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Dockerfile

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+FROM python:3.11-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:product_sales_predictor_api"]

app.py

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+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize Flask app
+product_sales_predictor_api = Flask("Product Sales Predictor")
+
+# Load the trained model
+model = joblib.load("../model/product_sales_prediction_model_v1_0.joblib")
+
+# Home endpoint
+@product_sales_predictor_api.get('/')
+def home():
+    return "Welcome to the SuperKart Product Sales Prediction API!"
+
+# Single product prediction
+@product_sales_predictor_api.post('/v1/product')
+def predict_single_product():
+    try:
+        # Get JSON data from request
+        product_info = request.get_json()
+
+        # Extract features
+        prod_input = {
+            'Product_Weight': product_info['Product_Weight'],
+            'Product_Sugar_Content': product_info['Product_Sugar_Content'],
+            'Product_Allocated_Area': product_info['Product_Allocated_Area'],
+            'Product_Type': product_info['Product_Type'],
+            'Product_MRP': product_info['Product_MRP'],
+            'Store_Id': product_info['Store_Id'],
+            'Store_Establishment_Year': product_info['Store_Establishment_Year'],
+            'Store_Size': product_info['Store_Size'],
+            'Store_Location_City_Type': product_info['Store_Location_City_Type'],
+            'Store_Type': product_info['Store_Type']
+        }
+
+        # Convert to DataFrame and predict
+        input_data = pd.DataFrame([prod_input])
+        prediction = model.predict(input_data)[0]
+
+        return jsonify({'predicted_sales': round(prediction, 2)})
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+# Batch prediction
+@product_sales_predictor_api.post('/v1/products')
+def predict_multiple_products():
+    try:
+        # Get uploaded CSV file
+        input_file = request.files['file']
+        input_data = pd.read_csv(input_file)
+
+        # Remove Product_Id if present
+        prediction_data = input_data.drop(['Product_Id'], axis=1, errors='ignore')
+
+        # Make predictions
+        predictions = model.predict(prediction_data).tolist()
+        predictions = [round(pred, 2) for pred in predictions]
+
+        # Create output dictionary
+        if 'Product_Id' in input_data.columns:
+            prod_id_list = input_data['Product_Id'].tolist()
+            result = dict(zip(prod_id_list, predictions))
+        else:
+            result = {f"product_{i+1}": pred for i, pred in enumerate(predictions)}
+
+        return jsonify(result)
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 500
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

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+numpy: 2.3.2
+pandas: 2.3.1
+scikit-learn: 1.7.1
+xgboost: 3.0.4
+joblib: 1.5.1
+Werkzeug: 3.1.3
+flask: 3.1.2
+gunicorn: 23.0.0
+requests: 2.32.4
+uvicorn[standard]
+streamlit: 1.48.1