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Dockerfile

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+FROM python:3.9-slim
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy all files from the current directory to the container's working directory
+COPY . .
+
+# Install dependencies from the requirements file without using cache to reduce image size
+RUN pip install -- no-cache-dir -- upgrade -r requirements.txt
+
+# Define the command to start the application using Gunicorn with 4 worker processes
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app: sales_predictor_api"]

app.py

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+
+import numpy as np
+import pandas as pd
+import joblib
+from flask import Flask, request, jsonify
+
+#initate flask application
+sales_price_prediction_api = Flask("SuperKart Sales Price Prediction API")
+
+#Load the trained model
+model = joblib.load("superkart_prediction.joblib")
+
+
+# Define a route for the home page (GET request)
+@SalesPredictionBackend_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 SuperKart Sales Prediction API!"
+
+# Define an endpoint for single property prediction (POST request)
+@SalesPredictionBackend_api.post('/v1/sales')
+def predict_rental_prife():
+  """
+  This function handles POST requests to the '/v1/sales' endpoint.
+  It expects a JSON payload containing producr details and returns
+  the predicted sales amount as a JSON response.
+  """
+  # Get the JSON data from the request body
+  sales_data = request.get_json()
+  
+  # Extract relevant features from the JSON data
+  sample = {
+      'Product_Id': sales_data['Product_Id'],
+      'Product_Weight': sales_data['Product_Weight'],
+      'Product_Sugar_Content': sales_data['Product_Sugar_Content'],
+      'Product_Allocated_Area': sales_data['Product_Allocated_Area'],
+      '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_Establishment_Year': sales_data['Store_Establishment_Year'],
+      'Store_Size': sales_data['Store_Size'],
+      'Store_Location_City_Type': sales_data['Store_Location_City_Type'],
+      'Store_Type': sales_data['Store_Type']
+      }
+  #Convert the extracted sata into a Panda Dataframe
+  input_data = pd.DataFrame([sample])
+
+  #Make prediction (get sales_prediction)
+  prediction_log_sale = model.predict(input_data)
+  
+  #calculated_sale
+  predicted_sale = np.exp(prediction_log_sale[0])
+  
+  #convert predicted price to Pyton float
+  predicted_sale = round(float(predicted_sale), 2)
+
+  #return the sales
+  return jsonify({'predicted Sales (indollars)': predicted_sale})
+
+#define an endpoint for batch predictions (POST request)
+@SalesPredictionBackend_api.post('/v1/sales/batch')
+def predict_sales_batch():
+  """
+  This function handles POST requests to the '/v1/rentalbatch' endpoint.
+  It expects a CSV file containing property details for multiple properties
+  and returns the predicted rental prices as a dictionary in the JSON response.
+  """
+
+  # Get the uploaded CSV file from the requqst
+  file = request. files['file']
+  
+  # Read the CSV file into a Pandas DataFrame
+  input_data = pd.read_csv(file)
+  
+  # Make predictions for all properties in the DataFrame (get log_prices)
+  predicted_log_sales = model.predict(input_data).tolist()
+
+  #calculate actual prices
+  predicted_prices = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_prices]
+  
+  # Create a dictionary of predictions with property IDs as keys
+  property_ids = input_data['id'].tolist() # Assuming 'id' is the property ID column
+  output_dict = dict(zip(property_ids, predicted_prices) ) # Use actual prices
+  # Return the predictions dictionary as
+  return output_dict
+
+# Run the Flask application in debug mode if this script is executed directly
+if __name__ == '_main_':
+  sales_price_prediction_api.run(debug=True)
+  

requirements.txt

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+pandas == 2.2.2
+numpy == 2.0.2
+scikit-learn == 1.6.1
+xgboost == 2.1.4
+joblib == 1.4.2
+Werkzeug == 2.2.2
+flask == 2.2.2
+gunicorn == 20.1.0
+requests == 2.28.1
+uvicorn[standard]
+streamlit == 1.43.2

superkart_prediction.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2a1c875817ca94e6944732721953770e476548d571e8e99af8e6cee658bbe433
+size 1015101