import json
import requests
import os
import traceback
import gradio as gr # Imports the Gradio library, which is used to create user interfaces for machine learning models.

HF_TOKEN = os.environ.get("HF_TOKEN", None)
API_URL = "https://api-inference.huggingface.co/models/"
    
def s2t(audio, model_name):
    with open(audio, "rb") as f:
        data = f.read()
    try:
        url = API_URL + model_name
        headers = {"Authorization": f"Bearer {HF_TOKEN}"}
        response = requests.request("POST", url, headers=headers, data=data)
        text = json.loads(response.content.decode("utf-8"))
        text = text['text']
    except:
        text = f"Transcription failed with error：\n{traceback.format_exc()}"

    yield text

with gr.Blocks() as demo:
    with gr.Column():
        voice = gr.Audio(sources=["microphone"], type="filepath", label="Voice")
        audio = gr.Audio(sources=["upload"], type="filepath", label="Audio file")
        model_name = gr.Dropdown(
            label="Models:",
            choices=[
                    "openai/whisper-large-v3",
                    "openai/whisper-large-v2",
                    "openai/whisper-large",
                    "openai/whisper-medium",
                    "openai/whisper-small",
                    "openai/whisper-base",
                    "openai/whisper-tiny",
                ],
            value="openai/whisper-large-v3",
        )
    with gr.Column():
        output = gr.Textbox(label="Transcription results")
            
    voice.stop_recording(s2t, inputs=[voice, model_name], outputs=output)
    audio.upload(s2t, inputs=[audio, model_name], outputs=output)
    
demo.queue().launch(max_threads=4)