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app.py

@@ -6,49 +6,58 @@ import pandas as pd  # For data manipulation
 from flask import Flask, request, jsonify  # For creating the Flask API
 
 # Initialize Flask app with a name
-extraalearn_api = Flask("ExtraaLearn")
+extraalearn_api = Flask("ExtraaLearn Leads Predictor")
 
-# Load the trained churn prediction model
+# Load the trained machine learning model extraalearn_model.joblib
 model = joblib.load("extraalearn_model.joblib")
 
 # Define a route for the home page
 @extraalearn_api.get('/')
 def home():
-    return "Welcome to the ExtraaLearn System"
+    """
+    This function handles GET requests to the root URL ('/') of the API.
+    It returns a simple welcome message.
+    """
+    return "Welcome to the ExtraaLearn Prediction API!"
 
-# Define an endpoint to predict churn for a single lead
+# Define an endpoint to predict a single lead
 @extraalearn_api.post('/v1/predict')
 def predict_sales():
-    # Get JSON data from the request
+    """
+    This function handles POST requests to the '/v1/predict' endpoint.
+    It expects a JSON payload containing product details and returns
+    the predicted lead's outcome status as a JSON response.
+    """
+    # Get the JSON data from the request body
     data = request.get_json()
 
     # Extract relevant lead features from the input data
     sample = {
-    'Age': data['age'],
-    'Current_Occupation': data['current_occupation'],
-    'First_Interaction': data['first_interaction'],
-    'Profile_Completed': data['profile_completed'],
-    'Website_Visits': data['website_visits'],
-    'Time_Spent_on_Website': data['time_spent_on_website'],
-    'Page_Views_Per_Visit': data['page_views_per_visit'],
-    'Last_Activity': data['last_activity'],
-    'Print_Media_Type1': data['print_media_type1'],
-    'Print_Media_Type2': data['print_media_type2'],
-    'Digital_Media': data['digital_media'],
-    'Educational_Channels': data['educational_channels'],
-    'Referral': data['referral']
-}
-
-    # Convert the extracted data into a DataFrame
+      'Age': data['age'],
+      'Current_Occupation': data['current_occupation'],
+      'First_Interaction': data['first_interaction'],
+      'Profile_Completed': data['profile_completed'],
+      'Website_Visits': data['website_visits'],
+      'Time_Spent_on_Website': data['time_spent_on_website'],
+      'Page_Views_Per_Visit': data['page_views_per_visit'],
+      'Last_Activity': data['last_activity'],
+      'Print_Media_Type1': data['print_media_type1'],
+      'Print_Media_Type2': data['print_media_type2'],
+      'Digital_Media': data['digital_media'],
+      'Educational_Channels': data['educational_channels'],
+      'Referral': data['referral']
+    }
+
+    # Convert the extracted data into a Pandas DataFrame
     input_data = pd.DataFrame([sample])
 
-    # Make a churn prediction using the trained model
+    # Make prediction using the trained model
     prediction = model.predict(input_data)[0]
 
     # Return the prediction as a JSON response
     return jsonify({'Sales': prediction})
 
 
-# Run the Flask app in debug mode
+# Run the Flask app in debug mode if this script is executed directly
 if __name__ == '__main__':
     extraalearn_api.run(debug=True)