requirement.txt

Flask==2.2.2
pandas==1.5.3
scikit-learn==1.2.2
joblib==1.2.0
gunicorn==20.1.0

app.py

@@ -0,0 +1,57 @@
+import joblib
+import pandas as pd
+from flask import Flask, request, jsonify
+
+app = Flask(__name__)
+
+# Load the serialized model and its components
+try:
+    model = joblib.load('extraalearn_best_model.joblib')
+except FileNotFoundError:
+    print("Error: 'extraalearn_best_model.joblib' not found. Ensure it's in the same directory.")
+    model = None
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    """
+    Predicts lead conversion based on input data.
+    Input data should be a JSON object with lead features.
+    """
+    if not model:
+        return jsonify({'error': 'Model not loaded. Check server logs.'}), 500
+
+    try:
+        # Get the JSON data from the request
+        data = request.get_json(force=True)
+        
+        # Convert the dictionary to a DataFrame. The feature names must match the training data.
+        input_df = pd.DataFrame([data])
+        
+        # Ensure the columns are in the correct order for the pipeline
+        required_columns = ['age', 'current_occupation', 'first_interaction', 
+                            'profile_completed', 'website_visits', 'time_spent_on_website', 
+                            'page_views_per_visit', 'last_activity', 'print_media_type1', 
+                            'print_media_type2', 'digital_media', 'educational_channels', 
+                            'referral']
+        input_df = input_df[required_columns]
+
+        # Make prediction
+        prediction = model.predict(input_df)[0]
+        prediction_proba = model.predict_proba(input_df)[0].tolist()
+
+        # Return the result
+        result = {
+            'prediction': int(prediction),
+            'prediction_label': 'Converted' if prediction == 1 else 'Not Converted',
+            'probabilities': {
+                'Not Converted': prediction_proba[0],
+                'Converted': prediction_proba[1]
+            }
+        }
+        return jsonify(result)
+
+    except Exception as e:
+        return jsonify({'error': str(e)}), 400
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=5000)