app.py

import gradio as gr
import os
from models.model import *

import ast
import argparse
import glob
import pickle

import numpy as np
import pandas as pd
block_css = """
#notice_markdown {
    font-size: 104%
}
#notice_markdown th {
    display: none;
}
#notice_markdown td {
    padding-top: 6px;
    padding-bottom: 6px;
}
#leaderboard_markdown {
    font-size: 104%
}
#leaderboard_markdown td {
    padding-top: 6px;
    padding-bottom: 6px;
}
#leaderboard_dataframe td {
    line-height: 0.1em;
}
footer {
    display:none !important
}
.image-container {
    display: flex;
    align-items: center;
    padding: 1px;
}
.image-container img {
    margin: 0 30px;
    height: 20px;
    max-height: 100%;
    width: auto;
    max-width: 20%;
}
"""
def model_hyperlink(model_name, link):
    return f'<a target="_blank" href="{link}" style="color: var(--link-text-color); text-decoration: underline;text-decoration-style: dotted;">{model_name}</a>'
def load_leaderboard_table_csv(filename, add_hyperlink=True):
    lines = open(filename).readlines()
    heads = [v.strip() for v in lines[0].split(",")]
    rows = []
    for i in range(1, len(lines)):
        row = [v.strip() for v in lines[i].split(",")]
        for j in range(len(heads)):
            item = {}
            for h, v in zip(heads, row):
                if h != "AI歌手名/AI Singer" and h != "歌手代表作或介绍链接" and h != "模型zip链接/Link to Model Zip File" and h != "模型贡献者/Model Contributor":
                    item[h] = int(v)
                else:
                    item[h] = v
            if add_hyperlink:
                item["AI歌手名/AI Singer"] = model_hyperlink(item["AI歌手名/AI Singer"], item["歌手代表作或介绍链接"])
        rows.append(item)
    return rows
    
@spaces.GPU
def get_arena_table(model_table_df):
    # sort by rating
    model_table_df = model_table_df.sort_values(by=["训练素材时长/Duration of Training Dataset(min)"], ascending=False)
    values = []
    for i in range(len(model_table_df)):
        row = []
        model_key = model_table_df.index[i]
        model_name = model_table_df["AI歌手名/AI Singer"].values[model_key]
        # rank
        row.append(i + 1)
        # model display name
        row.append(model_name)

        row.append(
            model_table_df["模型zip链接/Link to Model Zip File"].values[model_key]
        )

        row.append(
            model_table_df["训练素材时长/Duration of Training Dataset(min)"].values[model_key]
        )

        row.append(
            model_table_df["训练epoch数/Epoch"].values[model_key]
        )

        row.append(
            model_table_df["模型贡献者/Model Contributor"].values[model_key]
        )

        values.append(row)
    return values



title_markdown = ("""
<h2 align="center"> 🌊💕🎶 滔滔AI，AI歌手模型开源社区 </h2>
<h3 align="center"> 🌟 完全开源、完全免费、共建共享！全网AI歌手任您选择！ </h3>
""")

pic_markdown = ("""
<h3 align="center">  </h3>
<h1 align="center"><a href="https://www.talktalkai.com/"><img src="https://download.openxlab.org.cn/models/Kevin676/rvc-models/weight/talktalkai-cover.png", alt="talktalkai-cover" border="0" style="margin: 0 auto; height: 300px;" /></a> </h1>
""")


data = load_leaderboard_table_csv("./OCRBench.csv")
model_table_df = pd.DataFrame(data)
text_size = gr.themes.sizes.text_lg


with gr.Blocks(theme=gr.themes.Base(text_size=text_size), css=block_css) 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>")
        gr.Markdown("##### 点击[此链接](https://docs.google.com/spreadsheets/d/1owfUtQuLW9ReiIwg6U9UkkDmPOTkuNHf0OKQtWu1iaI/edit?gid=1227575351#gid=1227575351)，查看全网所有开源AI歌手模型，超9000个模型任您挑选 🥳")
        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</h3>")
        gr.HTML("<h4>上传完成后您立即可以搜索到您上传的模型</h4>")
        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.Tab("🍻滔滔AI精选模型"):
        arena_table_vals = get_arena_table(model_table_df)
        md = """
        AI翻唱🎶：您可以在社区中复制您喜欢的AI歌手的“模型zip链接”，之后就可以在“🌟重磅首发 - AI歌手全明星💕”页面中通过粘贴zip链接来使用您喜欢的AI歌手模型啦！\n
        [手机端📱](https://g-app-center-40055665-0593-xqmmjg6.openxlab.space)查看 滔滔AI精选模型
        """
        gr.Markdown(md, elem_id="leaderboard_markdown")
        gr.Dataframe(
            headers=[
                "排序",
                "AI歌手名/AI Singer",
                "模型zip链接/Link to Model Zip File",
                "训练素材时长/Duration of Training Dataset(min)",
                "训练epoch数/Epoch",
                "模型贡献者/Model Contributor",
            ],
            datatype=[
                "str",
                "markdown",
                "str",
                "number",
                "number",
                "str",
            ],
            value=arena_table_vals,
            elem_id="arena_leaderboard_dataframe",
            height=800,
            column_widths=[50, 100, 205, 95, 95, 95],
            wrap=True,
        )

    gr.Markdown(pic_markdown)
    gr.Markdown("###### <center>注意❗：请不要生成会对个人以及组织造成侵害的内容，此程序仅供科研、学习及个人娱乐使用。请自觉合规使用此程序，程序开发者不负有任何责任。</center>")

    gr.HTML('''
        <div class="footer">
                    <p>🌊🏞️🎶 - 江水东流急，滔滔无尽声。 明·顾璘
                    </p>
        </div>
    ''')
   
    
    app.queue(max_size=40, api_open=False)
    app.launch(max_threads=400, show_error=True)