Update app.py

app.py

@@ -26,7 +26,7 @@ def get_model():
         ).to(device).eval()
     return tokenizer, model
 
-# Global threshold for the "Verdict" label
+# Threshold for Verdict Label
 THRESHOLD = 0.60 
 
 # -----------------------------
@@ -82,7 +82,6 @@ def analyze(text):
     if not pure_sents:
         return "—", "—", "<em>No sentences detected.</em>", None
 
-    # Context window analysis
     windows = []
     for i in range(len(pure_sents)):
         start = max(0, i - 1)
@@ -98,7 +97,7 @@ def analyze(text):
     weighted_avg = sum(p * l for p, l in zip(probs, lengths)) / total_words if total_words > 0 else 0
 
     # -----------------------------
-    # HTML RECONSTRUCTION (Revised Thresholds)
+    # HTML RECONSTRUCTION
     # -----------------------------
     highlighted_html = "<div style='font-family: sans-serif; line-height: 1.8;'>"
     prob_map = {idx: probs[i] for i, idx in enumerate(pure_sents_indices)}
@@ -110,14 +109,13 @@ def analyze(text):
         
         if i in prob_map:
             score = prob_map[i]
-            
-            # Revised coloring logic: < 60% is Human (Green)
+            # Heatmap logic: < 60% is Human (Green)
             if score < 0.60: 
-                color, bg = "#11823b", "rgba(17, 130, 59, 0.15)" # Green
+                color, bg = "#11823b", "rgba(17, 130, 59, 0.15)"
             elif score < 0.80: 
-                color, bg = "#b8860b", "rgba(184, 134, 11, 0.15)" # Amber
+                color, bg = "#b8860b", "rgba(184, 134, 11, 0.15)"
             else: 
-                color, bg = "#b80d0d", "rgba(184, 13, 13, 0.15)" # Red
+                color, bg = "#b80d0d", "rgba(184, 13, 13, 0.15)"
             
             highlighted_html += (
                 f"<span style='background:{bg}; padding:2px 4px; border-radius:4px; border-bottom: 2px solid {color};' "
@@ -128,18 +126,22 @@ def analyze(text):
             highlighted_html += block
     highlighted_html += "</div>"
 
-    # Final Verdict logic based on the 60% rule
+    # --- THE FIXES ---
+    # 1. Verdict Label Fix
     label = "AI Content Detected" if weighted_avg >= THRESHOLD else "0 or * AI Content Detected"
     
+    # 2. Weighted Score Fix: If below 50%, show '*'
+    display_score = f"{weighted_avg:.1%}" if weighted_avg >= 0.50 else "*"
+    
     df = pd.DataFrame({"Sentence": pure_sents, "AI Confidence": [f"{p:.1%}" for p in probs]})
-    return label, f"{weighted_avg:.1%}", highlighted_html, df
+    return label, display_score, highlighted_html, df
 
 # -----------------------------
 # GRADIO INTERFACE
 # -----------------------------
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("## 🕵️ AI Detector Pro")
-    gr.Markdown("Sentence-level analysis with weighted context windows. **Threshold: < 60% = Human.**")
+    gr.Markdown("Sentence-level analysis. **Green < 60% | Score masked if < 50%**")
     
     with gr.Row():
         with gr.Column(scale=3):