Update app.py

app.py

@@ -1,5 +1,26 @@
 import gradio as gr
 
-gr.Interface.load("models/Hello-SimpleAI/chatgpt-detector-roberta",
-                  title="🤖 Start detecting AI Plagiarism",
-                 description= "Paste in the text you want to check and get a holistic score for how much of the document is written by AI. This model is based on Hello Simple's paper [arxiv: 2301.07597](https://arxiv.org/abs/2301.07597) and Github project [Hello-SimpleAI/chatgpt-comparison-detection](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection).").launch()
+model = gr.Interface.load("models/Hello-SimpleAI/chatgpt-detector-roberta",
+                  title="🤖 Start detecting AI Plagiarism").launch()
+
+def predict_en(text):
+    res = model(text)[0]
+    return res['label'],res['score']
+
+with gr.Blocks() as demo:
+    gr.Markdown("""
+                     Paste in the text you want to check and get a holistic score for how much of the document is written by AI. This model is based on Hello Simple's paper [arxiv: 2301.07597](https://arxiv.org/abs/2301.07597) and Github project [Hello-SimpleAI/chatgpt-comparison-detection](https://github.com/Hello-SimpleAI/chatgpt-comparison-detection).
+                """)
+    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])
+
+
+demo.launch()