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Dockerfile

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+# Use a lightweight official Python image
+FROM python:3.9-slim-buster
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy the requirements file and install dependencies
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the application files
+COPY . .
+
+# Expose the port the Flask app will run on
+#EXPOSE 7860
+
+# Command to run the Flask application
+#CMD ["python", "app.py"]
+
+# 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_revenue_forecaster_api"]

app.py

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+
+import joblib
+from flask import Flask, request, jsonify
+import pandas as pd
+import numpy as np
+
+# Load the trained model and preprocessor
+model = joblib.load('final_model.joblib')
+preprocessor = joblib.load('preprocessor.joblib')
+
+superkart_revenue_forecaster_api = Flask("SuperKart Sales Revenue Forecaster")
+
+# Define a route for the home page
+@superkart_revenue_forecaster_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 Revenue Forecaster - By Vidyasagar Chitchula!'
+
+# Define the prediction route
+@superkart_revenue_forecaster_api.route('/forecast_revenue', methods=['POST'])
+def forecast_Revenue():
+    try:
+        data = request.get_json()
+
+        # Convert input data to a Pandas DataFrame
+        input_df = pd.DataFrame([data])
+
+        # Recreate the 'Store_Age' feature if 'Store_Establishment_Year' is provided
+        if 'Store_Establishment_Year' in input_df.columns:
+            input_df['Store_Age'] = 2025 - input_df['Store_Establishment_Year']
+            input_df = input_df.drop('Store_Establishment_Year', axis=1)
+
+        # Drop 'Product_Id' if it exists in the input
+        if 'Product_Id' in input_df.columns:
+            input_df = input_df.drop('Product_Id', axis=1)
+
+        # Preprocess the input data
+        processed_data = preprocessor.transform(input_df)
+
+        # Make prediction
+        prediction = model.predict(processed_data)
+
+        return jsonify({'predicted_sales': prediction[0]})
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+if __name__ == '__main__':
+    superkart_revenue_forecaster_api.run(host='0.0.0.0', port=5000)

final_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4952cc0a10eaddb3114c051e637a29c35c2279b004a5b33a09fd25a5514778cc
+size 485605

preprocessor.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:df5c9a62cdbbded3f573d4a2fc5f63903447b00652cec8c6d3210998bb161920
+size 5118

requirements.txt

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+flask
+joblib==1.4.2
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==2.1.4
+requests==2.32.3