Upload folder using huggingface_hub

Dockerfile

@@ -0,0 +1,16 @@
+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 '/content/drive/MyDrive/Colab Notebooks/Module 7 - Model Deployment/Project/rf/backend_files/*' . 
+
+# 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:rf_superkart_prediction_api"]

app.py

@@ -0,0 +1,54 @@
+
+# Initialize the Flask application
+rf_superkart_prediction_api = Flask("SuperKart Sales Prediction with Random Forest")
+
+# Load the trained machine learning model
+rf_model = joblib.load("/content/drive/MyDrive/Colab Notebooks/Module 7 - Model Deployment/Project/rf/backend_files/superkart_sales_prediction_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+@rf_superkart_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 the SuperKart Sales Prediction API!"
+
+# Define an endpoint for single property prediction (POST request)
+@rf_superkart_prediction_api.post('/v1/predict')
+def predict_sales():
+    """
+    This function handles POST requests to the '/v1/predict' endpoint.
+    It expects a JSON payload containing store details and returns
+    the predicted sales as a JSON response.
+    """
+    # Get the JSON data from the request body
+    store_data = request.get_json()
+
+    # Extract relevant features from the JSON data
+    sample = {
+        'Product_Weight': data['Product_Weight'],
+        'Product_Sugar_Content': data['Product_Sugar_Content'],
+        'Product_Allocated_Area': data['Product_Allocated_Area'],
+        'Product_MRP': data['Product_MRP'],
+        'Store_Size': data['Store_Size'],
+        'Store_Location_City_Type': data['Store_Location_City_Type'],
+        'Store_Type': data['Store_Type'],
+        'Product_Id_char': data['Product_Id_char'],
+        'Store_Age_Years': data['Store_Age_Years'],
+        'Product_Type_Category': data['Product_Type_Category']
+    }
+
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make prediction (get log_price)
+    predicted_sales = rf_model.predict(input_data)[0]
+
+    # Return the prediction
+    return jsonify({'Predicted Sales': predicted_sales})
+
+
+# Run the Flask application in debug mode if this script is executed directly
+if __name__ == '__main__':
+    rf_superkart_prediction_api.run(debug=True)

requirements.txt

@@ -0,0 +1,13 @@
+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+seaborn==0.13.2
+joblib==1.4.2
+xgboost==2.1.4
+joblib==1.4.2
+Werkzeug==2.2.2
+flask==2.2.2
+gunicorn==20.1.0
+requests==2.32.3
+uvicorn[standard]
+streamlit==1.43.2

superkart_sales_prediction_model_v1_0.joblib

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c9155b5afe619d61ca196d3c9b06aa3d345171a955358074ddca2dbb0a3e4d83
+size 41408531