app.py

from flask import Flask, request, jsonify
import pickle
import pandas as pd
import traceback

app = Flask(__name__)

# Load pipeline (preprocessor + model)
with open("best_model.pkl", "rb") as f:
    model = pickle.load(f)


@app.route("/", methods=["GET"])
def health():
    return jsonify({"status": "ExtraaLearn API running"}), 200


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

        if "inputs" not in data:
            return jsonify({"error": "Expected key 'inputs'"}), 400

        df = pd.DataFrame(data["inputs"])

        pred = int(model.predict(df)[0])
        prob = float(model.predict_proba(df)[0][1])

        if prob >= 0.75:
            category = "High Conversion Potential"
        elif prob >= 0.40:
            category = "Medium Conversion Potential"
        else:
            category = "Low Conversion Potential"

        return jsonify({
            "prediction": pred,
            "conversion_probability": round(prob, 2),
            "lead_category": category
        }), 200

    except Exception as e:
        return jsonify({
            "error": "Prediction failed",
            "details": str(e),
            "trace": traceback.format_exc()
        }), 500