import gradio as gr import hashlib import tempfile import requests import pandas as pd from datatable import (dt, f,fread, by, ifelse, update, sort,count, min, max, mean, sum, rowsum,rowmean,rowcount,shift,rbind,cbind,union) 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 def fx_m(s:str): headers= {"Content-Type": "application/json"} url="https://m-formatter.azurewebsites.net/api/v2" data={'code':s,'resultType':'text'} respose=requests.post(url,json=data,headers=headers) ms=respose.json() return ms['result'] def fx_dax(s:str): url="https://www.daxformatter.com/" data = {"embed":"1","l":"short","fx":s} ct=requests.post(url = url,data = data) html=ct.text s1=html.split('
')[1] s2='
'+s1.split('万剑归宗' return s2 def fx_datatable(s:str): a=exec(s) return {k: v for k, v in locals().items() if isinstance(v,pd.DataFrame)} def fx_dd(tk:str,s:str): headers= {"Content-Type": "application/json"} url="https://oapi.dingtalk.com/robot/send?access_token="+tk data={'msgtype':'text','text':{'title': '吹牛逼',"content": s}, 'at': {'atMobiles': [], 'isAtAll': True}} response=requests.post(url,json=data,headers=headers) return response.text def dd_ocr(tk,sl,dt): headers= {"Content-Type": "application/json"} url="https://oapi.dingtalk.com/topapi/ocr/structured/recognize?access_token="+tk dc={"身份证":"idcard","增值税发票":"invoice","营业执照":"blicense","银行卡":"bank_card","车牌":"car_no","机动车发票":"car_invoice","驾驶证":"driving_license","行驶证":"vehicle_license","火车票":"train_ticket","定额发票":"quota_invoice","出租车发票":"taxi_ticket","机票行程单":"air_itinerary","审批表单":"approval_table","花名册":"roster"} data={"image_url":sl,"type":dc[dt]} response=requests.post(url,json=data,headers=headers) return response.json() def fx_dt(dc,x,y): DT=fread(dc) gr.JSON(DT.names) D_table=DT[:,sum(f[int(y-1)]),by(f[int(x-1)])] return D_table.to_pandas() demo=gr.Blocks(css="#jsc:hover{background-color: red;}") 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") 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",lines=18) 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') with gr.TabItem("DAX-Formatter"): gr.Markdown("# DAX表达式格式化测试") with gr.Row(): DAX_input=gr.Textbox(label="请填写需要格式化的DAX表达式",lines=28) DAX_button=gr.Button("格式化>>") DAX_output=gr.HTML(label="DAX表达式格式化结果") DAX_button.click(fx_dax, inputs=DAX_input, outputs=DAX_output,api_name='DAX') with gr.TabItem("Python-Execute"): gr.Markdown("# Python脚本测试") d_input=gr.Textbox(label="请填写需要datatable库处理的脚本",lines=18) d_output=gr.JSON(label="输出>") d_button=gr.Button("开始编译>>") d_button.click(fx_datatable, inputs=d_input, outputs=d_output,api_name='pyexe') with gr.TabItem("钉钉群消息推送"): gr.Markdown("# 推送测试") dd_input=[gr.Textbox(label="请填写机器人token"),gr.Textbox(label="请填写需要推送的信息",lines=10)] dd_output=gr.Textbox(label="推送提示") dd_button=gr.Button("提交") dd_button.click(fx_dd, inputs=dd_input, outputs=dd_output,api_name='dingding_robot') with gr.TabItem("钉钉ocr"): gr.Markdown("# 网络图片OCR识别") ocr_input=[gr.Textbox(label="请填写ocr_token"),gr.Textbox(label="请填写图片网址"),gr.Radio(["身份证","增值税发票","营业执照","银行卡","车牌","机动车发票","驾驶证","行驶证","火车票","定额发票","出租车发票","机票行程单","审批表单","花名册"],value="营业执照增值税发票",label="请选择识别类型：")] ocr_button=gr.Button("开始识别>>") ocr_output=gr.JSON(label="识别结果") ocr_button.click(dd_ocr, inputs=ocr_input, outputs=ocr_output,api_name='dingding_ocr') with gr.TabItem("datatable"): gr.Markdown("# datatable测试") dt_input=[gr.File(label="选择需要读取的文档",type='bytes'),gr.Number(label='请输入分组列序号'),gr.Number(label='请输入聚合列序号')] dt_output=gr.DataFrame(label="输出",max_rows=10,wrap=True) dt_button=gr.Button("分类汇总") dt_button.click(fx_dt, inputs=dt_input, outputs=dt_output,api_name='datatable') with gr.TabItem("js"): gr.Markdown("# js处理测试") a=gr.Textbox(label='请输入需要js处理的字符串') js_output=gr.Textbox(label="JS输出") js_button1=gr.Button("js_test") js_button2=gr.Button("js_test2") js_button=gr.Button("js逆序字符串处理") js_button3=gr.Button("调用js引用",elem_id="jsc") js_button1.click(None,a,js_output,_js="x=>'你好：'+x") js_button2.click(None,a,js_output,_js="x=>`你好：${x},欢迎吹水！`") js_button.click(None,a,js_output,_js="(x) => [...x].reverse().join('')",api_name='js') js_button3.click(None,a,js_output,_js="x=>{return md5(x)};") demo.launch()