app.py

import os
import gradio as gr
from transformers import pipeline
from huggingface_hub import login

auth_token = os.environ.get("access_token")
login(token=auth_token)
pipeline_en = pipeline(task="text-classification", model="MeiJuice/CheckGPT")
pipeline_zh = pipeline(task="text-classification", model="MeiJuice/CheckGPT-Chinese")


def predict_en(text):
    res = pipeline_en(text, truncation=True)[0]
    return "ChatGPT" if res['label'] == "LABEL_1" else "human", res['score']


def predict_zh(text):
    res = pipeline_zh(text, truncation=True, max_length=512)[0]
    return "ChatGPT" if res['label'] == "LABEL_1" else "human", res['score']


with gr.Blocks() as demo:
    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="No one can call back yesterday, Yesterday will not be called again.")
        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="联邦学习（Federated learning）是在进行分布式机器学习的过程中，各参与方可借助其他参与方数据进行联合建模和使用模型。参与各方无需传递和共享原始数据资源，同时保护模型参数，即在数据不出本地的情况下，进行数据联合训练、联合应用，建立合法合规的机器学习模型,成为一种解决合作中数据隐私与数据共享矛盾的新路径，FL本质上承诺多方通过交换梯度而不是原始数据来联合训练模型。")
        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')

    gr.Markdown("""
                <center><a href="https://clustrmaps.com/site/1bunn"  title='Visit tracker'><img src="//www.clustrmaps.com/map_v2.png?d=8kMOCu2ulreZ_7l-HCuhaNgRvPpqKGPZxGzf4Ex1jr8&cl=ffffff"/></a></center>
                """)


demo.launch()