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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:sales_forecast_api"]

app.py

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+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+import numpy as np
+
+# Initialize Flask app
+sales_forecast_api = Flask("SuperKart Sales Forecast Predictor")
+
+# Load the trained SuperKart sales model
+model = joblib.load("superkart_sales_model_v1_0.joblib")
+
+# Define a route for the home page
+@sales_forecast_api.get('/')
+def home():
+    return "Welcome to the SuperKart Sales Revenue Forecasting API!"
+
+# Define an endpoint to predict sales for a single product-store combination
+@sales_forecast_api.post('/v1/sales')
+def predict_sales():
+    # Get JSON data from the request
+    sales_data = request.get_json()
+
+    # Extract relevant features from the input data
+    # Note: Store_Age will be calculated from Store_Establishment_Year
+    current_year = 2024
+    store_age = current_year - sales_data['Store_Establishment_Year']
+
+    sample = {
+        'Product_Weight': sales_data['Product_Weight'],
+        'Product_Sugar_Content': sales_data['Product_Sugar_Content'],
+        'Product_Allocated_Area': sales_data['Product_Allocated_Area'],
+        'Product_Type': sales_data['Product_Type'],
+        'Product_MRP': sales_data['Product_MRP'],
+        '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'],
+        'Store_Age': store_age
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make a prediction using the trained model
+    prediction = model.predict(input_data).tolist()[0]
+
+    # Return the prediction as a JSON response
+    return jsonify({'Predicted_Sales_Total': prediction})
+
+# Define an endpoint to predict sales for a batch of product-store combinations
+@sales_forecast_api.post('/v1/salesbatch')
+def predict_sales_batch():
+    # Get the uploaded CSV file from the request
+    file = request.files['file']
+
+    # Read the file into a DataFrame
+    input_data = pd.read_csv(file)
+
+    # Calculate Store_Age if not present
+    if 'Store_Age' not in input_data.columns:
+        current_year = 2024
+        input_data['Store_Age'] = current_year - input_data['Store_Establishment_Year']
+
+    # Make predictions for the batch data
+    predictions = model.predict(input_data).tolist()
+
+    # Add predictions to the DataFrame
+    input_data['Predicted_Sales_Total'] = predictions
+
+    # Convert results to dictionary
+    result = input_data.to_dict(orient="records")
+
+    return jsonify(result)
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    sales_forecast_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]

superkart_sales_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:200c1855aa4114e035256f3c29e366623272deda030dc23af2613f70315a8f4b
+size 23837399