsonify

app = Flask(__name__)

# Load model
MODEL_PATH = "predictive_model_smote.pkl"

with open(MODEL_PATH, "rb") as f:
    model = pickle.load(f)

@app.route("/")
def home():
    return {"message": "Predictive Maintenance API is running"}

@app.route("/predict", methods=["POST"])
def predict():
    try:
        data = request.json
        
        # Expected input features in correct order
        features = [
            data["Air temperature [K]"],
            data["Process temperature [K]"],
            data["Rotational speed [rpm]"],
            data["Torque [Nm]"],
            data["Tool wear [min]"]
        ]

        features_array = np.array([features])
        prediction = model.predict(features_array)[0]

        return jsonify({
            "prediction": int(prediction)
        })

    except Exception as e:
        return jsonify({"error": str(e)})

if __name__ == "__main__":
    port = int(os.environ.get("PORT", 7860))
    app.run(host="0.0.0.0", port=port)