Update app.py

app.py

@@ -3,8 +3,8 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 import re
 
-# Load more accurate detection model
-MODEL = "desklib/ai-text-detector-v1.01"
+# Stable AI detection model
+MODEL = "Hello-SimpleAI/HC3-DeBERTa"
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
 model = AutoModelForSequenceClassification.from_pretrained(MODEL)
 
@@ -24,14 +24,18 @@ def detect_ai(text):
         with torch.no_grad():
             outputs = model(**inputs)
             probs = torch.softmax(outputs.logits, dim=1)
+
+        # HC3 model: [0] = human, [1] = ChatGPT
         ai_score = float(probs[0][1])
         results.append({"sentence": sent, "ai_score": ai_score})
 
+    # Highlight
     highlighted = ""
     for r in results:
         color = get_color(r['ai_score'])
         highlighted += f"<span style='background-color:{color}; padding:2px'>{r['sentence']} </span>"
 
+    # Total AI probability
     if results:
         avg_ai = sum(r['ai_score'] for r in results) / len(results)
         total_percent = round(avg_ai * 100, 2)
@@ -41,9 +45,10 @@ def detect_ai(text):
 
     return highlighted, {"sentences": results, "total_ai_percent": total_percent}
 
+
 with gr.Blocks() as demo:
-    gr.Markdown("##  AI Detector (upgraded to DeBERTa-v3 large)")
-    gr.Markdown("Paste text: green = human-like, yellow = mixed, red = AI-like.")
+    gr.Markdown("## 🤖 AI Detector (HC3 DeBERTa)")
+    gr.Markdown("Paste text: green = human-like, red = AI-like")
     input_text = gr.Textbox(lines=8, placeholder="Enter text here…")
     output_html = gr.HTML()
     output_json = gr.JSON()