Create app.py

app.py

@@ -0,0 +1,52 @@
+# Import necessary libraries
+import numpy as np
+import joblib  # For loading the serialized model
+import pandas as pd  # For data manipulation
+from flask import Flask, request, jsonify  # For creating the Flask API
+
+
+# Initialize Flask app with a name
+learn_api = Flask("ExtraaLearn_Predictor")
+
+# Load the trained prediction model
+model = joblib.load('extralearn_model_v1.joblib') 
+
+# Define a route for the home page
+@learn_api.get('/')
+def home():
+    return "Welcome to the Lead Conversion-Prediction System"
+
+# Define an endpoint to predict conversion of a single customer
+@learn_api.post('/v1/predict')
+def predict_sales():    #Was 'prediction', & then 'enrollment'
+    # Get JSON data from the request
+    data = request.get_json()
+
+    # Extract relevant customer 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
+    input_data = pd.DataFrame([sample])
+
+    # Make a prediction using the trained model
+    prediction = model.predict(input_data).tolist()[0]
+
+    # Return the prediction as a JSON response
+    return jsonify({'Lead': prediction})
+
+# Run the Flask app in debug mode
+if __name__ == '__main__':
+    learn_api.run(debug=True)