import whisper
import gradio as gr


model = whisper.load_model('base')



def transcribe(inputs , timestamp):
    if inputs is None:
          raise gr.Error("No audio file submitted! Please upload or record an audio file before submitting your request.")
    output = ""
    result = model.transcribe(inputs)
    if timestamp == "Yes":
      for indx, segment in enumerate(result['segments']):
        output += str(datetime.timedelta (seconds=segment['start'])) +" "+ str(datetime.timedelta (seconds=segment['end'])) + "\n"
        output += segment['text'].strip() + '\n'
    else:
      output = result["text"]


    print(result)
    return  output




interface = gr.Interface(
    fn=transcribe,
    inputs=[gr.Audio(sources=["upload"],type="filepath"),
            gr.Radio(["Yes", "No"], label="Timestamp", info="Displays with timestamp if needed."),],
    outputs="text",
    title="Whisper Large V3: Transcribe Audio",
    description=(
        "Transcribe long-form microphone or audio inputs with the click of a button! Demo uses the OpenAI Whisper API"
    )
)

interface.launch()