Update app.py

app.py

@@ -1,5 +1,6 @@
+import os
 import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
 import torch
 import spacy
 import subprocess
@@ -45,7 +46,7 @@ def detect_ai_generated(text):
         label = "Human"
         probability = human_probability
     
-    return f"The content is {probability:.2f}% {label} Written"
+    return f"The content is {probability:.2f}% {label} Written", probability
 
 # Function to get synonyms using NLTK WordNet
 def get_synonyms_nltk(word, pos):
@@ -117,17 +118,18 @@ def paraphrase_and_correct(text):
     return final_text
 
 # Gradio interface definition
-with gr.Blocks() as interface:
+with gr.Blocks() as demo:
     with gr.Row():
         with gr.Column():
-            text_input = gr.Textbox(lines=5, label="Input Text")
-            detect_button = gr.Button("AI Detection")
-            paraphrase_button = gr.Button("Paraphrase & Correct")
-        with gr.Column():
-            output_text = gr.Textbox(label="Output")
+            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='Probability (%)')
 
-    detect_button.click(detect_ai_generated, inputs=text_input, outputs=output_text)
-    paraphrase_button.click(paraphrase_and_correct, inputs=text_input, outputs=output_text)
+    button1.click(detect_ai_generated, inputs=[t1], outputs=[label1, score1])
 
-# Launch the Gradio app
-interface.launch(debug=False)
+demo.launch()