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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
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:product_store_sales_predictor_api"]

app.py

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+
+# Initialize the Flask application
+product_store_sales_predictor_api = Flask("SuperKart Product Store Sales Predictor")
+
+# Load the trained machine learning model
+model = joblib.load("saved_model_path")
+
+# Define a route for the home page (GET request)
+@product_store_sales_predictor_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 SuperKart Product Store Sales Predictor API!"
+
+# Define an endpoint for single property prediction (POST request)
+@product_store_sales_predictor_api.post('/v1/productsales')
+def predict_product_sales():
+    """
+    This function handles POST requests to the '/v1/productsales' endpoint.
+    It expects a JSON payload containing Product and store details and returns
+    the predicted sales price as a JSON response.
+    """
+    # Get the JSON data from the request body
+    product_data = request.get_json()
+
+    # Extract relevant features from the JSON data
+    sample = {
+        'Product_Weight': product_data['Product_Weight'],
+        'Product_Allocated_Area': product_data['Product_Allocated_Area'],
+        'Product_MRP': product_data['Product_MRP'],
+        'Store_Age': product_data['Store_Age'],
+        'Product_Identifier': product_data['Product_Identifier'],
+        'Product_Sugar_Content_No Sugar': product_data['Product_Sugar_Content_No Sugar'],
+        'Product_Sugar_Content_Regular': product_data['Product_Sugar_Content_Regular'],
+        'Product_Sugar_Content_reg': product_data['Product_Sugar_Content_reg'],
+        'Product_Type_Breads': product_data['Product_Type_Breads'],
+        'Product_Type_Breakfast': product_data['Product_Type_Breakfast'],
+        'Product_Type_Canned': product_data['Product_Type_Canned'],
+        'Product_Type_Dairy': product_data['Product_Type_Dairy'],
+        'Product_Type_Frozen Foods': product_data['Product_Type_Frozen Foods'],
+        'Product_Type_Fruits and Vegetables': product_data['Product_Type_Fruits and Vegetables'],
+        'Product_Type_Hard Drinks': product_data['Product_Type_Hard Drinks'],
+        'Product_Type_Health and Hygiene': product_data['Product_Type_Health and Hygiene'],
+        'Product_Type_Household': product_data['Product_Type_Household'],
+        'Product_Type_Meat': product_data['Product_Type_Meat'],
+        'Product_Type_Others': product_data['Product_Type_Others'],
+        'Product_Type_Seafood': product_data['Product_Type_Seafood'],
+        'Product_Type_Snack Foods': product_data['Product_Type_Snack Foods'],
+        'Product_Type_Soft Drinks': product_data['Product_Type_Soft Drinks'],
+        'Product_Type_Starchy Foods': product_data['Product_Type_Starchy Foods'],
+        'Store_Id_OUT002': product_data['Store_Id_OUT002'],
+        'Store_Id_OUT003': product_data['Store_Id_OUT003'],
+        'Store_Id_OUT004': product_data['Store_Id_OUT004'],
+        'Store_Size_Medium': product_data['Store_Size_Medium'],
+        'Store_Size_Small': product_data['Store_Size_Small'],
+        'Store_Location_City_Type_Tier 2': product_data['Store_Location_City_Type_Tier 2'],
+        'Store_Location_City_Type_Tier 3': product_data['Store_Location_City_Type_Tier 3'],
+        'Store_Type_Food Mart': product_data['Store_Type_Food Mart'],
+        'Store_Type_Supermarket Type1': product_data['Store_Type_Supermarket Type1'],
+        'Store_Type_Supermarket Type2': product_data['Store_Type_Supermarket Type2']
+    }
+
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (get log_price)
+    predicted_sales = model.predict(input_data)[0]
+
+    # Convert predicted_price to Python float
+    predicted_sales = round(float(predicted_sales), 2)
+    # The conversion above is needed as we convert the model prediction (log price) to actual price using np.exp, which returns predictions as NumPy float32 values.
+    # When we send this value directly within a JSON response, Flask's jsonify function encounters a datatype error
+
+    # Return the actual price
+    return jsonify({'Predicted Product Store Sales Total': predicted_sales})
+
+
+# Define an endpoint for batch prediction (POST request)
+@product_store_sales_predictor_api.post('/v1/productsalesbatch')
+def predict_product_sales_batch():
+    """
+    This function handles POST requests to the '/v1/productsalesbatch' endpoint.
+    It expects a CSV file containing product and store details for multiple entries
+    and returns the predicted sales as a dictionary in the JSON response.
+    """
+    # Get the uploaded CSV file from the request
+    file = request.files['file']
+
+    # Read the CSV file into a Pandas DataFrame
+    input_data = pd.read_csv(file)
+
+    # Make predictions for all products in the DataFrame
+    predicted_sales = model.predict(input_data).tolist()
+
+    # Create a dictionary of predictions with Product_Id as keys
+    product_ids = input_data['Product_Id'].tolist()  # Assuming 'Product_Id' is the product ID column
+    output_dict = dict(zip(product_ids, predicted_sales))
+
+    # Return the predictions dictionary as a JSON response
+    return output_dict
+
+# Run the Flask application in debug mode if this script is executed directly
+if __name__ == '__main__':
+    product_store_sales_predictor_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]
+streamlit==1.43.2