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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:app"]

XGBoostRegressor_BEST_Pipeline.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1316e0f1ffa836f435afbed414f60cb1ca4f0d1ab02c9b3c3a3c3246ef4aabb2
+size 2236826

app.py

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+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+# Initialize Flask app with a name
+app = Flask("SuperKart Sales Predictor")
+
+# Load the trained churn prediction model
+model = joblib.load("RandomForestRegressor_Model_BEST.joblib")
+
+# Define a route for the home page
+@app.get('/')
+def home():
+    return "Welcome to the SuperKart Sales Prediction API"
+
+# Define an endpoint to predict churn for a single customer
+@app.post('/v1/product')
+def predict_churn():
+    # Get JSON data from the request
+    customer_data = request.get_json()
+    
+    # Extract relevant customer features from the input data
+    sample = {
+        'Product_Id': customer_data['Product_Id']
+        'Product_Weight': customer_data['Product_Weight'],
+        'Product_Sugar_Content': customer_data['Product_Sugar_Content'],
+        'Product_Allocated_Area': customer_data['Product_Allocated_Area'],
+        'Product_Type': customer_data['Product_Type'],
+        'Product_MRP': customer_data['Product_MRP'],
+        'Store_Id': customer_data['Store_Id'],
+        'Store_Establishment_Year': customer_data['Store_Establishment_Year'],
+        'Store_Size': customer_data['Store_Size'],
+        'Store_Location_City_Type': customer_data['Store_Location_City_Type'],
+        'Store_Type': customer_data['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).tolist()[0]
+    
+    # Return the prediction as a JSON response
+    return jsonify({'Prediction': prediction})
+
+
+# Run the Flask app in debug mode
+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==2.28.1
+uvicorn[standard]
+streamlit==1.43.2