import gradio as gr
from processing import process_input
from visualization import update_visibility_and_charts

def create_gradio_interface():
    with gr.Blocks() as demo:
        gr.Markdown("# Conversation Analysis")
        
        with gr.Row():
            input_file = gr.File(label="Upload File")
            submit_button = gr.Button("Analyze")
        
        status_output = gr.Textbox(label="Status")
        exec_time_output = gr.Textbox(label="Execution Time")
        lang_output = gr.Textbox(label="Detected Language")
        transcription_output = gr.Textbox(label="Transcription", lines=10)
        
        speaker_outputs = []
        for speaker in range(3):
            with gr.Group(visible=True, label=f"Speaker {speaker + 1}"):
                for _ in range(3):  # 3 analysis types
                    speaker_outputs.extend([
                        gr.Plot(visible=False),
                        gr.Plot(visible=False),
                        gr.Textbox(visible=False)
                    ])
        
        def process_and_update(file):
            status, exec_time, lang, attachments, bigfive, personalities, transcription = process_input(file)
            return update_visibility_and_charts(status, exec_time, lang, transcription, attachments, bigfive, personalities)
        
        submit_button.click(
            process_and_update,
            inputs=[input_file],
            outputs=[status_output, exec_time_output, lang_output, transcription_output] + speaker_outputs
        )

    return demo

# The main execution of the script
if __name__ == "__main__":
    demo = create_gradio_interface()
    demo.launch()