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app.py

@@ -1,24 +1,29 @@
 import joblib
-import pandas
-from flask import Flask, jsonify, request
+import pandas as pd
+from flask import Flask, request, jsonify
 
+# Initialize Flask app with a name
 app = Flask("Telecom Customer Churn Predictor")
+
+# Load the trained churn prediction model
 model = joblib.load("churn_prediction_model_v1_0.joblib")
 
+# Define a route for the home page
 @app.get('/')
 def home():
-  return "Welcome to the Telecom churn model"
+    return "Welcome to the Telecom Customer Churn Prediction API"
 
-#Churn for a single customer
+# 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()
 
-  customer_data = request.get_json()
-
-  sample = {
-      'SeniorCitizen':customer_data['SeniorCitizen'],
-      'Partner':customer_data['Partner'],
-      'Dependents': customer_data['Dependents'],
+    # Extract relevant customer features from the input data
+    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'],
@@ -26,16 +31,41 @@ def predict_churn():
         'PaymentMethod': customer_data['PaymentMethod'],
         'MonthlyCharges': customer_data['MonthlyCharges'],
         'TotalCharges': customer_data['TotalCharges']
-  }
+    }
 
-  input_data = pd.DataFrame([sample])
+    # Convert the extracted data into a DataFrame
+    input_data = pd.DataFrame([sample])
 
-  predicted_val = model.predict(input_data)[0]
-  prediction_label = "churn" if predicted_val == 1 else "not churn"
+    # Make a churn prediction using the trained model
+    prediction = model.predict(input_data).tolist()[0]
 
-  return jsonify({'Prediction':prediction_label})
+    # Map prediction result to a human-readable label
+    prediction_label = "churn" if prediction == 1 else "not churn"
 
-#run the flask app in debug mode
-if __name__ == '__main__':
-  app.run(debug=True)
+    # Return the prediction as a JSON response
+    return jsonify({'Prediction': prediction_label})
+
+# Define an endpoint to predict churn for a batch of customers
+@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
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    app.run(debug=True)