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Dockerfile

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+
+# Base Image
+FROM python:3.9-slim
+
+# Setting the working directory inside the container
+WORKDIR /app
+
+# Copying all files from the current directory to the container's working directory
+COPY . .
+
+# Installing dependencies from the requirements file without using cache to reduce image size
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+# Defining the command to start the application using Gunicorn with 4 worker processes
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:product_store_sales_predictor_api"]

app.py

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+
+# Libraries to help with reading and manipulating data
+import numpy as np
+import pandas as pd  
+
+# For loading the serialized model
+import joblib  
+
+# For creating the Flask API
+from flask import Flask, request, jsonify  
+
+# Initializing the Flask application
+product_store_sales_predictor_api = Flask("Product Store Sales Predictor")
+
+# Loading the serialised ML model (XGBRegressor Tuned)
+model = joblib.load("product_store_sales_prediction_model_v1_0.joblib")
+
+# Route for the home page (GET request)
+@product_store_sales_predictor_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 Product Store Sales Prediction API!"
+
+# Endpoint for Sales prediction for a single product in a given store (POST request)
+@product_store_sales_predictor_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 amount as a JSON response.
+    """
+    # Retrieving the JSON data from the request body
+    product_store_data = request.get_json()
+
+    # Extracting the required from the JSON data
+    sample = {
+        'Product_Weight': product_store_data['Product_Weight'], 
+        'Product_Allocated_Area': product_store_data['Product_Allocated_Area'],
+        'Product_MRP': product_store_data['Product_MRP'],
+        'Store_Establishment_Year': product_store_data['Store_Establishment_Year'],
+        'Product_Sugar_Content': product_store_data['Product_Sugar_Content'],
+        'Product_Type': product_store_data['Product_Type'],
+        'Store_Id': product_store_data['Store_Id'],
+        'Store_Size': product_store_data['Store_Size'],
+        'Store_Location_City_Type': product_store_data['Store_Location_City_Type'],
+        'Store_Type': product_store_data['Store_Type']
+    }
+
+    # Converting the extracted data into a Pandas DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Predicting the sales amount
+    predicted_sales = model.predict(input_data)[0]
+
+    # Convert predicted_sales to Python float
+    predicted_sales = round(float(predicted_sales), 2)
+
+    # Return the predicted sales amount
+    return jsonify({'Predicted Sales Amount': predicted_sales})
+
+if __name__ == '__main__':
+    product_store_sales_predictor_api.run(debug=True)

product_store_sales_prediction_model_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b18f749f9335729b14cc01661acd8d9f6ffefba9a225d3d2b5d424d6a1131980
+size 208249

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