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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:sales_predictor_api"]

app.py

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+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize Flask app with a name
+sales_predictor_api = Flask("Product_Store_Total_Sales_Predictor")
+
+# Load the trained Product Store Total Sales prediction model
+model = saved_model
+
+# Define a route for the home page
+@sales_predictor_api.get('/')
+def home():
+    return "Welcome to the Product Store Total Sales Prediction API!"
+
+# Define an endpoint to predict product sales for a specific store
+@sales_predictor_api.post('/v1/product')
+def predict_product_sales():
+    # Get JSON data from the request
+    product_data = request.get_json()
+
+    # Extract relevant product features from the input data
+    sample = {
+        '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'].map(size_map),
+        'Store_Location_City_Type': product_data['Store_Location_City_Type'],
+        'Store_Type': product_data['Store_Type'],
+        'Store_Age': 2025 - product_data['Store_Establishment_Year']
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make a churn prediction using the trained model
+    prediction = model.predict(input_data).tolist()[0]
+
+    # Return the prediction as a JSON response
+    return jsonify({'Product Store Sales Total': prediction})
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    app.run(debug=True)

product_sales_total_prediction_v1_0.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f943dd8c0455f29391c420fcda63861f855f3d4bf3e1539ff6ea80c5a205bfef
+size 45040883

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]
+streamlit==1.43.2
+huggingface_hub==0.30.1