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backend_files/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 app (assuming `app.py` contains the Flask instance named `app`)
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:rental_price_predictor_api"]

backend_files/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 the Flask application
+superkart_sales_api = Flask("SuperKart Sales Predictor")
+
+# Load the trained machine learning model
+model = joblib.load("superkart_sales_prediction_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+@superkart_sales_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 sales prediction (POST request)
+@superkart_sales_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 as a JSON response.
+    """
+    # Get the JSON data from the request body
+    product_data = request.get_json()
+
+    # Extract relevant features from the JSON data
+    sample = {
+        'Product_Weight': product_data['Product_Weight'],
+        'Product_Allocated_Area': product_data['Product_Allocated_Area'],
+        'Product_MRP': product_data['Product_MRP'],
+        'Store_Establishment_Year': product_data['Store_Establishment_Year'],
+        'Product_Sugar_Content': product_data['Product_Sugar_Content'],
+        'Product_Type': product_data['Product_Type'],
+        'Store_Size': product_data['Store_Size'],
+        'Store_Location_City_Type': product_data['Store_Location_City_Type'],
+        'Store_Type': product_data['Store_Type']
+    }
+
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction
+    predicted_sales = model.predict(input_data)[0]
+
+    # Convert predicted_sales to Python float
+    predicted_sales = round(float(predicted_sales), 2)
+
+    # Return the prediction as JSON
+    return jsonify({'Predicted Sales (units)': predicted_sales})
+
+
+# Define an endpoint for batch prediction (POST request)
+@superkart_sales_api.post('/v1/salesbatch')
+def predict_sales_batch():
+    """
+    This function handles POST requests to the '/v1/salesbatch' endpoint.
+    It expects a CSV file containing product details for multiple products
+    and returns the predicted sales 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 rows in the DataFrame
+    predicted_sales = model.predict(input_data).tolist()
+
+    # Round and convert to float
+    predicted_sales = [round(float(sale), 2) for sale in predicted_sales]
+
+    # If the CSV has an 'id' column, use it as keys; else just index
+    if 'id' in input_data.columns:
+        ids = input_data['id'].tolist()
+    else:
+        ids = list(range(1, len(predicted_sales) + 1))
+
+    output_dict = dict(zip(ids, predicted_sales))
+
+    # Return the predictions dictionary as a JSON response
+    return jsonify(output_dict)
+
+
+# Run the Flask application in debug mode if this script is executed directly
+if __name__ == '__main__':
+    superkart_sales_api.run(debug=True)

backend_files/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

frontend_files/Dockerfile

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+# Use a minimal base image with Python 3.9 installed
+FROM python:3.9-slim
+
+# Set the working directory inside the container to /app
+WORKDIR /app
+
+# Copy all files from the current directory on the host to the container's /app directory
+COPY . .
+
+# Install Python dependencies listed in requirements.txt
+RUN pip3 install -r requirements.txt
+
+# Define the command to run the Streamlit app on port 8501 and make it accessible externally
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]
+
+# NOTE: Disable XSRF protection for easier external access in order to make batch predictions

frontend_files/app.py

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+
+import streamlit as st
+import pandas as pd
+import requests
+
+# -------------------------------
+# Streamlit Frontend for Superkart Sales Prediction
+# -------------------------------
+
+# Set the title of the Streamlit app
+st.title("Superkart Sales Prediction App")
+
+# Section for online prediction
+st.subheader("Online Prediction")
+
+# Collect user input for product & store features
+product_weight = st.number_input("Product Weight (grams)", min_value=0.0, value=500.0, step=50.0)
+product_visibility = st.number_input("Product Visibility", min_value=0.0, max_value=1.0, step=0.01, value=0.05)
+product_mrp = st.number_input("Product MRP (₹)", min_value=0.0, value=120.0, step=1.0)
+
+outlet_establishment_year = st.number_input("Outlet Establishment Year", min_value=1950, max_value=2030, value=2000, step=1)
+outlet_size = st.selectbox("Outlet Size", ["Small", "Medium", "High"])
+outlet_location_type = st.selectbox("Outlet Location Type", ["Tier 1", "Tier 2", "Tier 3"])
+outlet_type = st.selectbox("Outlet Type", ["Grocery Store", "Supermarket Type1", "Supermarket Type2", "Supermarket Type3"])
+
+# Convert user input into a DataFrame
+input_data = pd.DataFrame([{
+    
+    'Product_Weight': product_weight,
+    'Product_Visibility': product_visibility,
+    'Product_MRP': product_mrp,
+    'Outlet_Establishment_Year': outlet_establishment_year,
+    'Outlet_Size': outlet_size,
+    'Outlet_Location_Type': outlet_location_type,
+    'Outlet_Type': outlet_type
+}])
+
+# Make prediction when the "Predict" button is clicked
+if st.button("Predict Sales"):
+    response = requests.post(
+        "https://<username>-Superkart_Docker_space.hf.space/v1/sales",
+        json=input_data.to_dict(orient='records')[0]
+    )  # Send data to backend API
+    
+    if response.status_code == 200:
+        prediction = response.json()['Predicted Sales']
+        st.success(f"Predicted Sales: {prediction}")
+    else:
+        st.error("Error making prediction. Please check the backend logs.")
+
+# Section for batch prediction
+st.subheader("Batch Prediction")
+
+# Allow users to upload a CSV file for batch prediction
+uploaded_file = st.file_uploader("Upload CSV file for batch prediction", type=["csv"])
+
+# Make batch prediction when the "Predict Batch" button is clicked
+if uploaded_file is not None:
+    if st.button("Predict Batch Sales"):
+        response = requests.post(
+            "https://<username>-Superkart_Docker_space.hf.space/v1/salesbatch",
+            files={"file": uploaded_file}
+        )  # Send file to backend API
+        
+        if response.status_code == 200:
+            predictions = response.json()
+            st.success(" Batch predictions completed!")
+            st.write(predictions)  # Display the predictions
+        else:
+            st.error("Error making batch prediction.")

frontend_files/requirements.txt

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+pandas==2.2.2
+requests==2.28.1
+streamlit==1.43.2