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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:store_sales_predictor_api"]

app.py

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+from flask import Flask, request, jsonify
+
+# Initialize Flask app
+store_sales_predictor_api = Flask("Super Kart Store Sales Predictor Application")
+
+# Load the trained model
+try:
+    superkart_model = joblib.load("superkart_storesales_prediction_model_v1_0.joblib") #superkart_deployment_files/
+    print("Model loaded successfully.")
+except FileNotFoundError:
+    print("Error: 'superkart_storesales_prediction_model_v1_0.joblib' not found. Please train and save the model first.")
+    superkart_model = None
+
+# Define home page for app
+@store_sales_predictor_api.get('/')
+def home():
+    return "Welcome to Super Kart Store Sales Predictor Application",200
+
+# Health check endpoint
+@store_sales_predictor_api.get('/healthcheck')
+def health_check():
+    """
+    Returns a 200 status code and a JSON response to indicate the service is healthy.
+    """
+    return jsonify({"status": "healthy"}), 200
+    
+# Define prediction form page for app
+@store_sales_predictor_api.post('/v1/predict')
+def predict_sales_price():
+
+    """
+    Handles prediction requests.
+    Expects a JSON payload with 'features'.
+    """
+    try:
+        # Get data from the POST request
+        payload = request.get_json()
+    
+        # Extract Relevant Features from Payload
+        app_features = {
+            "Product_Weight": payload["Product_Weight"],
+            "Product_Sugar_Content": payload["Product_Sugar_Content"],
+            "Product_Allocated_Area": payload["Product_Allocated_Area"],
+            "Product_Type": payload["Product_Type"],
+            "Product_MRP": payload["Product_MRP"],
+            "Store_Establishment_Year": payload["Store_Establishment_Year"],
+            "Store_Location_City_Type": payload["Store_Location_City_Type"],
+            "Store_Type": payload["Store_Type"],
+            "Store_Size": payload["Store_Size"]}    
+        # store app_features in dataframe
+        input_data = pd.DataFrame([app_features])
+    
+        # Make prediction and get store sales
+        predicted_sales = superkart_model.predict(input_data)[0]
+    
+        # calculate actual value
+        predicted_sales_value = np.exp(predicted_sales)
+    
+        # convert value to python float
+        predicted_sales_value = round(float(predicted_sales_value),2)
+    
+        return jsonify({"predicted store sales total-": predicted_sales_value}), 200
+
+    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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+pandas==2.2.2
+numpy==2.0.2
+datetime
+seaborn
+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]

superkart_storesales_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d2b143d1a035b38f23cb8fd447f1ca593c390c7f9992a88c46dd824db88fb452
+size 1433507

superkartapp.py

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+from flask import Flask, request, jsonify
+
+# Initialize Flask app
+store_sales_predictor_api = Flask("Super Kart Store Sales Predictor Application")
+
+# Load the trained model
+try:
+    superkart_model = joblib.load("superkart_storesales_prediction_model_v1_0.joblib") #superkart_deployment_files/
+    print("Model loaded successfully.")
+except FileNotFoundError:
+    print("Error: 'superkart_storesales_prediction_model_v1_0.joblib' not found. Please train and save the model first.")
+    superkart_model = None
+
+# Define home page for app
+@store_sales_predictor_api.get('/')
+def home():
+    return "Welcome to Super Kart Store Sales Predictor Application",200
+
+# Health check endpoint
+@store_sales_predictor_api.get('/healthcheck')
+def health_check():
+    """
+    Returns a 200 status code and a JSON response to indicate the service is healthy.
+    """
+    return jsonify({"status": "healthy"}), 200
+    
+# Define prediction form page for app
+@store_sales_predictor_api.post('/v1/predict')
+def predict_sales_price():
+
+    """
+    Handles prediction requests.
+    Expects a JSON payload with 'features'.
+    """
+    try:
+        # Get data from the POST request
+        payload = request.get_json()
+    
+        # Extract Relevant Features from Payload
+        app_features = {
+            "Product_Weight": payload["Product_Weight"],
+            "Product_Sugar_Content": payload["Product_Sugar_Content"],
+            "Product_Allocated_Area": payload["Product_Allocated_Area"],
+            "Product_Type": payload["Product_Type"],
+            "Product_MRP": payload["Product_MRP"],
+            "Store_Establishment_Year": payload["Store_Establishment_Year"].
+            "Store_Location_City_Type": payload["Store_Location_City_Type"],
+            "Store_Type": payload["Store_Type"],
+            "Store_Size": payload["Store_Size"]
+        }
+    
+       # store app_features in dataframe
+       input_data= pd.DataFrame([app_features])
+    
+       # Make prediction and get store sales
+       predicted_sales = superkart_model.predict(input_data)[0]
+    
+       # calculate actual value
+       predicted_sales_value = np.exp(predicted_sales)
+    
+       # convert value to python float
+       predicted_sales_value = round(float(predicted_sales_value),2)
+    
+       return jsonify({"predicted store sales total-": predicted_sales_value}), 200
+
+   except Exception as e:
+        return jsonify({"error": str(e)}), 500
+  
+   # Run the Flask app in debug mode
+   if __name__ == '__main__':
+     app.run(debug=True)