app.py

import streamlit as st
import os
import json
import re
import datasets
import tiktoken
import zipfile
from pathlib import Path

# 定义 tiktoken 编码器
encoding = tiktoken.get_encoding("cl100k_base")

# MGTHuman 类
class MGTHuman(datasets.GeneratorBasedBuilder):
    VERSION = datasets.Version("1.0.0")
    BUILDER_CONFIGS = [
        datasets.BuilderConfig(name="human", version=VERSION, description="This part of human data"),
        datasets.BuilderConfig(name="Moonshot", version=VERSION, description="Data from the Moonshot model"),
        datasets.BuilderConfig(name="gpt35", version=VERSION, description="Data from the gpt-3.5-turbo model"),
        datasets.BuilderConfig(name="Llama3", version=VERSION, description="Data from the Llama3 model"),
        datasets.BuilderConfig(name="Mixtral", version=VERSION, description="Data from the Mixtral model"),
        datasets.BuilderConfig(name="Qwen", version=VERSION, description="Data from the Qwen model"),
    ]
    DEFAULT_CONFIG_NAME = "human"

    def truncate_text(self, text, max_tokens=2048):
        tokens = encoding.encode(text, allowed_special={'<|endoftext|>'})
        if len(tokens) > max_tokens:
            tokens = tokens[:max_tokens]
            truncated_text = encoding.decode(tokens)
            last_period_idx = truncated_text.rfind('。')
            if last_period_idx == -1:
                last_period_idx = truncated_text.rfind('.')
            if last_period_idx != -1:
                truncated_text = truncated_text[:last_period_idx + 1]
            return truncated_text
        else:
            return text

    def get_text_by_index(self, filepath, index):
        count = 0
        with open(filepath, 'r') as f:
            data = json.load(f)
        for row in data:
            if not row["text"].strip():
                continue
            if count == index:
                text = self.truncate_text(row["text"], max_tokens=2048)
                return text
            count += 1
        return "Index 超出范围，请输入有效的数字。"

# Streamlit UI
st.title("MGTHuman Dataset Viewer")

# 上传包含 JSON 文件的 ZIP 文件
uploaded_folder = st.file_uploader("上传包含 JSON 文件的 ZIP 文件夹", type=["zip"])
if uploaded_folder:
    folder_path = Path("temp")
    folder_path.mkdir(exist_ok=True)
    zip_path = folder_path / uploaded_folder.name
    with open(zip_path, "wb") as f:
        f.write(uploaded_folder.getbuffer())

    with zipfile.ZipFile(zip_path, 'r') as zip_ref:
        zip_ref.extractall(folder_path)

    # 获取所有 JSON 文件并分类到不同的 domain
    category = {}
    for json_file in folder_path.glob("*.json"):
        domain = json_file.stem.split('_task3')[0]
        category.setdefault(domain, []).append(str(json_file))

    # 显示可用的 domain
    st.write("可用的数据种类:", list(category.keys()))

    # 用户选择 domain
    selected_domain = st.selectbox("选择数据种类", options=list(category.keys()))
    
    # 输入序号查看文本
    index_to_view = st.number_input("输入要查看的文本序号", min_value=0, step=1)
    
    if st.button("显示文本"):
        # 选择第一个文件进行展示
        file_to_display = category[selected_domain][0]
        mgt_human = MGTHuman(name=selected_domain)
        text = mgt_human.get_text_by_index(file_to_display, index=index_to_view)
        st.write("对应的文本内容为：", text)

# 清理上传文件的临时目录
if st.button("清除文件"):
    import shutil
    shutil.rmtree("temp")
    st.write("临时文件已清除。")