tests/test_model_finetuning.py

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()