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Dockerfile

@@ -1,20 +1,16 @@
-FROM python:3.13.5-slim
+FROM python:3.9-slim
 
+# Set the working directory inside the container
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
+# Copy all files from the current directory to the container's working directory
+COPY . .
 
-COPY requirements.txt ./
-COPY src/ ./src/
+# Install dependencies from the requirements file without using cache to reduce image size
+RUN pip install --no-cache-dir -r requirements.txt
 
-RUN pip3 install -r requirements.txt
-
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+# 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"]

app.py

@@ -0,0 +1,56 @@
+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+app = Flask("Telecom Customer Churn Predictor")
+model = joblib.load("churn_prediction_model_v1_0.joblib")
+
+@app.get('/')
+def home():
+  return "Welcome to the Telecom Customer Churn Prediction API"
+
+@app.post('/customer/v1')
+def predict_churn():
+  cutomer_data = request.get_json()
+
+  sample = {
+       'SeniorCitizen': customer_data['SeniorCitizen'],
+        'Partner': customer_data['Partner'],
+        'Dependents': customer_data['Dependents'],
+        'tenure': customer_data['tenure'],
+        'PhoneService': customer_data['PhoneService'],
+        'InternetService': customer_data['InternetService'],
+        'Contract': customer_data['Contract'],
+        'PaymentMethod': customer_data['PaymentMethod'],
+        'MonthlyCharges': customer_data['MonthlyCharges'],
+        'TotalCharges': customer_data['TotalCharges']
+
+  }
+input_data = pd.DataFrame([sample])
+
+prediction = model.predict(input_data).tolist()[0]
+prediction_label = "churn" if prediction == 1 else "not churn"
+return jsonify({'prediction': predxition_label})
+
+@app.post('/v1/customerbatch')
+def predict_churn_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 = [
+        '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))
+
+    return output_dict
+
+  if __name__ == '__main__':
+    app.run(debug=True)

requirements.txt

@@ -1,3 +1,10 @@
-altair
-pandas
-streamlit
+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]