app.py

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

# Initialize the Flask application
super_kart_api = Flask("Super Kart Price Predictor")

# Load the trained machine learning model
model_path = "backend_files/super_kart_model_v1_0.joblib"
try:
    model = joblib.load(model_path)
    print(f"Model loaded successfully from {model_path}")
except FileNotFoundError:
    raise FileNotFoundError(f"Model file not found at {model_path}. Ensure it's included in the deployment.")

# Define a route for the home page (GET request)
@super_kart_api.get('/')
def home():
    """
    This function handles GET requests to the root URL ('/') of the API.
    It returns a simple welcome message.
    """
    return "Welcome to the Super Kart Price Prediction API!"

# Define an endpoint for single product sales prediction (POST request)
@super_kart_api.post('/v1/sales')
def predict_sales():
    """
    This function handles POST requests to the '/v1/sales' endpoint.
    It expects a JSON payload containing product and store details and returns
    the predicted sales total as a JSON response.
    """
    # Get the JSON data from the request body
    input_data = request.get_json()
    
    # Extract relevant features from the JSON data
    sample = {
        'Product_Weight': input_data['Product_Weight'],
        'Product_Sugar_Content': input_data['Product_Sugar_Content'],
        'Product_Allocated_Area': input_data['Product_Allocated_Area'],
        'Product_Type': input_data['Product_Type'],
        'Product_MRP': input_data['Product_MRP'],
        'Store_Establishment_Year': input_data['Store_Establishment_Year'],
        'Store_Size': input_data['Store_Size'],
        'Store_Location_City_Type': input_data['Store_Location_City_Type'],
        'Store_Type': input_data['Store_Type']
    }
    
    # Convert to DataFrame
    features_df = pd.DataFrame([sample])
    
    # Apply one-hot encoding
    features_df = pd.get_dummies(features_df, columns=['Product_Type', 'Store_Type'], drop_first=True)
    
    # Apply ordinal encoding
    sugar_mapping = {'No Sugar': 0, 'Low Sugar': 1, 'Regular': 2}
    size_mapping = {'Small': 0, 'Medium': 1, 'High': 2}
    city_mapping = {'Tier 3': 0, 'Tier 2': 1, 'Tier 1': 2}
    
    features_df['Product_Sugar_Content'] = features_df['Product_Sugar_Content'].map(sugar_mapping)
    features_df['Store_Size'] = features_df['Store_Size'].map(size_mapping)
    features_df['Store_Location_City_Type'] = features_df['Store_Location_City_Type'].map(city_mapping)
    
    # Make prediction
    predicted_sales = model.predict(features_df)[0]
    predicted_sales = round(float(predicted_sales), 2)
    
    # Return the predicted sales total
    return jsonify({'Predicted Sales Total (in dollars)': predicted_sales})

# Run the app (for testing locally)
if __name__ == '__main__':
    super_kart_api.run(debug=True)

# %%writefile backend_files/app.py
# # Import necessary libraries
# import numpy as np
# import joblib  # For loading the serialized model
# import pandas as pd  # For data manipulation
# from flask import Flask, request, jsonify  # For creating the Flask API

# # Initialize the Flask application
# super_kart_api = Flask("Super Kart Price Predictor")

# # Load the trained machine learning model (updated path to match deployment structure)
# model_path = "super_kart_model_v1_0.joblib"
# try:
#     model = joblib.load(model_path)
#     print(f"Model loaded successfully from {model_path}")
# except FileNotFoundError:
#     raise FileNotFoundError(f"Model file not found at {model_path}. Ensure it's included in the deployment.")

# # Define a route for the home page (GET request)
# @super_kart_api.get('/')
# def home():
#     """
#     This function handles GET requests to the root URL ('/') of the API.
#     It returns a simple welcome message.
#     """
#     return "Welcome to the Super Kart Price Prediction API!"

# # Define an endpoint for single product sales prediction (POST request)
# @super_kart_api.post('/v1/sales')
# def predict_sales():
#     """
#     This function handles POST requests to the '/v1/sales' endpoint.
#     It expects a JSON payload containing product and store details and returns
#     the predicted sales total as a JSON response.
#     """
#     # Get the JSON data from the request body
#     input_data = request.get_json()
    
#     # Extract relevant features from the JSON data
#     # Note: Exclude Product_Id and Store_Id if they are not used in prediction
#     sample = {
#         'Product_Weight': input_data['Product_Weight'],
#         'Product_Sugar_Content': input_data['Product_Sugar_Content'],
#         'Product_Allocated_Area': input_data['Product_Allocated_Area'],
#         'Product_Type': input_data['Product_Type'],
#         'Product_MRP': input_data['Product_MRP'],
#         'Store_Establishment_Year': input_data['Store_Establishment_Year'],
#         'Store_Size': input_data['Store_Size'],
#         'Store_Location_City_Type': input_data['Store_Location_City_Type'],
#         'Store_Type': input_data['Store_Type']
#     }
#     # Convert the extracted data into a Pandas DataFrame
#     features_df = pd.DataFrame([sample])
    
#     # Apply one-hot encoding for nominal columns (matching training)
#     features_df = pd.get_dummies(features_df, columns=['Product_Type', 'Store_Type'], drop_first=True)
    
#     # Apply ordinal encoding (based on provided orders)
#     sugar_mapping = {'No Sugar': 0, 'Low Sugar': 1, 'Regular': 2}
#     size_mapping = {'Small': 0, 'Medium': 1, 'High': 2}
#     city_mapping = {'Tier 3': 0, 'Tier 2': 1, 'Tier 1': 2}
    
#     features_df['Product_Sugar_Content'] = features_df['Product_Sugar_Content'].map(sugar_mapping)
#     features_df['Store_Size'] = features_df['Store_Size'].map(size_mapping)
#     features_df['Store_Location_City_Type'] = features_df['Store_Location_City_Type'].map(city_mapping)
    
#     # Make prediction (assuming direct sales prediction; adjust if log-transformed)
#     predicted_sales = model.predict(features_df)[0]
    
#     # If your model predicts log(sales), uncomment and use this instead:
#     # predicted_log_sales = model.predict(features_df)[0]
#     # predicted_sales = np.exp(predicted_log_sales)
    
#     # Convert to Python float and round to 2 decimals
#     predicted_sales = round(float(predicted_sales), 2)
    
#     # Return the predicted sales total
#     return jsonify({'Predicted Sales Total (in dollars)': predicted_sales})

# # Run the app (for testing locally; remove or adjust for production)
# if __name__ == '__main__':
#     super_kart_api.run(debug=True)