app.py

import gradio as gr
from ui.dataset_input import create_dataset_input, load_example_dataset
from ui.analysis_screen import process_analysis_request

def create_app():
    """
    Create a streamlined Gradio app for dataset input and Bag of Words analysis. 
    
    Returns:
        gr.Blocks: The Gradio application
    """
    with gr.Blocks(title="LLM Response Comparator") as app:
        # Application state to share data between tabs
        dataset_state = gr.State({})
        analysis_results_state = gr.State({})
        
        # Dataset Input Tab
        with gr.Tab("Dataset Input"):
            dataset_inputs, example_dropdown, load_example_btn, create_btn, prompt, response1, model1, response2, model2 = create_dataset_input()
            
            # Load example dataset
            load_example_btn.click(
                fn=load_example_dataset,
                inputs=[example_dropdown],
                outputs=[dataset_inputs]
            )

            # Save dataset to state
            create_btn.click(
                fn=lambda p, r1, m1, r2, m2: {
                    "entries": [
                        {"prompt": p, "response": r1, "model": m1},
                        {"prompt": p, "response": r2, "model": m2}
                    ]
                },
                inputs=[prompt, response1, model1, response2, model2],
                outputs=[dataset_state]
            )
        
        # Analysis Tab
        with gr.Tab("Analysis"):
            analysis_options, analysis_params, run_analysis_btn, analysis_output = process_analysis_request()
            
            # Run analysis
            run_analysis_btn.click(
                fn=process_analysis_request,
                inputs=[dataset_state, analysis_options],
                outputs=[analysis_results_state, analysis_output]
            )
    
    return app

if __name__ == "__main__":
    app = create_app()
    app.launch()