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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_predictor_api"]

app.py

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+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize Flask app with a name
+sales_predictor_api = Flask("SuperKart Sales Predictor")
+
+# Load the trained churn prediction model
+model = joblib.load("sales_prediction_model_v1_0.joblib")
+
+# Define a route for the home page
+@sales_predictor_api.get('/')
+def home():
+    return "Welcome to the SuperKart Sales Prediction API!"
+
+# Define an endpoint to predict churn for a product sale info
+@sales_predictor_api.post('/v1/predict')
+def predict_sales():
+    try:
+        # Get JSON data from the request
+        data = request.get_json()
+
+        # Extract features expected by the model
+        sample = {
+            'Product_Weight': data['Product_Weight'],
+            'Product_Sugar_Content': data['Product_Sugar_Content'],
+            'Product_Allocated_Area': data['Product_Allocated_Area'],
+            'Product_Type': data['Product_Type'],
+            'Product_MRP': data['Product_MRP'],
+            'Store_Id': data['Store_Id'],
+            'Store_Establishment_Year': data['Store_Establishment_Year'],
+            'Store_Size': data['Store_Size'],
+            'Store_Location_City_Type': data['Store_Location_City_Type'],
+            'Store_Type': data['Store_Type']
+        }
+
+        # Convert to DataFrame
+        input_df = pd.DataFrame([sample])
+
+        # Predict sales (numeric value)
+        prediction = model.predict(input_df).tolist()[0]
+
+        # Return prediction as JSON
+        return jsonify({
+            'Predicted_Sales': round(prediction, 2)
+        })
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    app.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]
+streamlit==1.43.2

sales_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:efd9ab74ce639892dbc38b9f2c14e2215ebfd4891dbb653d961285ccb6da823e
+size 641443