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

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_forecast_revenue = Flask("Superkart Forecast Revenue")
+
+# Load the trained machine learning model
+model = joblib.load("./backend_files/forecast_sales_prediction_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+@forecast_revenue_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 Forecast Revenue API!"
+
+# Define an endpoint for single property prediction (POST request)
+@forecast_revenue_api.post('/v1/revenue')
+def forecast_revenue():
+    """
+    This function handles POST requests to the '/v1/revenue' endpoint.
+    It expects a JSON payload containing store details and returns
+    the predicted revenue as a JSON response.
+    """
+    # Get the JSON data from the request body
+    store_data = request.get_json()
+
+    # Extract relevant features from the JSON data
+    sample = {
+        'product_weight': store_data['Product_Weight'],
+        'product_allocated_area': store_data['Product_Allocated_Area'],
+        'product_mrp': store_data['Product_MRP'],
+        'product_sugar_content': store_data['Product_Sugar_Content'],
+        'product_type': store_data['Product_Type'],
+        'store_size': store_data['Store_Size'],
+        'city_type': store_data['Store_Location_City_Type'],
+        'store_type': store_data['Store_Type']    
+        }
+
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (get log_price)
+    forecast_revenue = model.predict(input_data)[0]
+
+    # Return the actual price
+    return jsonify({'Forecasted revenue (in dollars)': forecast_revenue})
+
+# Run the Flask application in debug mode if this script is executed directly
+if __name__ == '__main__':
+    superkart_forecast_revenue.run(debug=True)

forecast_sales_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d7cd08970ef445b2770bc59f56e314e9bd6c39da505a3e21ba7f1a6e23e6c4f3
+size 207760

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