app.py

import os
import gradio as gr
from transformers import pipeline


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("""
                ##  🔬🔬🔬 
                """)

    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')
    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')

    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')

demo.launch()