import gradio as gr
from transcribe import transcribe

def main(audio_file, number_of_speakers):
  # Audio to Text Converter
  text_data = transcribe(audio_file, number_of_speakers)
  print(text_data)
  title = "ss"
  short_summary = "dsa"
  sentiment_analysis = "gyn"
  quality = "dsdww"
  detailed_summary = "jbjbjbjs"
  return title, short_summary, sentiment_analysis, quality, detailed_summary

# UI Interface on the Hugging Face Page
with gr.Blocks() as demo:
  with gr.Box():
    with gr.Row():
      with gr.Column():
        audio_file = gr.File(label="Upload a Audio file (.wav)", file_count=1)
        number_of_speakers = gr.Number(label="Number of Speakers", value=2)
        with gr.Row():
          btn_clear = gr.ClearButton(value="Clear", components=[audio_file, number_of_speakers])
          btn_submit = gr.Button(value="Submit")
      with gr.Column():
        title = gr.Textbox(label="Title", placeholder="Title for Conversation")
        short_summary = gr.Textbox(label="Short Summary", placeholder="Short Summary for Conversation")
        sentiment_analysis = gr.Textbox(label="Sentiment Analysis", placeholder="Sentiment Analysis for Conversation")
        quality = gr.Textbox(label="Quality of Conversation", placeholder="Quality of Conversation")
        detailed_summary = gr.Textbox(label="Detailed Summary", placeholder="Detailed Summary for Conversation")
      btn_submit.click(fn=main, inputs=[audio_file, number_of_speakers], outputs=[title, short_summary, sentiment_analysis, quality, detailed_summary])
    gr.Markdown("## Examples")
    gr.Examples( 
      examples=[
        ["./examples/sample4.wav", 2],
      ],
      inputs=[audio_file, number_of_speakers],
      outputs=[title, short_summary, sentiment_analysis, quality, detailed_summary],
      fn=main,
    )
  gr.Markdown(
    """
    See [github.com/facebookresearch/audiocraft](https://github.com/facebookresearch/audiocraft)
    for more details.
    """
  )

demo.launch()