import gradio as gr
import os
from constants import VOICE_METHODS, BARK_VOICES, EDGE_VOICES
import platform
from models.model import *
from tts.conversion import COQUI_LANGUAGES
import pytube
import os
import traceback
from pydub import AudioSegment
# from audio_enhance.functions import audio_enhance

def convert_yt_to_wav(url):
    if not url:
        return "Primero introduce el enlace del video", None
    
    try:
        print(f"Convirtiendo video {url}...")
        # Descargar el video utilizando pytube
        video = pytube.YouTube(url)
        stream = video.streams.filter(only_audio=True).first()
        video_output_folder = os.path.join(f"yt_videos")  # Ruta de destino de la carpeta
        audio_output_folder = 'audios'

        print("Downloading video")
        video_file_path = stream.download(output_path=video_output_folder)
        print(video_file_path)

        file_name = os.path.basename(video_file_path)
        
        audio_file_path = os.path.join(audio_output_folder, file_name.replace('.mp4','.wav'))
        # convert mp4 to wav
        print("Converting to wav")
        sound = AudioSegment.from_file(video_file_path,format="mp4")
        sound.export(audio_file_path, format="wav")
        
        if os.path.exists(video_file_path):
            os.remove(video_file_path)
            
        return "Success", audio_file_path
    except ConnectionResetError as cre:
        return "Se ha perdido la conexión, recarga o reintentalo nuevamente más tarde.", None
    except Exception as e:
        return str(e), None


title_markdown = ("""
<h1 align="center"> 🌊💕🎶 滔滔AI，AI歌手模型开源社区 </h2>
<h2 align="center"> 🌟 完全开源、完全免费、共建共享！全网AI歌手任您选择！ </h2>
""")


with gr.Blocks() as app:
    
    gr.Markdown(title_markdown)

    with gr.Tab("模型搜索及上传"):
        gr.HTML("<h3>1. 搜索AI歌手模型</h3>")
        search_name = gr.Textbox(placeholder="孙燕姿", label="请填写模型名称进行搜索", show_label=True)
         # Salida
        with gr.Row():
            sarch_output = gr.Markdown(label="搜索结果")
            
        btn_search_model = gr.Button(value="开始搜索吧💖", variant="primary")
        btn_search_model.click(fn=search_model, inputs=[search_name], outputs=[sarch_output])
        
        gr.HTML("<h3>2. 上传AI歌手模型至社区 （上传完成后您立即可以搜索到您上传的模型）</h3>")
        post_name = gr.Textbox(placeholder="滔滔歌姬", label="请填写模型名称", show_label=True)
        post_model_url = gr.Textbox(placeholder="https://huggingface.co/kevinwang676/RVC-models/resolve/main/talktalkgirl.zip", label="模型链接", info="1.推荐使用Hugging Face存放模型 2.复制Hugging Face模型链接后，需要将链接中的blob四个字母替换成resolve，使模型可以通过链接直接下载", show_label=True)
        post_creator = gr.Textbox(placeholder="滔滔AI", label="模型贡献者", info="可填写您的昵称或任何有趣的ID", show_label=True)
        post_version = gr.Dropdown(choices=["RVC v1", "RVC v2"], value="RVC v2", label="RVC模型版本", show_label=True)
        
         # Salida
        with gr.Row():
            post_output = gr.Markdown(label="模型上传状态")
            
        btn_post_model = gr.Button(value="开始上传吧💕", variant="primary")
        btn_post_model.click(fn=post_model, inputs=[post_name, post_model_url, post_version, post_creator], outputs=[post_output])
                
        #     with gr.Column():
        #         model_voice_path07 = gr.Dropdown(
        #             label=i18n("RVC Model:"),
        #             choices=sorted(names),
        #             value=default_weight,
        #         )
        #         best_match_index_path1, _ = match_index(
        #             model_voice_path07.value
        #         )

        #         file_index2_07 = gr.Dropdown(
        #             label=i18n("Select the .index file:"),
        #             choices=get_indexes(),
        #             value=best_match_index_path1,
        #             interactive=True,
        #             allow_custom_value=True,
        #         )
        # with gr.Row():
        #     refresh_button_ = gr.Button(i18n("Refresh"), variant="primary")
        #     refresh_button_.click(
        #         fn=change_choices2,
        #         inputs=[],
        #         outputs=[model_voice_path07, file_index2_07],
        #     )
        # with gr.Row():
        #     original_ttsvoice = gr.Audio(label=i18n("Audio TTS:"))
        #     ttsvoice = gr.Audio(label=i18n("Audio RVC:"))

        # with gr.Row():
        #     button_test = gr.Button(i18n("Convert"), variant="primary")

        # button_test.click(
        #     tts.use_tts,
        #     inputs=[
        #         text_test,
        #         tts_test,
        #         model_voice_path07,
        #         file_index2_07,
        #         # transpose_test,
        #         vc_transform0,
        #         f0method8,
        #         index_rate1,
        #         crepe_hop_length,
        #         f0_autotune,
        #         ttsmethod_test,
        #     ],
        #     outputs=[ttsvoice, original_ttsvoice],
        # )

    
    
    app.queue(max_size=40, api_open=False)
    app.launch(max_threads=400, show_error=True)