app.py

import gradio as gr
from helper_fns import process_files, get_summarization_method
from summarizer import summarize_file
from helper_fns import text_to_speech


def generate_output(method,  file):

    summary_text = summarize_file(method, file)
    audio_summary = text_to_speech(summary_text)

    return summary_text, audio_summary


with gr.Blocks() as demo:
    with gr.Row():
        with gr.Column():
            files = gr.UploadButton(
                            label='Upload Files For Summarization',
                            file_count='multiple',
                            file_types=["pdf", "docx", "pptx"]
                            )
            summarization_method_radio = gr.Radio(choices=['map_reduce', 'stuff', 'refine'],
                                                  value='map_reduce',
                                                  label='Select Summarization Method',
                                                  interactive=False)          

            generate_summaries_button = gr.Button(value='Generate Summaries',
                                       interactive=False,
                                       elem_id='summary_button')
            

            
            files.upload(process_files, None, outputs=[generate_summaries_button,
                                                        summarization_method_radio])
            summarization_method_radio.input(fn = get_summarization_method,
                                            inputs=summarization_method_radio)

        with gr.Column():
            summary_text = gr.Textbox(label='Summarized Text: ',
                                      interactive=False)
            summary_audio = gr.Audio(label='Summarized audio',
                       sources='upload',
                       type='filepath',
                       interactive=False,
                       autoplay=False)

    generate_summaries_button.click(
        fn = generate_output,
        inputs=[summarization_method_radio, files],
        outputs=[summary_text, audio_file]
    )



demo.launch()