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SuperKartSalesPrectionBackend

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

# Initialize Flask app with a name
superkart_product_sales_prediction_api = Flask("SuperKart Sales Forecast API")

# Load the trained churn prediction model
model = joblib.load("superkart_product_sales_prediction_model_v1_0.joblib")

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

# Define an endpoint to predict churn for a single customer
@superkart_product_sales_prediction_api.post('/v1/forecast')
def predict_sales_forecast():

  """
    This function handles POST requests to the '/v1/forecast' endpoint.
    It expects a JSON payload containing property details and returns
    the predicted rental price as a JSON response.
  """

    # Get JSON data from the request
  product_data = request.get_json()

  # Extract relevant customer features from the input data
  sample = {
        'Product_Weight': product_data.get('Product_Weight'),
        'Product_Sugar_Content': product_data.get('Product_Sugar_Content'),
        'Product_Allocated_Area': product_data.get('Product_Allocated_Area'),
        'Product_Type': product_data.get('Product_Type'),
        'Product_MRP': product_data.get('Product_MRP'),
        'Store_Id': product_data.get('Store_Id'),
        'Store_Establishment_Year': product_data.get('Store_Establishment_Year'),
        'Store_Size': product_data.get('Store_Size'),
        'Store_Location_City_Type': product_data.get('Store_Location_City_Type'),
        'Store_Type': product_data.get('Store_Type')
    }

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

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

  # Convert predicted_price to Python float
  predicted_sales = round(float(predicted_sales), 2)


  # Return the prediction as a JSON response
  return jsonify({'Prediction': predicted_sales})

@superkart_product_sales_prediction_api.post('/v1/forecastbatch')
def predict_sales_forecast_batch():
    """
    This function handles POST requests to the '/v1/forecastbatch' endpoint.
    It expects a CSV file containing product and store details for multiple products
    and returns the predicted sales forecast prices as a dictionary in the JSON response.
    """
    # Get the uploaded CSV file from the request
    file = request.files['file']

    # Read the CSV file into a Pandas DataFrame
    input_data = pd.read_csv(file)
    #print(input_data.loc[0])

    # Make predictions for all products in the DataFrame (get sales prices)
    predicted_sales = model.predict(input_data).tolist()

    # Convert predicted_price to Python float
    predicted_sales = [round(float(price), 2) for price in predicted_sales]

    #print(predicted_sales)
    # Create a dictionary of predictions with property IDs as keys
    product_ids = input_data['Product_Id'].tolist()  # Assuming 'Product_Id' is the product_id ID column
    output_dict = dict(zip(product_ids, predicted_sales))  # Use actual prices

    # Return the predictions dictionary as a JSON response
    return output_dict

# Run the Flask app in debug mode
if __name__ == '__main__':
    superkart_product_sales_prediction_api.run(debug=True)

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

	# Initialize Flask app with a name
	superkart_product_sales_prediction_api = Flask("SuperKart Sales Forecast API")

	# Load the trained churn prediction model
	model = joblib.load("superkart_product_sales_prediction_model_v1_0.joblib")

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

	# Define an endpoint to predict churn for a single customer
	@superkart_product_sales_prediction_api.post('/v1/forecast')
	def predict_sales_forecast():

	"""
	This function handles POST requests to the '/v1/forecast' endpoint.
	It expects a JSON payload containing property details and returns
	the predicted rental price as a JSON response.
	"""

	# Get JSON data from the request
	product_data = request.get_json()

	# Extract relevant customer features from the input data
	sample = {
	'Product_Weight': product_data.get('Product_Weight'),
	'Product_Sugar_Content': product_data.get('Product_Sugar_Content'),
	'Product_Allocated_Area': product_data.get('Product_Allocated_Area'),
	'Product_Type': product_data.get('Product_Type'),
	'Product_MRP': product_data.get('Product_MRP'),
	'Store_Id': product_data.get('Store_Id'),
	'Store_Establishment_Year': product_data.get('Store_Establishment_Year'),
	'Store_Size': product_data.get('Store_Size'),
	'Store_Location_City_Type': product_data.get('Store_Location_City_Type'),
	'Store_Type': product_data.get('Store_Type')
	}

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

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

	# Convert predicted_price to Python float
	predicted_sales = round(float(predicted_sales), 2)


	# Return the prediction as a JSON response
	return jsonify({'Prediction': predicted_sales})

	@superkart_product_sales_prediction_api.post('/v1/forecastbatch')
	def predict_sales_forecast_batch():
	"""
	This function handles POST requests to the '/v1/forecastbatch' endpoint.
	It expects a CSV file containing product and store details for multiple products
	and returns the predicted sales forecast prices as a dictionary in the JSON response.
	"""
	# Get the uploaded CSV file from the request
	file = request.files['file']

	# Read the CSV file into a Pandas DataFrame
	input_data = pd.read_csv(file)
	#print(input_data.loc[0])

	# Make predictions for all products in the DataFrame (get sales prices)
	predicted_sales = model.predict(input_data).tolist()

	# Convert predicted_price to Python float
	predicted_sales = [round(float(price), 2) for price in predicted_sales]

	#print(predicted_sales)
	# Create a dictionary of predictions with property IDs as keys
	product_ids = input_data['Product_Id'].tolist() # Assuming 'Product_Id' is the product_id ID column
	output_dict = dict(zip(product_ids, predicted_sales)) # Use actual prices

	# Return the predictions dictionary as a JSON response
	return output_dict

	# Run the Flask app in debug mode
	if __name__ == '__main__':
	superkart_product_sales_prediction_api.run(debug=True)