import gradio as gr
import pulp
import pandas as pd
import openpyxl
import requests
import os
os.system('pip install paddlepaddle')
os.system('pip install paddleocr')
from paddleocr import PaddleOCR, draw_ocr
from PIL import Image
import torch

w_style="""
<style>
footer {visibility: hidden; }
</style>
"""

html_code='''
<iframe src="https://web.powerva.microsoft.com/environments/Default-51a58d6c-4fcf-4b75-8608-d00bf7f244d5/bots/new_bot_830e155fc862429e89683426b31c9bd5/webchat" height="500" frameborder="1" style="width:100%"></iframe>
'''

ht_text='''
<iframe width="800" height="800" frameborder="0" scrolling="no" src="https://dyrscomcn-my.sharepoint.com/personal/huangcaiguang_dyrs_com_cn/_layouts/15/Doc.aspx?sourcedoc={6d3da844-5b54-473b-a32a-08659600d015}&action=embedview&AllowTyping=True&ActiveCell='业绩'!A1&wdInConfigurator=True&wdInConfigurator=True&edesNext=false&ejss=false"></iframe>
'''

demo = gr.Blocks(css='''
#chedan {-webkit-transition-duration: 0.4s; transition-duration: 0.4s; border-radius: 40%}
#chedan:hover {background-color: #4CAF50; color: white;}
#chedan:active {background-color: #3e8e41; box-shadow: 0 5px #666; transform: translateY(4px);}
''')

def fx1(x):
    return f"欢迎练习Gradio, {x}!"

def fx2(x,y):
    m=pulp.LpProblem()
    x=list(map(int,x.split(',')))
    t=[pulp.LpVariable('t'+str(i),cat=pulp.LpBinary) for i in range(len(x))]
    m+=pulp.lpDot(x,t)
    m+=(pulp.lpDot(x,t)==y)
    m.solve()
    resu={'data':[x[i] for i in range(len(t)) if pulp.value(t[i])]}
    return resu

def fx3(x):
    df=pd.read_excel(x,header=0,engine='openpyxl')
    return df
    
def fx4(x):
    url_s="https://tts.baidu.com/text2audio?tex="+x+"&cuid=baike&lan=ZH&ie=utf-8&ctp=1&pdt=301&vol=9&rate=32&per=0"
    return "点击音频文件 [voice]("+url_s+")"
    
def fx5():
    return html_code
    
def fx6(img, lang):
    ocr = PaddleOCR(use_angle_cls=True, lang={'中文':'ch','英文':'en','法文':'fr','德文':'german','韩文':'korean','日文':'japan'}[lang],use_gpu=False)
    img_path = img.name
    result = ocr.ocr(img_path, cls=True)
    image = Image.open(img_path).convert('RGB')
    boxes = [line[0] for line in result]
    txts = [line[1][0] for line in result]
    scores = [line[1][1] for line in result]
    im_show = draw_ocr(image, boxes, txts, scores,
                       font_path='simfang.ttf')
    im_show = Image.fromarray(im_show)
    im_show.save('result.jpg')
    return 'result.jpg'

model2 = torch.hub.load(
    "AK391/animegan2-pytorch:main",
    "generator",
    pretrained=True,
    device="cuda",
    progress=False
)
face2paint = torch.hub.load(
    'AK391/animegan2-pytorch:main', 'face2paint', 
    size=512, device="cuda",side_by_side=False
)
def fx7(img):
    out = face2paint(model2, img)
    return out
    
    
with demo:
    gr.Markdown(
    """
    # <center> WEB APP测试应用!
    <div align=center><img src="https://pbihub.cn/uploads/images/201809/23/44/n6xk1x6UnN.gif" width="405" height="405" /></div>
    """)
    with gr.Tabs():
        with gr.TabItem("测试1"):
            text_input = gr.Textbox(placeholder='请输入测试字符串，如"畅心"',label="请输入测试内容",show_label=True)
            text_output = gr.Textbox(label="输出结果",show_label=True)
            tj_button = gr.Button("提交>>",elem_id="chedan")
        with gr.TabItem("凑数"):
            val_input = [gr.Textbox(placeholder='请输入凑数序列，如"1,3,5,10,16,54,32,48,6,7"',label="请输入待凑数序列",show_label=True),gr.Number(value=80,label="请输入凑数和值",show_label=True)]
            json_output = gr.JSON(label="输出运算结果",show_label=True)
            cs_button = gr.Button("开始凑数>>")
        with gr.TabItem("文件交互"):
            file_input = gr.File(file_count="single",label="请选择需要读取的excel文件",show_label=True)
            table_output = gr.Dataframe(label="输出读取的表格数据",show_label=True)
            dq_button = gr.Button("开始读取>>")
        with gr.TabItem("TTS"):
            TS_input = gr.Textbox(placeholder='请输入测试字符串，如"欢迎测试字符串转语音功能模块"',label="请输入测试内容",show_label=True)
            audio_output = gr.Markdown(label="点击生成音频文件",show_label=True)
            tts_button = gr.Button("开始转换>>")
        with gr.TabItem("Power Virtual Agents"):
            pva_button = gr.Button("调用机器人对话>>")
            pva_output=gr.HTML(label="机器人聊天窗口",show_label=True)
        with gr.TabItem("Excel365测试"):
            gr.HTML(value=ht_text,label="在线版演示",show_label=True)
        with gr.TabItem("OCR识别"):
            ocr_input=[gr.Image(type='file', label='请提供你需要识别的照片'),gr.Dropdown(choices=['中文','英文','法文','德文','韩文','日文'], type="value", default='中文', label='请选择需要识别的语言')]
            ocr_output=gr.Image(type='file', label='识别结果')
            ocr_button = gr.Button("开始ocr识别")
        with gr.TabItem("风格迁移"):
            qy_input=gr.Image(type="pil",label="请选择需要风格迁移的照片")
            qy_output=gr.Image(type="pil",label="输出迁移风格结果")
            qy_button=gr.Button("开始调用模型")
            
    tj_button.click(fx1, inputs=text_input, outputs=text_output)
    cs_button.click(fx2, inputs=val_input, outputs=json_output,api_name="ghqj")
    dq_button.click(fx3, inputs=file_input, outputs=table_output,api_name="df")
    tts_button.click(fx4, inputs=TS_input, outputs=audio_output)
    pva_button.click(fx5, inputs=[],outputs=pva_output)
    ocr_button.click(fx6,inputs=ocr_input,outputs=ocr_output)
    qy_button.click(fx7,inputs=qy_input,outputs=qy_output)

demo.launch(enable_queue=True)