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import os
import gradio as gr
from transformers import pipeline
# auth_token = os.environ.get("access_token")
pipeline_en = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta")
pipeline_zh = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-roberta-chinese")
def predict_en(text):
res = pipeline_en(text)[0]
return res['label'],res['score']
def predict_zh(text):
res = pipeline_zh(text)[0]
return res['label'],res['score']
with gr.Blocks() as demo:
gr.Markdown("""
## ChatGPT Detector 🔬 (Sinlge-text version)
Visit our project on Github: [chatgpt-comparison-detection project](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection)<br>
欢迎在 Github 上关注我们的 [ChatGPT 对比与检测项目](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection)
We provide three kinds of detectors, all in Bilingual / 我们提供了三个版本的检测器，且都支持中英文:
- [**QA version / 问答版**](https://huggingface.co/spaces/Hello-SimpleAI/chatgpt-detector-qa)<br>
detect whether an **answer** is generated by ChatGPT for certain **question**, using PLM-based classifiers / 判断某个**问题的回答**是否由ChatGPT生成，使用基于PTM的分类器来开发;
- [Sinlge-text version / 独立文本版 (👈 Current / 当前使用)](https://huggingface.co/spaces/Hello-SimpleAI/chatgpt-detector-single)<br>
detect whether a piece of text is ChatGPT generated, using PLM-based classifiers / 判断**单条文本**是否由ChatGPT生成，使用基于PTM的分类器来开发;
- [Linguistic version / 语言学版](https://huggingface.co/spaces/Hello-SimpleAI/chatgpt-detector-ling)<br>
detect whether a piece of text is ChatGPT generated, using linguistic features / 判断**单条文本**是否由ChatGPT生成，使用基于语言学特征的模型来开发;
""")
with gr.Tab("English"):
gr.Markdown("""
Note: Providing more text to the `Text` box can make the prediction more accurate!
""")
t1 = gr.Textbox(lines=5, label='Text',value="There are a few things that can help protect your credit card information from being misused when you give it to a restaurant or any other business:\n\nEncryption: Many businesses use encryption to protect your credit card information when it is being transmitted or stored. This means that the information is transformed into a code that is difficult for anyone to read without the right key.")
button1 = gr.Button("🤖 Predict!")
label1 = gr.Textbox(lines=1, label='Predicted Label 🎃')
score1 = gr.Textbox(lines=1, label='Prob')
with gr.Tab("中文版"):
gr.Markdown("""
注意: 在`文本`栏中输入更多的文本，可以让预测更准确哦！
""")
t2 = gr.Textbox(lines=5, label='文本',value="对于OpenAI大力出奇迹的工作，自然每个人都有自己的看点。我自己最欣赏的地方是ChatGPT如何解决 “AI校正(Alignment)“这个问题。这个问题也是我们课题组这两年在探索的学术问题之一。")
button2 = gr.Button("🤖 预测!")
label2 = gr.Textbox(lines=1, label='预测结果 🎃')
score2 = gr.Textbox(lines=1, label='模型概率')
button1.click(predict_en, inputs=[t1], outputs=[label1,score1], api_name='predict_en')
button2.click(predict_zh, inputs=[t2], outputs=[label2,score2], api_name='predict_zh')
# Page Count
gr.Markdown("""
<center><a href='https://clustrmaps.com/site/1bsdc' title='Visit tracker'><img src='//clustrmaps.com/map_v2.png?cl=080808&w=a&t=tt&d=NXQdnwxvIm27veMbB5F7oHNID09nhSvkBRZ_Aji9eIA&co=ffffff&ct=808080'/></a></center>
""")
demo.launch() |