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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:SK_Sales_Forecast_api"]

SuperKart_Sales_Forecast_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a874074e877e30b5ab16dfe0fe5ddcb1fd001ecca468fbe745f12a7fb3b4c714
+size 206602

app.py

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+# Import necessary libraries
+from datetime import datetime
+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
+SK_Sales_Forecast_api = Flask("SK_Sales_Backend")
+
+# Load the trained machine learning model
+model = joblib.load("deployment/SuperKart_Sales_Forecast_model_v1_0.joblib")
+
+# Define a route for the home page (GET request)
+@SK_Sales_Forecast_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 Forecast API!"
+
+# Define an endpoint for single product sales prediction (POST request)
+@SK_Sales_Forecast_api.post('/salespredict')
+
+
+def predict_product_sale():
+    """
+    This function handles POST requests to the '/salespredict' endpoint.
+    It expects a JSON payload containing property details and returns
+    the predicted rental price as a JSON response.
+    """
+    # Get the JSON data from the request body
+    product_data = request.get_json()
+
+    # Extract relevant features from the JSON data
+    sample = {
+        'Product_Id': product_data['Product_Id'],
+        'Product_Weight': product_data['Product_Weight'],
+        'Product_Sugar_Content': product_data['Product_Sugar_Content'],
+        'Product_Allocated_Area': product_data['Product_Allocated_Area'],
+        'Product_Type': product_data['Product_Type'],
+        'Product_MRP': product_data['Product_MRP'],
+        'Store_Establishment_Year': product_data['Store_Establishment_Year'],
+        'Store_Size': product_data['Store_Size'],
+        'Store_Location_City_Type': product_data['Store_Location_City_Type'],
+        'Store_Type': product_data['Store_Type']
+    }
+
+    # Convert the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Extract the Product_Code and Store_Age before feeding to the model
+    input_data["Product_Code"] = input_data["Product_Id"].str[:2]
+    input_data.drop("Product_Id", axis=1, inplace=True)
+
+    current_year = datetime.now().year
+    input_data["Store_Age"] = current_year - input_data["Store_Establishment_Year"]
+    input_data.drop("Store_Establishment_Year", axis=1, inplace=True)
+
+    # Make prediction
+    predicted_sale = model.predict(input_data)[0]
+
+    # Return the actual price
+    return jsonify({'Predicted Sale': predicted_sale})
+
+
+# Define an endpoint for batch prediction (POST request)
+@SK_Sales_Forecast_api.post('/salespredictbatch')
+
+
+def predict_product_sale_batch():
+    """
+    This function handles POST requests to the '/salespredictbatch' 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)
+
+    # Extract the Product_Code and Store_Age before feeding to the model
+    input_data["Product_Code"] = input_data["Product_Id"].str[:2]
+    product_ids = input_data['Product_Id'].tolist()
+    input_data.drop("Product_Id", axis=1, inplace=True)
+    
+    current_year = datetime.now().year
+    input_data["Store_Age"] = current_year - input_data["Store_Establishment_Year"]
+    input_data.drop("Store_Establishment_Year", axis=1, inplace=True)
+
+    # Make predictions for all properties in the DataFrame
+    predicted_sales = model.predict(input_data).tolist()
+
+    # Create a dictionary of predictions with product IDs as keys
+    #product_ids = input_data['Product_Id'].tolist()
+    output_dict = dict(zip(product_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__':
+    SK_Sales_Forecast_api.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==2.28.1
+uvicorn[standard]
+streamlit==1.43.2