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be_app.py

@@ -1,66 +1,4 @@
-# import numpy as np
-# import joblib
-# import pandas as pd
-# from flask import Flask, request, jsonify
 
-# # Initialize the Flask app with a custom name
-# super_kart_predictor_api = Flask("Super Kart Sales Predictor")
-
-# # Load the trained model from the specified path
-# # Make sure model_path variable is defined or replace with the actual path string
-# model = joblib.load(model_path)
-
-# # Define a route for the home page (GET request)
-# @super_kart_predictor_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 Sales Predictor API!"
-
-# # Define a route for predictions (POST request)
-# @super_kart_predictor_api.post("/v1/sales")
-# def predict_sales():
-#   """
-#   This function handles POST requests to the /v1/sales endpoint.
-#   It expects a JSON payload containing commodity sales details and returns
-#   the predicted sales as a JSON response
-#   """
-#   # Get JSON data from the POST request
-#   superkart_data = request.get_json
-#   print(superkart_data)
-
-#   # Extract relevant features from the JSON payload into a dictionary
-#   sample = {
-#       'Product_Weight': superkart_data['Product_Weight'],
-#       'Product_Allocated_Area': superkart_data['Product_Allocated_Area'],
-#       'Product_MRP': superkart_data['Product_MRP'],
-#       'Store_Tenure': superkart_data['Store_Tenure'],
-#       'Product_Category': superkart_data['Product_Category'],
-#       'Product_Sugar_Content': superkart_data['Product_Sugar_Content'],
-#       'Product_Type': superkart_data['Product_Type'],
-#       'Store_Id': superkart_data['Store_Id'],
-#       'Store_Size': superkart_data['Store_Size'],
-#       'Store_Location_City_Type': superkart_data['Store_Location_City_Type'],
-#       'Store_Type': superkart_data['Store_Type'],
-#       'Perishability': superkart_data['Perishability']
-#   }
-
-#   # Create a DataFrame from the input dictionary for model compatibility
-#   input_data = pd.DataFrame([sample])
-
-#   # Predict sales price using the loaded model
-#   predicted_sales_price = model.predict(input_data)[0]
-
-#   # Convert predicted sales price back from log scale using exponential
-#   predicted_sales = np.exp(predicted_sales_price)
-
-#   # Convert the prediction to a float type with rounding to 2 decimal places
-#   predicted_sales = float(predicted_sales, 2)
-
-#   # Return the prediction as a JSON response
-#   return jsonify({"predicted_sales_price": predicted_sales})
 import numpy as np
 import joblib
 import pandas as pd