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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:superkart_product_sales_prediction_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
+superkart_product_sales_prediction_api = Flask("SuperKart Sales Forecast API")
+
+# Load the trained churn prediction model
+model = joblib.load("superkart_product_sales_prediction_model_v1_0.joblib")
+
+# Define a route for the home page
+@superkart_product_sales_prediction_api.get('/')
+def home():
+    return "Welcome to the SuperKart Sales Forecast API"
+
+# Define an endpoint to predict churn for a single customer
+@superkart_product_sales_prediction_api.post('/v1/forecast')
+def predict_sales_forecast():
+   """
+    This function handles POST requests to the '/v1/forecast' endpoint.
+    It expects a JSON payload containing property details and returns
+    the predicted rental price as a JSON response.
+    """
+
+    # Get JSON data from the request
+    product_data = request.get_json()
+
+    # Extract relevant customer features from the input data
+    sample = {
+        'Product_Weight': product_data.get('Product_Weight'),
+        'Product_Sugar_Content': product_data.get('Product_Sugar_Content'),
+        'Product_Allocated_Area': product_data.get('Product_Allocated_Area'),
+        'Product_Type': product_data.get('Product_Type'),
+        'Product_MRP': product_data.get('Product_MRP'),
+        'Store_Id': product_data.get('Store_Id'),
+        'Store_Establishment_Year': product_data.get('Store_Establishment_Year'),
+        'Store_Size': product_data.get('Store_Size'),
+        'Store_Location_City_Type': product_data.get('Store_Location_City_Type'),
+        'Store_Type': product_data.get('Store_Type')
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make a churn prediction using the trained model
+    predicted_sales = model.predict(input_data).tolist()[0]
+
+    # Convert predicted_price to Python float
+    predicted_sales = round(float(predicted_sales), 2)
+
+
+    # Return the prediction as a JSON response
+    return jsonify({'Prediction': predicted_sales})
+
+@superkart_product_sales_prediction_api.post('/v1/forecastbatch')
+def predict_sales_forecast_batch():
+    """
+    This function handles POST requests to the '/v1/forecastbatch' endpoint.
+    It expects a CSV file containing product and store details for multiple products
+    and returns the predicted sales forecast prices 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 properties in the DataFrame (get log_prices)
+    predicted_sales = model.predict(input_data).tolist()
+
+
+    # Create a dictionary of predictions with property IDs as keys
+    product_ids = input_data['Product_Id'].tolist()  # Assuming 'id' is the property ID column
+    output_dict = dict(zip(product_ids, predicted_sales))  # Use actual prices
+
+    # Return the predictions dictionary as a JSON response
+    return output_dict
+
+# 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
+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