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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_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
+from pathlib import Path # For using a robust, absolute path
+
+# Define the base directory of the script
+BASE_DIR = Path(__file__).resolve().parent
+
+# Define the full path to your model file
+MODEL_PATH = BASE_DIR / "xgb_tuned.joblib"
+
+# Initialize Flask application
+superkart_api = Flask("SuperKart Sales Predictor")
+
+# Load the trained machine learning model
+model = joblib.load(MODEL_PATH)
+
+# Define a route for the home page (GET request)
+@superkart_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 Predictor API !"
+
+# Define an endpoint to predict for a single observation
+@superkart_api.post('/v1/predict')
+def predict_sales():
+    """
+    This function handles POST requests to the '/v1/predict' 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
+    data = request.get_json()
+
+    # Extract relevant customer features from the input data. The order of the column names matters.
+    sample = {
+        'Product_Weight': data['Product_Weight'],
+        'Product_MRP': data['Product_MRP'],
+        'Product_Allocated_Area': data['Product_Allocated_Area'],
+        'Product_Sugar_Content': data['Product_Sugar_Content'],
+        'Store_Size': data['Store_Size'],
+        'Store_Location_City_Type': data['Store_Location_City_Type'],
+        'Store_Type': data['Store_Type'],
+        'Store_Age_Years': data['Store_Age_Years'],
+        'Product_Id_prefix': data['Product_Id_prefix'],
+        'Product_FD_perishable': data['Product_FD_perishable'],
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make a store sales prediction using the trained model
+    prediction = model.predict(input_data).tolist()[0]
+
+    # Return the prediction as a JSON response
+    return jsonify({'Sales': prediction})
+
+# Define an endpoint for batch prediction (POST request)
+@superkart_api.post('/v1/batch')
+def predict_sales_batch():
+    """
+    This function handles POST requests to the '/v1/batch' 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 = 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
+    predicted_sales = model.predict(input_data).tolist()
+
+    # Create a dictionary of predictions with property IDs as keys
+    product_ids = input_data['Product_Id'].tolist()
+    output_dict = dict(zip(product_ids, predicted_sales))
+
+    # Return the predictions dictionary as a JSON response
+    return output_dict
+
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    superkart_api.run(debug=True)

requirements.txt

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+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

xgb_tuned.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5a34eaf37d4a092b2e7be417213f8644f5147374e5db932101cd8b480d64bd2e
+size 120950