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@@ -1,4 +1,3 @@
-# Use the official slim Python 3.9 image as the base image
 FROM python:3.9-slim
 
 # Set the working directory inside the container
@@ -8,10 +7,10 @@ WORKDIR /app
 COPY . .
 
 # Install dependencies from the requirements file without using cache to reduce image size
-RUN pip install --no-cache-dir --upgrade -r requirements.txt
+RUN pip install --no-cache-dir --upgrade -r server_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 `server.py` contains the Flask instance named `app`)
-CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "python server.py & streamlit run app.py"]
+# - `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:server"]

server.py

@@ -9,16 +9,16 @@ app = Flask("Customer Churn Predictor")
 model = joblib.load("churn_prediction_model_v1_0.joblib")
 
 # Define a route for the home page
-# @app.get('/')
-# def home():
-    # return "Welcome to the Customer Churn Prediction API!"
+@app.get('/')
+def home():
+    return "Welcome to the Customer Churn Prediction API!"
 
 # Define an endpoint to predict churn for a single customer
 @app.post('/v1/customer')
 def predict_churn():
     # Get JSON data from the request
     customer_data = request.get_json()
-    
+
     # Extract relevant customer features from the input data
     sample = {
         'CreditScore': customer_data['CreditScore'],
@@ -31,7 +31,7 @@ def predict_churn():
         'IsActiveMember': customer_data['IsActiveMember'],
         'EstimatedSalary': customer_data['EstimatedSalary']
     }
-    
+
     # Convert the extracted data into a DataFrame
     input_data = pd.DataFrame([sample])
 
@@ -40,7 +40,7 @@ def predict_churn():
 
     # Map prediction result to a human-readable label
     prediction_label = "churn" if prediction == 1 else "not churn"
-    
+
     # Return the prediction as a JSON response
     return jsonify({'Churn expected?': prediction_label})
 
@@ -53,8 +53,12 @@ def predict_churn_batch():
     # Read the file into a DataFrame
     input_data = pd.read_csv(file)
 
-    # Make predictions for the batch data
-    predictions = ['Churn' if x == 1 else "Not Churn" for x in model.predict(input_data.drop("CustomerId",axis=1)).tolist()]
+    # Make predictions for the batch data and convert raw predictions into a readable format
+    predictions = [
+        'Churn' if x == 1
+        else "Not Churn"
+        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))
@@ -63,4 +67,4 @@ def predict_churn_batch():
 
 # Run the Flask app in debug mode
 if __name__ == '__main__':
-    app.run(debug=True)
+    app.run(debug=True)

server_requirements.txt

@@ -0,0 +1,11 @@
+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