app.py

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

# Create a Flask application instance
app = Flask(__name__)

# Define model path
MODEL_DIR = "model_artifacts"
MODEL_FILENAME = "best_sales_forecast_model.joblib"
MODEL_PATH = os.path.join(MODEL_DIR, MODEL_FILENAME)

# Load model at startup
try:
    model = joblib.load(MODEL_PATH)
    print(" Model loaded successfully!")
except Exception as e:
    print(f" Error loading model: {e}")
    model = None

# Health check route
@app.route("/", methods=["GET"])
def index():
    return jsonify({"status": "Backend is running!"})

# Prediction route
@app.route("/predict", methods=["POST"])
def predict():
    if model is None:
        return jsonify({"error": "Model not loaded"}), 500

    try:
        # Get request JSON
        data = request.get_json(force=True)
        if not data:
            return jsonify({"error": "No input data provided"}), 400

        # Convert to DataFrame
        df = pd.DataFrame(data)

        # Drop ID column if present, as it's not used in prediction
        if "Product_Id" in df.columns:
            df = df.drop("Product_Id", axis=1)

        # Predict
        predictions = model.predict(df)

        return jsonify({"predictions": predictions.tolist()})

    except Exception as e:
        return jsonify({"error": str(e)}), 400

# Entry point
if __name__ == "__main__":
    app.run(host="0.0.0.0", port=5000)