Update app.py

app.py

@@ -2,9 +2,9 @@ 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-single",use_auth_token=auth_token)
-pipeline_zh = pipeline(task="text-classification", model="Hello-SimpleAI/chatgpt-detector-single-chinese",use_auth_token=auth_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")
 
 
 
@@ -21,28 +21,9 @@ def predict_zh(text):
 
 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！
@@ -51,13 +32,16 @@ with gr.Blocks() as demo:
         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')
 
-    # 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()