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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
+# - `-w 4`: Uses 4 worker processes for handling requests
+# - `-b 0.0.0.0:7860`: Binds the server to port 7860 on all network interfaces
+# - `app:app`: Runs the Flask application named 'sales_prediction_api' from the 'app.py' file
+
+ENTRYPOINT ["gunicorn", "-w", "4", "--bind", "0.0.0.0:7860", "app:sales_prediction_api"]

app.py

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+
+# 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 flask app with a name
+sales_prediction_api = Flask("Superkart Sales Predictor")
+
+# Load the trained machine learning model
+model = joblib.load("superkart_sales_prediction_model.joblib")
+
+# Define a route for the home page
+@sales_prediction_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 Superkart Sales Prediction API!"
+
+# Define an endpoint for single property prediction (POST request)
+@sales_prediction_api.post("/v1/predict")
+def predict_sales():
+    """
+    This function handles POST requests to the '/v1/predi' endpoint.
+    It expects a JSON payload containing property details and returns
+    the predicted rental price as a JSON response.
+    """
+# Get the JSON data from the request body
+business_data = request.get_json()
+
+# Extract relevant features from the JSON data
+sample = {
+      'Product_Weight': business_data['Product_Weight'] ,
+      'Product_Sugar_Content': business_data['Product_Sugar_Content'],
+      'Product_Allocated_Area': business_data['Product_Allocated_Area'],
+      'Product_Type': business_data['Product_Type'],
+      'Product_MRP': business_data['Product_MRP'],
+      'Store_Establishment_Year': business_data['Store_Establishment_Year'],
+      'Store_Size': business_data['Store_Size'],
+      'Store_Location_City_Type': business_data['Store_Location_City_Type'],
+      'Store_Type': business_data['Store_Type']
+  }
+  
+# Convert the extracted data into a Pandas DataFrame
+input_data = pd.DataFrame([business_data])
+
+# Make a sales prediction using the sales model
+predicted_sales = model.predict(input_data)[0]
+
+# Convert predicted_price to Python float
+predicted_sales = float(predicted_sales)
+
+# Return the actual sales
+return jsonify({'Predicted sales': predicted_sales})
+
+if __name__ == "__main__":
+    sales_prediction_api.run(debug=True)
+
+# Define an endpoint for batch prediction (POST request)
+@sales_prediction_api.post("/v1/predict/batch")
+def predict_sales_batch():
+    """
+    This function handles POST requests to the '/v1/predict/batch' 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 request
+    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()
+
+    # Create a dictionary of predictions with property IDs as keys
+    Store_Type = input_data['Store_Type'].tolist() 
+    output_dict = dict(zip(Store_Type, predicted_sales))  # Use actual prices
+
+    # Return the predictions dictionary as a JSON response
+    return output_dict
+
+    # Run the Flask application in debug mode if this script is executed directly
+if __name__ == '__main__':
+    sales_prediction_api.run(debug=True)

requirements.txt

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+Flask==3.1.0
+joblib==1.4.2
+pandas==2.2.2
+scikit-learn==1.6.1
+gunicorn==23.0.0
+numpy==2.0.2
+seaborn==0.13.2
+xgboost==2.1.4
+requests==2.32.3
+streamlit==1.45.1

superkart_sales_prediction_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e9b8219f2405b2d0b7d27792cf7cb26b54083c77f9998caf16386f41cc56a7c8
+size 63813587