app.py

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

app = Flask(__name__)

# Load the serialized model
model = joblib.load('best_sales_forecasting_model.pkl')

@app.route('/')
def home():
    return "Sales Forecasting Backend is running!"

@app.route('/predict', methods=['POST'])
def predict():
    try:
        data = request.get_json(force=True)
        # Convert the incoming data to a pandas DataFrame
        # Assuming the incoming data is a list of dictionaries, where each dictionary is a data point
        input_data = pd.DataFrame(data)

        # Ensure the columns are in the same order as the training data
        # This assumes you have access to the columns from your training data (X_train)
        # You might need to adjust this part based on how you handle feature ordering
        # For demonstration, let's assume the input data has the same columns in the same order
        # In a real application, you might need to reorder columns or handle missing ones

        # Make predictions
        predictions = model.predict(input_data)

        # Return predictions as a JSON response
        return jsonify(predictions.tolist())
    except Exception as e:
        return jsonify({'error': str(e)})

if __name__ == '__main__':
    # Running on 0.0.0.0 makes it accessible externally, useful for deployment
    app.run(host='0.0.0.0', port=5000)