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")

# _CITATION = """\
# @InProceedings{huggingface:dataset,
# title = {MGT detection},
# author={Trustworthy AI Lab},
# year={2024}
# }
# """

# _DESCRIPTION = """\
# For detecting machine generated text.
# """

# _HOMEPAGE = ""
# _LICENSE = ""

# # 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 _info(self):
#         features = datasets.Features(
#             {
#                 "id": datasets.Value("int32"),
#                 "text": datasets.Value("string"),
#                 "file": datasets.Value("string"),
#             }
#         )
#         return datasets.DatasetInfo(
#             description=_DESCRIPTION,
#             features=features,
#             homepage=_HOMEPAGE,
#             license=_LICENSE,
#             citation=_CITATION,
#         )

#     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, cut_tokens=False, max_tokens=2048):
#         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 = row["text"]
#                 if cut_tokens:
#                     text = self.truncate_text(text, max_tokens)
#                 return text
#             count += 1
#         return "Index 超出范围，请输入有效的数字。"
    
#     def count_entries(self, filepath):
#         """返回文件中的总条数，用于动态生成索引范围"""
#         count = 0
#         with open(filepath, 'r') as f:
#             data = json.load(f)
#             for row in data:
#                 if row["text"].strip():
#                     count += 1
#         return count

# # 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)

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

#     # 显示可用的 domain 下拉框
#     if category:
#         selected_domain = st.selectbox("选择数据种类", options=list(category.keys()))
        
#         # 确定该 domain 的第一个文件路径并获取条目数量
#         file_to_display = category[selected_domain][0]
#         mgt_human = MGTHuman(name=selected_domain)
#         total_entries = mgt_human.count_entries(file_to_display)
#         st.write(f"可用的索引范围: 0 到 {total_entries - 1}")
        
#         # 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, max_value=total_entries - 1, step=1)

#         # 添加复选框以选择是否切割文本
#         cut_tokens = st.checkbox("是否对文本进行token切割", value=False)
        
#         if st.button("显示文本"):
#             text = mgt_human.get_text_by_index(file_to_display, index=index_to_view, cut_tokens=cut_tokens)
#             st.write("对应的文本内容为：", text)
#     else:
#         st.write("未找到任何 JSON 文件，请检查 ZIP 文件结构。")

# # 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。")

import streamlit as st
from transformers import pipeline

# Initialize Hugging Face text classifier
@st.cache_resource  # Cache the model to avoid reloading
def load_model():
    # Use a Hugging Face pre-trained text classification model
    # Replace with a suitable model if necessary
    classifier = pipeline("text-classification", model="roberta-base-openai-detector")
    return classifier

st.title("Machine-Generated Text Detector")
st.write("Enter a text snippet, and I will analyze it to determine if it is likely written by a human or generated by a machine.")

# Load the model
classifier = load_model()

# Input text
input_text = st.text_area("Enter text here:", height=150)

# Button to trigger detection
if st.button("Analyze"):
    if input_text:
        # Make prediction
        result = classifier(input_text)

        # Extract label and confidence score
        label = result[0]['label']
        score = result[0]['score'] * 100  # Convert to percentage for readability

        # Display result
        if label == "LABEL_1":
            st.write(f"**Result:** This text is likely **Machine-Generated**.")
        else:
            st.write(f"**Result:** This text is likely **Human-Written**.")

        # Display confidence score
        st.write(f"**Confidence Score:** {score:.2f}%")
    else:
        st.write("Please enter some text for analysis.")