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@@ -13,4 +13,4 @@ RUN pip install --no-cache-dir --upgrade -r requirements.txt
 # - `-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:SuperKart_predictor_api"]
+CMD ["gunicorn", "-w", "4", "-b", "0.0.0.0:7860", "app:cust_churn_predictor_api"]

app.py

@@ -1,52 +1,53 @@
-
 import joblib
 import pandas as pd
 from flask import Flask, request, jsonify
 
 # Initialize Flask app with a name
-SuperKart_predictor_api = Flask ("SuperKart Predictor")
+cust_churn_predictor_api = Flask ("Customer Churn Predictor Week1")
 
 # Load the trained churn prediction model
-model = joblib.load ("SuperKart_prediction_model_v1_0.joblib")
+model = joblib.load ("churn_prediction_model_v2_0.joblib")
 
 # Define a route for the home page
-@SuperKart_predictor_api.get ('/')
+@cust_churn_predictor_api.get ('/')
 def home ():
-    return "Welcome to the Super Kart Prediction!"
+    return "Welcome to the Customer Churn Prediction Week1 API!"
 
 # Define an endpoint to predict churn for a single customer
-@SuperKart_predictor_api.post ('/v1/SuperKartSales')
-def predict_sales ():
+@cust_churn_predictor_api.post ('/v1/customer')
+def predict_churn ():
     # Get JSON data from the request
-    sales_data = request.get_json ()
+    customer_data = request.get_json ()
 
     # Extract relevant customer features from the input data
-    data_info = {
-        'Product_Weight'            : sales_data ['Product_Weight'],
-        'Product_Sugar_Content'     : sales_data ['TotalCharges'],
-        'Product_Allocated_Area'    : sales_data ['Product_Allocated_Area'],
-        'Product_Type'              : sales_data ['Product_Type'],
-        'Product_MRP'               : sales_data ['Product_MRP'],
-        'Store_Id'                  : sales_data ['Store_Id'],
-        'Store_Establishment_Year'  : sales_data ['Store_Establishment_Year'],
-        'Store_Size'                : sales_data ['Store_Size'],
-        'Store_Location_City_Type'  : sales_data ['Store_Location_City_Type'],
-        'Store_Type'                : sales_data ['Store_Type']
+    sample = {
+        'customerID'      : customer_data ['customerID'],
+        'SeniorCitizen'   : customer_data ['SeniorCitizen'],
+        'tenure'          : customer_data ['tenure'],
+        'MonthlyCharges'  : customer_data ['MonthlyCharges'],
+        'TotalCharges'    : customer_data ['TotalCharges'],
+        'Partner'         : customer_data ['Partner'],
+        'Dependents'      : customer_data ['Dependents'],
+        'PhoneService'    : customer_data ['PhoneService'],
+        'InternetService' : customer_data ['InternetService'],
+        'Contract'        : customer_data ['Contract'],
+        'PaymentMethod'   : customer_data ['PaymentMethod']
     }
 
     # Convert the extracted data into a DataFrame
-    input_data = pd.DataFrame ([data_info])
+    input_data = pd.DataFrame ([sample])
 
     # Make a churn prediction using the trained model
-    predicted_sales = model.predict (input_data).tolist ()[0]
+    prediction = model.predict (input_data).tolist ()[0]
 
-    # Return the prediction as a JSON response
-    return jsonify ({'Sales Prediction': predicted_sales})
+    # 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 ({'Prediction': prediction_label})
 
-# Define an endpoint to predict sales for a batch of data
-# here we assume the data to conatain same columns as per the data provided for this project
-@SuperKart_predictor_api.post ('/v1/SuperKartBatchSales')
+# Define an endpoint to predict churn for a batch of customers
+@cust_churn_predictor_api.post ('/v1/customerbatch')
 def predict_churn_batch ():
     # Get the uploaded CSV file from the request
     file = request.files ['file']
@@ -54,27 +55,18 @@ def predict_churn_batch ():
     # Read the file into a DataFrame
     input_data = pd.read_csv (file)
 
-    # Handle Product_Id if present
-    if "Product_Id" in input_data.columns:
-        product_ids = input_data["Product_Id"].copy()
-        X = input_data.drop(columns=["Product_Id"])
-    else:
-        product_ids = None
-        X = input_data
-
-    # Make predictions
-    predictions = model.predict(X).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 ()
+    ]
 
-    # Prepare response
-    if product_ids is not None:
-        # Return mapping of Product_Id to predictions
-        output_dict = dict(zip(product_ids.tolist(), predictions))
-    else:
-        # If no Product_Id, just return index → predictions
-        output_dict = dict(zip(input_data.index.tolist(), predictions))
+    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__':
-    SuperKart_predictor_api.run (debug=True)
+    cust_churn_predictor_api.run (debug=True)