main.py

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

# Initialize Flask app with a name
app = Flask("SuperKart Sales Predictor")

# Load the trained sales prediction model
model = joblib.load("best_sales_forecasting_model.joblib")


# Define a route for the home page
@app.get('/')
def home():
    return "Welcome to the SuperKart Sales Prediction API"

# Define an endpoint to predict sales
@app.post('/v1/totalsales')
def predict_price():
    # Get JSON data from the request
    sales_data = request.get_json()
    return model

    # Extract relevant sales features from the input data
    sample = {
            'Product_Weight': sales_data['Product_Weight'],
            'Product_Sugar_Content': sales_data['Product_Sugar_Content'],
            'Product_Allocated_Area': sales_data['Product_Allocated_Area'],
            'Product_Type': sales_data['Product_Type'],
            'Product_MRP': sales_data['Product_MRP'],
            'Store_Id': sales_data['Store_Id'],
            'Store_Size': sales_data['Store_Size'],
            'Store_Location_City_Type': sales_data['Store_Location_City_Type'],
            'Store_Type': sales_data['Store_Type'],
            'Store_Current_Age': sales_data['Store_Current_Age']
        }


    # Convert the extracted data into a DataFrame
    input_data = pd.DataFrame([sample])

    # Make a sales prediction using the trained model
    prediction = model.predict(input_data)[0]

    # Return the prediction as a JSON response
    return jsonify({'predicted_sales': float(round(prediction, 2))})

# @app.route('/predict_batch', methods=['POST'])
# def predict_batch():
#     """
#     API endpoint for batch predictions.
#     Expects a JSON array of JSON objects, where each object is a product-store combination.
#     """
#     try:
#         data = request.get_json(force=True)
#         # Convert the list of dictionaries to a pandas DataFrame
#         df = pd.DataFrame(data)
#         # Ensure columns are in the same order as during training
#         df = df[model.feature_names_in_]
#         predictions = model.predict(df)
#         # Return the predictions as a JSON response
#         return jsonify({'Predictions': predictions.tolist()})
#     except Exception as e:
#         return jsonify({'error': str(e)})

# if __name__ == '__main__':
#     # Run the Flask app
#     # Setting debug=True allows for automatic reloading and provides a debugger
#     app.run(debug=True, host='0.0.0.0', port=7860)