Update app.py

app.py

@@ -26,7 +26,8 @@ def get_model():
         ).to(device).eval()
     return tokenizer, model
 
-THRESHOLD = 0.41 
+# UPDATED THRESHOLD: Only 81% and above is flagged as AI
+THRESHOLD = 0.81 
 
 # -----------------------------
 # PROTECT STRUCTURE
@@ -67,12 +68,10 @@ def split_preserving_structure(text):
 # -----------------------------
 @torch.inference_mode()
 def analyze(text):
-    # Basic cleanup
     text = text.strip()
     if not text:
         return "—", "—", "<em>Please enter text...</em>", None
         
-    # --- WORD COUNT CHECK ---
     word_count = len(text.split())
     if word_count < 300:
         warning_msg = f"⚠️ <b>Insufficient Text:</b> Your input has {word_count} words. Please enter at least 300 words for an accurate analysis."
@@ -90,7 +89,6 @@ 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)
@@ -101,13 +99,12 @@ 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 (Red vs Green Only)
+    # 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)}
@@ -120,13 +117,11 @@ def analyze(text):
         if i in prob_map:
             score = prob_map[i]
             
-            # Use THRESHOLD for binary color logic
-            if score > THRESHOLD:
-                # RED: Above the threshold (AI detected)
-                color, bg = "#b80d0d", "rgba(184, 13, 13, 0.15)"
+            # Logic: Red for > 0.81, Green for everything else (<= 0.81)
+            if score >= THRESHOLD:
+                color, bg = "#b80d0d", "rgba(184, 13, 13, 0.15)" # RED
             else:
-                # GREEN: Below the threshold (Human-like)
-                color, bg = "#11823b", "rgba(17, 130, 59, 0.15)"
+                color, bg = "#11823b", "rgba(17, 130, 59, 0.15)" # GREEN
             
             highlighted_html += (
                 f"<span style='background:{bg}; padding:2px 4px; border-radius:4px; border-bottom: 2px solid {color};' "
@@ -137,8 +132,8 @@ def analyze(text):
             highlighted_html += block
     highlighted_html += "</div>"
 
-    # --- FINAL VERDICT LOGIC ---
-    if weighted_avg > THRESHOLD:
+    # --- FINAL VERDICT ---
+    if weighted_avg >= THRESHOLD:
         label = f"{weighted_avg:.0%} AI Content Detected"
         display_score = f"{weighted_avg:.1%}"
     else:
@@ -152,12 +147,12 @@ def analyze(text):
 # GRADIO INTERFACE
 # -----------------------------
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("## 🕵️ Detector Pro")
-    gr.Markdown(f"Sentence-level analysis. **Min 300 words required.** Score masked (*) if ≤ {THRESHOLD*100:.0f}%.")
+    gr.Markdown("## 🕵️ AI Detector Pro")
+    gr.Markdown(f"Strict Analysis. Threshold: **{THRESHOLD*100:.0f}%**. Everything below this is considered Human.")
     
     with gr.Row():
         with gr.Column(scale=3):
-            text_input = gr.Textbox(label="Paste Text", lines=12, placeholder="Minimum 300 words required for analysis...")
+            text_input = gr.Textbox(label="Paste Text", lines=12, placeholder="Minimum 300 words...")
             run_btn = gr.Button("Analyze", variant="primary")
         with gr.Column(scale=1):
             verdict_out = gr.Label(label="Verdict")