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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:churn_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
+sale_predictor_api = Flask("Store sale Predictor")
+
+
+# Define a route for the home page
+@sale_predictor_api.get('/')
+def home():
+    return "Welcome to the Sale Prediction API!"
+
+# Define an endpoint to predict sale for a single store
+@sale_predictor_api.post('/v1/store')
+def predict_sale():
+    # Get JSON data from the request
+    store_data = request.get_json()
+
+    # Extract relevant customer features from the input data
+    sample = {
+        'Product_Weight': store_data['Product_Weight'],
+        'Product_Sugar_Content': store_data['Product_Sugar_Content'],
+        'Product_Allocated_Area': store_data['Product_Allocated_Area'],
+        'Product_Type': store_data['Product_Type'],
+        'Product_MRP': store_data['Product_MRP'],
+        'Store_Establishment_Year': store_data['Store_Establishment_Year'],
+        'Store_Size': store_data['Store_Size'],
+        'Store_Location_City_Type': store_data['Store_Location_City_Type'],
+        'Store_Type': store_data['Store_Type']
+    }
+
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([sample])
+
+    # Make a sale prediction using the trained model
+    prediction = model.predict(input_data).tolist()[0]
+
+    # Map prediction result to a human-readable label
+    prediction_label = "sale" if prediction == 1 else "not sale"
+
+    # Return the prediction as a JSON response
+    return jsonify({'Prediction': prediction_label})
+
+# Define an endpoint to predict sale for a batch of customers
+@sale_predictor_api.post('/v1/storebatch')
+def predict_sale_batch():
+    # Get the uploaded CSV file from the request
+    file = request.files['file']
+
+    # Read the file into a DataFrame
+    input_data = pd.read_csv(file)
+
+    # Make predictions for the batch data and convert raw predictions into a readable format
+    predictions = [
+        'sale' if x == 1
+        else "Not sale"
+        for x in model.predict(input_data.drop("CustomerId",axis=1)).tolist()
+    ]
+
+    cust_id_list = input_data.CustomerId.values.tolist()
+    output_dict = dict(zip(cust_id_list, predictions))
+
+    return output_dict
+
+# 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