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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", "0.0.0.0:5000", "app:app"]

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
+app = Flask("Store Sales Predictor")
+
+# Load the trained machine learning model
+model = joblib.load("store_sales_prediction_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+@app.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 Store Sales Prediction API!"
+
+
+# Define an endpoint for single property prediction (POST request)
+@app.post('/v1/sales')
+def predict_sales():
+    """
+    This function handles POST requests to the '/v1/sales' endpoint.
+    It expects a JSON payload containing property details and returns
+    the predicted store sales as a JSON response.
+    """
+    # Get the JSON data from the request body
+    dataset = request.get_json()
+
+    # Extract relevant features from the JSON data
+    sample = {
+        'Product_Weight': dataset['Product_Weight'],
+        'Product_Sugar_Content': dataset['Product_Sugar_Content'],
+        'Product_Allocated_Area': dataset['Product_Allocated_Area'],
+        'Product_Type': dataset['Product_Type'],
+        'Product_MRP': dataset['Product_MRP'],
+        'Store_Establishment_Year': dataset['Store_Establishment_Year'],
+        'Store_Size': dataset['Store_Size'],
+        'Store_Location_City_Type': dataset['Store_Location_City_Type'],
+        'Store_Type': dataset['Store_Type']
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make a sales prediction using the trained model
+    prediction = model.predict(input_data)[0]
+
+    # Return the prediction as a JSON response
+    return jsonify({'predicted_sales': float(round(prediction, 2))})
+
+
+# Define an endpoint for batch prediction (POST request)
+#@rental_price_predictor_api.post('/v1/salesbatch')
+#def predict_store_sales_batch():
+   # """
+   # This function handles POST requests to the '/v1/salesbatch' endpoint.
+  #  It expects a CSV file containing store and product details for multiple stores and products
+   # and returns the predicted sales 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 (get log_prices)
+   # predicted_log_sales = model.predict(input_data).tolist()
+
+    # Calculate actual prices
+   # predicted_sales = [round(float(np.exp(log_price)), 2) for log_price in predicted_log_sales]
+
+    # Create a dictionary of predictions with property IDs as keys
+   # property_ids = input_data['id'].tolist()  # Assuming 'id' is the property ID column
+   # output_dict = dict(zip(property_ids, predicted_sales))  # Use actual prices
+
+    # 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__':
+    app.run(debug=True)

requirements.txt

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+pandas==2.2.2
+numpy==2.0.2
+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==2.32.3
+uvicorn[standard]
+

store_sales_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:44c869a1419eebb76fe8f1218424fc4ce5c91bc00b7fd3dcdba98b39d8baaf5c
+size 6538