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Dockerfile

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+FROM python:3.11.13
+
+# 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:super_kart_api"]

app.py

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+# Import necessary libraries
+import numpy as np
+import joblib  # For loading the serialized model
+import pandas as pd  # For data manipulation
+from flask import Flask, request, jsonify  # For creating the Flask API
+
+# Initialize the Flask application
+super_kart_api = Flask("Super Kart Price Predictor")
+
+# Load the trained machine learning model (updated to match Super Kart model file)
+model = joblib.load("backend_files/super_kart_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+@super_kart_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 Super Kart Price Prediction API!"
+
+# Define an endpoint for single product sales prediction (POST request)
+@super_kart_api.post('/v1/sales')
+def predict_sales():
+    """
+    This function handles POST requests to the '/v1/sales' endpoint.
+    It expects a JSON payload containing product and store details and returns
+    the predicted sales total as a JSON response.
+    """
+    # Get the JSON data from the request body
+    input_data = request.get_json()
+
+    # Extract relevant features from the JSON data
+    # Note: Exclude Product_Id and Store_Id if they are not used in prediction
+    sample = {
+        'Product_Weight': input_data['Product_Weight'],
+        'Product_Sugar_Content': input_data['Product_Sugar_Content'],
+        'Product_Allocated_Area': input_data['Product_Allocated_Area'],
+        'Product_Type': input_data['Product_Type'],
+        'Product_MRP': input_data['Product_MRP'],
+        'Store_Establishment_Year': input_data['Store_Establishment_Year'],
+        'Store_Size': input_data['Store_Size'],
+        'Store_Location_City_Type': input_data['Store_Location_City_Type'],
+        'Store_Type': input_data['Store_Type']
+    }
+
+    # Convert the extracted data into a Pandas DataFrame
+    features_df = pd.DataFrame([sample])
+
+    # Apply one-hot encoding for nominal columns (matching training)
+    features_df = pd.get_dummies(features_df, columns=['Product_Type', 'Store_Type'], drop_first=True)
+
+    # Apply ordinal encoding (based on provided orders)
+    sugar_mapping = {'No Sugar': 0, 'Low Sugar': 1, 'Regular': 2}
+    size_mapping = {'Small': 0, 'Medium': 1, 'High': 2}
+    city_mapping = {'Tier 3': 0, 'Tier 2': 1, 'Tier 1': 2}
+
+    features_df['Product_Sugar_Content'] = features_df['Product_Sugar_Content'].map(sugar_mapping)
+    features_df['Store_Size'] = features_df['Store_Size'].map(size_mapping)
+    features_df['Store_Location_City_Type'] = features_df['Store_Location_City_Type'].map(city_mapping)
+
+    # Make prediction (assuming direct sales prediction; adjust if log-transformed)
+    predicted_sales = model.predict(features_df)[0]
+
+    # If your model predicts log(sales), uncomment and use this instead:
+    # predicted_log_sales = model.predict(features_df)[0]
+    # predicted_sales = np.exp(predicted_log_sales)
+
+    # Convert to Python float and round to 2 decimals
+    predicted_sales = round(float(predicted_sales), 2)
+
+    # Return the predicted sales total
+    return jsonify({'Predicted Sales Total (in dollars)': predicted_sales})
+
+# Run the app (for testing locally; remove or adjust for production)
+if __name__ == '__main__':
+    super_kart_api.run(debug=True)

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+scikit-learn==1.6.1
+xgboost==3.0.4
+joblib==1.5.1
+Werkzeug==3.1.3
+flask==3.1.1
+gunicorn==20.1.0
+requests==2.28.1
+uvicorn[standard]

super_kart_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c63695fa45429ae2f8178467fb3d36e524c2bf14adf7d3daa8df6c67188ac6c
+size 410265