import whisper
import gradio as gr
from pathlib import Path
from dotenv import load_dotenv
import os

transcription_value = ""


def transcribe_speech(filepath):
    if filepath is None:
        gr.Warning("No audio found, please retry.")

    model = whisper.load_model("base")
    result = model.transcribe(filepath, fp16=False)

    return result["text"]


def store_transcription(output):
    global transcription_value
    transcription_value = output
    return output


mic_transcribe = gr.Interface(
    fn=lambda x: store_transcription(transcribe_speech(x)),
    inputs=gr.Audio(sources=["microphone"], type="filepath"),
    outputs=gr.Textbox(label="Transcription")
)


test_interface = gr.Blocks()
with test_interface:
    gr.TabbedInterface(
        [mic_transcribe],
        ["Transcribe Microphone"]
    )

test_interface.launch(
    share=True,
    server_port=8000,
    #prevent_thread_lock=True
)

print(transcription_value)

#test_interface.close()