import gradio as gr

def run_evaluation(dataset_id, methodology):
    return f'Running evaluation for {dataset_id} with {methodology}'

    if methodology == 'A':
        run_a(dataset_id)
    elif methodology == 'B':
        run_b(dataset_id)
    elif methodology == 'C':
        run_c(dataset_id)
    
def schema_uploaded_file(file):
    file_paths = [file.name for file in file]
    return file_paths

demo = gr.Blocks(theme=gr.themes.Soft())

with demo:
    gr.Markdown("# BiasAware: Dataset Bias Detection")
    
    with gr.Row():
        with gr.Column(scale=1):
            gr.Markdown("Select a dataset to analyze")

            dataset_id = gr.Text(label="Dataset")
            gr.Examples(
                examples=["imdb", "amazon_reviews_multi", "tweet_eval"],
                fn=run_evaluation,
                inputs=[dataset_id]
            )

            methodology = gr.Dropdown(["Term Identity Diversity Analysis", "Textual Gender Label Evaluation", "GenBit"], label="Methodology")

            button = gr.Button("Run Evaluation")

        with gr.Column(scale=4):
            gr.Markdown("### Results")

            with gr.Box():
                methodology_title = gr.Markdown("### Identity Term Sampling")
                methodology_description = gr.Markdown("lorem ipsum")
            
            methodology_test_description = gr.Markdown("lorem ipsum")
            outputs = gr.Markdown()
            gr.Error("No results to display")
        

    methodology.change(
        fn=lambda x: (f'### {x}', "lorem ipseum", "lorem ipsum"),
        inputs=[methodology],
        outputs=[methodology_title, methodology_description, methodology_test_description]
    )

    button.click(
        fn=run_evaluation,
        inputs=[dataset_id, methodology],
        outputs=[outputs]
    )

     # For user dataset upload
    gr.Interface(schema_uploaded_file, "file", "text")


demo.launch()