import os

import gradio as gr
import torch
from huggingface_hub import snapshot_download
from nemo.collections.asr.models import ASRModel
from omegaconf import OmegaConf

device = "cuda" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32


def load_model(model_id: str):
    model_dir = snapshot_download(model_id)
    model_ckpt_path = os.path.join(model_dir, "model.nemo")
    asr_model = ASRModel.restore_from(model_ckpt_path)
    asr_model.eval()
    asr_model = asr_model.to(device)

    return asr_model


OmegaConf.register_new_resolver("load_model", load_model)

models_config = OmegaConf.to_object(OmegaConf.load("configs/models.yaml"))


def automatic_speech_recognition(model_id: str, audio_file: str):
    model = models_config[model_id]["model"]
    text = model.transcribe(audio_file)
    return text


demo = gr.Blocks(
    title="康統語音辨識系統",
)

with demo:
    default_model_id = list(models_config.keys())[0]
    model_drop_down = gr.Dropdown(
        models_config.keys(),
        value=default_model_id,
        label="模型",
    )

    gr.Markdown(
        """
        # 康統語音辨識系統
        """
    )
    gr.Interface(
        automatic_speech_recognition,
        inputs=[
            model_drop_down,
            gr.Audio(
                label="上傳或錄音",
                type="filepath",
                waveform_options=gr.WaveformOptions(
                    sample_rate=16000,
                ),
            ),
        ],
        outputs=[
            gr.Text(interactive=False, label="辨識結果"),
        ],
        allow_flagging="auto",
    )

demo.launch()