Update app.py

app.py

@@ -3,39 +3,39 @@ from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 import re
 
-# Stable AI detection model
-MODEL = "Hello-SimpleAI/HC3-DeBERTa"
+# Load model
+MODEL = "roberta-base-openai-detector"
 tokenizer = AutoTokenizer.from_pretrained(MODEL)
 model = AutoModelForSequenceClassification.from_pretrained(MODEL)
 
 def get_color(ai_score):
+    """Convert AI score (0-1) into a smooth green-yellow-red gradient."""
     red = int(ai_score * 255)
     green = int((1 - ai_score) * 255)
     return f"rgb({red},{green},0)"
 
 def detect_ai(text):
-    sentences = re.split(r'(?<=[.!?]) +', text)
+    # Split by paragraphs instead of sentences
+    paragraphs = re.split(r"\n\s*\n", text.strip())
     results = []
 
-    for sent in sentences:
-        if not sent.strip():
+    for para in paragraphs:
+        if not para.strip():
             continue
-        inputs = tokenizer(sent, return_tensors="pt", truncation=True, max_length=512)
+        inputs = tokenizer(para, return_tensors="pt", truncation=True, max_length=512)
         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})
+        results.append({"paragraph": para, "ai_score": ai_score})
 
-    # Highlight
+    # Build highlighted HTML
     highlighted = ""
     for r in results:
         color = get_color(r['ai_score'])
-        highlighted += f"<span style='background-color:{color}; padding:2px'>{r['sentence']} </span>"
+        highlighted += f"<div style='background-color:{color}; padding:6px; margin-bottom:4px'>{r['paragraph']}</div>"
 
-    # Total AI probability
+    # Compute total AI percentage (average across paragraphs)
     if results:
         avg_ai = sum(r['ai_score'] for r in results) / len(results)
         total_percent = round(avg_ai * 100, 2)
@@ -43,16 +43,16 @@ def detect_ai(text):
     else:
         total_percent = 0.0
 
-    return highlighted, {"sentences": results, "total_ai_percent": total_percent}
-
+    return highlighted, {"paragraphs": results, "total_ai_percent": total_percent}
 
 with gr.Blocks() as demo:
-    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…")
+    gr.Markdown("## 🤖 AI Detector (Paragraph-level)")
+    gr.Markdown("Paste your text below. Green = human-like, Yellow = mixed, Red = AI-like.")
+    input_text = gr.Textbox(lines=8, placeholder="Enter text here with paragraphs separated by blank lines...")
     output_html = gr.HTML()
     output_json = gr.JSON()
     run_btn = gr.Button("Detect AI")
+
     run_btn.click(detect_ai, inputs=input_text, outputs=[output_html, output_json])
 
 demo.launch()