Create tests/test_model_finetuning

tests/test_model_finetuning

@@ -0,0 +1,28 @@
+import unittest
+from unittest.mock import MagicMock
+import pandas as pd
+import streamlit as st
+from your_module import fine_tune_model
+
+class TestModelFineTuning(unittest.TestCase):
+
+    @patch('streamlit.file_uploader')
+    def test_upload_and_fine_tune_model(self, mock_file_uploader):
+        # Mock the file upload and return a mock DataFrame
+        mock_file_uploader.return_value = MagicMock()
+        mock_file_uploader.return_value.read.return_value = b'col1,col2\nvalue1,value2\nvalue3,value4'
+        
+        # Test dataset upload and model fine-tuning
+        df = pd.read_csv(mock_file_uploader.return_value)
+        
+        self.assertEqual(df.shape[0], 2)  # Assert two rows in the mock CSV
+        self.assertIn('col1', df.columns)  # Check if 'col1' exists in columns
+        
+        # Simulate fine-tuning process
+        result = fine_tune_model(df)  # Assuming you have a fine-tune function
+        
+        # Check that the model fine-tuned successfully
+        self.assertTrue(result)  # Assuming result is True on success
+
+if __name__ == '__main__':
+    unittest.main()