import gradio as gr
import hashlib
import tempfile
import requests
from TTS.utils.manage import ModelManager
from TTS.utils.synthesizer import Synthesizer
def fx(x:str):
    hash=hashlib.md5()
    hash.update(x.encode(encoding='utf-8'))
    return hash.hexdigest()
    
manager = ModelManager()
model_path, config_path, model_item = manager.download_model("tts_models/zh-CN/baker/tacotron2-DDC-GST")
synthesizer = Synthesizer(
    model_path, config_path, None, None, None,
)


def inference(text: str):
    wavs = synthesizer.tts(text)
    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
        synthesizer.save_wav(wavs, fp)
        return fp.name

headers= {"Content-Type": "application/json"}
url="https://m-formatter.azurewebsites.net/api/v2"
def fx_m(s:str):
    data={'code':s,'resultType':'text'}
    respose=requests.post(url, json=data, headers)
    return respose.text
        
demo=gr.Blocks()
with demo:
    with gr.Tabs():
        with gr.TabItem("测试1"):
            with gr.Column():
                text_input=gr.Textbox(placeholder='请输入测试字符串',label="请输入需要MD5加密的测试内容")
                text_output=gr.Textbox(label="输出",visible=False)
                text_input.change(fn=lambda visible: gr.update(visible=True), inputs=text_input, outputs=text_output)
                bb_button=gr.Button("运行")
                bb_button.click(fx, inputs=text_input, outputs=text_output,api_name='md5')
            with gr.Column():
                gr.Markdown("# TTS文本字符串转语音合成训练")
                TTS_input=gr.Textbox(label="输入文本",default="你好吗？我很好。") 
                TTS_button=gr.Button("合成")
                TTS_button.click(inference, inputs=TTS_input, outputs=gr.Audio(label="输出合成结果"),api_name='tts')
        with gr.TabItem("M-Formatter"):
            gr.Markdown("# PowerQuery M语言脚本格式化测试")
            M_input=gr.Textbox(label="请填写需要格式化的M脚本",default="let a=1,b=2 in a+b",lines=28)
            M_output=gr.Textbox(label="格式化结果",lines=50) 
            M_button=gr.Button("开始格式化>>")
            M_button.click(fx_m, inputs=M_input, outputs=M_output,api_name='M')
demo.launch()