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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 -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_prediction_api"]

app.py

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+import pandas as pd
+import joblib
+from flask import Flask, request, jsonify
+
+# Initiate flask app with a name
+product_sales_prediction_api = Flask("SuperKart Product Sales Prediction")
+
+# Load the trained sales prediction model
+model = joblib.load("deployment files/superkart_product_sales_prediction_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+
+@product_sales_prediction_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 Sales Prediction API!"
+
+# Define an endpoint for single property prediction (POST request)
+@product_sales_prediction_api.post("/v1/sales")
+def predict_product_sales():
+    """
+    This function handles POST requests to the '/v1/sales' endpoint.
+    It expects a JSON payload containing property details and returns
+    the predicted product sales price as a JSON response.
+    """
+    # Get the JSON data from the request body
+    product_data = requests.get_json()
+
+    # Extract relevant features from the JSON data
+    sample = {
+        'Product_Weight': product_data['Product_Weight'],
+        'Product_Sugar_Content' : product_data['Product_Sugar_Content'],
+        'Product_Allocated_Area' : product_data['Product_Allocated_Area'],
+        'Product_Type' : product_data['Product_Type'],
+        'Product_MRP' : product_data['Product_MRP'],
+        'Store_Size' : product_data['Store_Size'],
+        'Store_Location_City_Type' : product_data['Store_Location_City_Type'],
+        'Store_Type' : product_data['Store_Type'],
+        'Store_Age' : 2025- product_data['Store_Age']
+    }
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (get product_store_sales_price)
+    predicted_sales_price = model.predict(input_data)[0]
+
+    predicted_sales_price = round(float(predicted_sales_price), 2)
+
+    return jsonify({'Predicted Product Sales Price',predicted_sales_price})
+
+# Define an endpoint for batch prediction (POST request)
+@product_sales_prediction_api.post('v1/salesbatch')
+def predict_product_sales_batch():
+    """
+    This function handles POST requests to the '/v1/salesbatch' endpoint.
+    It expects a CSV file containing property details for multiple properties
+    and returns the predicted rental prices as a dictionary in the JSON response.
+    """
+    # Get the uploaded CSV file from the request
+    file = requests.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 prodict_sales)
+    predicted_product_sales = model.predict(input_data).tolist()
+    # Create a dictionary of predictions with product IDs as keys
+    product_ids=input_data['Product_Id'].tolist()
+    output_dict=dict(zip(product_ids,predicted_product_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_sales_prediction_api.run(debug=True)

requirements.txt

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+numpy==2.0.2
+pandas==2.2.2
+scikit-learn==1.6.1
+joblib==1.4.2
+xgboost==2.1.4
+requests==2.32.3
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+uvicorn[standard]