app.py

from flask import Flask, request, jsonify
import joblib
import pandas as pd

# Initialize Flask app
app = Flask(__name__)

# Load the trained final model
try:
    model = joblib.load("extraa_learn_project.pkl")
    print("Model loaded successfully.")
except Exception as e:
    print(f"Error loading model: {e}")
    model = None

# Define expected features
FEATURES = [
    "time_spent_on_website",
    "first_interaction",
    "profile_completed",
    "age",
    "page_views_per_visit",
    "last_activity"
]

@app.route("/", methods=["GET"])
def home():
    return jsonify({
        "message": "Backend is running!",
        "status": "ok",
        "required_features": FEATURES
    })

@app.route("/predict", methods=["POST"])
def predict():
    try:
        data = request.get_json()

        # Ensure all required features are present
        missing = [f for f in FEATURES if f not in data]
        if missing:
            return jsonify({"error": f"Missing features: {missing}"}), 400

        # Convert input into DataFrame
        df = pd.DataFrame([data], columns=FEATURES)

        # Make prediction
        prediction = model.predict(df)[0]
        proba = model.predict_proba(df).max()

        return jsonify({
            "prediction": int(prediction),
            "confidence": float(proba)
        })

    except Exception as e:
        return jsonify({"error": str(e)}), 500

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=8501)