Some files appropriated

.ipynb_checkpoints/app-checkpoint.py

@@ -128,20 +128,20 @@ def predict():
 
         data = request.json
 
-        # Extract train, test, and test_size with defaults
+        # Extract train, test, and train_size with defaults
         train = data.get('train', False)
         test = data.get('test', False)
-        test_size = float(data.get('test_size', 0.1))
+        train_size = float(data.get('train_size', 0.1))
 
         device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
         # Handle training mode
         if train in (True, 'true', 'True'):
-            start_pipelines(test_size=test_size)
+            start_pipelines(train_size=train_size)
             logger.info("PIPELINES FINISHED SUCCESSFULLY")
             return jsonify({
                 'message': 'Training pipelines executed successfully',
-                'test_size': test_size
+                'train_size': train_size
             }), 200
 
         # Handle testing/inference mode
@@ -277,5 +277,3 @@ def predict():
     except Exception as e:
         return jsonify({'error': str(e)}), 500
 
-if __name__ == '__main__':
-    app.run(debug=True, host='0.0.0.0', port=5000)

app.py

@@ -128,20 +128,20 @@ def predict():
 
         data = request.json
 
-        # Extract train, test, and test_size with defaults
+        # Extract train, test, and train_size with defaults
         train = data.get('train', False)
         test = data.get('test', False)
-        test_size = float(data.get('train_size', 0.1))
+        train_size = float(data.get('train_size', 0.1))
 
         device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
         # Handle training mode
         if train in (True, 'true', 'True'):
-            start_pipelines(test_size=test_size)
+            start_pipelines(train_size=train_size)
             logger.info("PIPELINES FINISHED SUCCESSFULLY")
             return jsonify({
                 'message': 'Training pipelines executed successfully',
-                'test_size': test_size
+                'train_size': train_size
             }), 200
 
         # Handle testing/inference mode