Update app.py

app.py

@@ -90,6 +90,7 @@ def analyze(text):
     if not pure_sents:
         return "—", "—", "<em>No sentences detected.</em>", None
 
+    # Sliding window inference
     windows = []
     for i in range(len(pure_sents)):
         start = max(0, i - 1)
@@ -100,12 +101,13 @@ def analyze(text):
     logits = mod(**inputs).logits
     probs = F.softmax(logits.float(), dim=-1)[:, 1].cpu().numpy().tolist()
 
+    # Calculate Weighted Average
     lengths = [len(s.split()) for s in pure_sents]
     total_words = sum(lengths)
     weighted_avg = sum(p * l for p, l in zip(probs, lengths)) / total_words if total_words > 0 else 0
 
     # -----------------------------
-    # HTML RECONSTRUCTION
+    # HTML RECONSTRUCTION (Red vs Green Only)
     # -----------------------------
     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)}
@@ -117,12 +119,14 @@ def analyze(text):
         
         if i in prob_map:
             score = prob_map[i]
-            if score < 0.60: 
-                color, bg = "#11823b", "rgba(17, 130, 59, 0.15)"
-            elif score < 0.80: 
-                color, bg = "#b8860b", "rgba(184, 134, 11, 0.15)"
-            else: 
+            
+            # Use THRESHOLD for binary color logic
+            if score > THRESHOLD:
+                # RED: Above the threshold (AI detected)
                 color, bg = "#b80d0d", "rgba(184, 13, 13, 0.15)"
+            else:
+                # GREEN: Below the threshold (Human-like)
+                color, bg = "#11823b", "rgba(17, 130, 59, 0.15)"
             
             highlighted_html += (
                 f"<span style='background:{bg}; padding:2px 4px; border-radius:4px; border-bottom: 2px solid {color};' "
@@ -133,15 +137,14 @@ def analyze(text):
             highlighted_html += block
     highlighted_html += "</div>"
 
-    # --- LOGIC PER REQUEST (40% THRESHOLD) ---
-    if weighted_avg > 0.40:
+    # --- FINAL VERDICT LOGIC ---
+    if weighted_avg > THRESHOLD:
         label = f"{weighted_avg:.0%} AI Content Detected"
         display_score = f"{weighted_avg:.1%}"
     else:
         label = "0 or * AI Content Detected"
         display_score = "*"
     
-    # Always return the dataframe if analysis was successful
     df = pd.DataFrame({"Sentence": pure_sents, "AI Confidence": [f"{p:.1%}" for p in probs]})
     return label, display_score, highlighted_html, df
 
@@ -149,8 +152,8 @@ def analyze(text):
 # GRADIO INTERFACE
 # -----------------------------
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("## 🕵️ AI Detector Pro")
-    gr.Markdown("Sentence-level analysis. **Min 300 words required.** Score masked (*) if ≤ 40%.")
+    gr.Markdown("## 🕵️ Detector Pro")
+    gr.Markdown(f"Sentence-level analysis. **Min 300 words required.** Score masked (*) if ≤ {THRESHOLD*100:.0f}%.")
     
     with gr.Row():
         with gr.Column(scale=3):