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)
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)
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)
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)
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("""
""") demo.launch()