Update app.py

app.py

@@ -26,7 +26,6 @@ def get_model():
         ).to(device).eval()
     return tokenizer, model
 
-# Threshold for Verdict Label (Updated to match your 40% logic)
 THRESHOLD = 0.41 
 
 # -----------------------------
@@ -68,8 +67,17 @@ def split_preserving_structure(text):
 # -----------------------------
 @torch.inference_mode()
 def analyze(text):
-    if not text.strip():
+    # 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."
+        return "Too Short", "N/A", f"<div style='color: #b80d0d; padding: 20px; border: 1px solid #b80d0d; border-radius: 8px;'>{warning_msg}</div>", None
+
     try:
         tok, mod = get_model()
     except Exception as e:
@@ -109,7 +117,6 @@ def analyze(text):
         
         if i in prob_map:
             score = prob_map[i]
-            # Heatmap logic: < 60% is Human (Green)
             if score < 0.60: 
                 color, bg = "#11823b", "rgba(17, 130, 59, 0.15)"
             elif score < 0.80: 
@@ -126,7 +133,7 @@ def analyze(text):
             highlighted_html += block
     highlighted_html += "</div>"
 
-    # --- UPDATED LOGIC PER REQUEST ---
+    # --- LOGIC PER REQUEST (40% THRESHOLD) ---
     if weighted_avg > 0.40:
         label = f"{weighted_avg:.0%} AI Content Detected"
         display_score = f"{weighted_avg:.1%}"
@@ -134,6 +141,7 @@ def analyze(text):
         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
 
@@ -142,11 +150,11 @@ def analyze(text):
 # -----------------------------
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("## 🕵️ AI Detector Pro")
-    gr.Markdown("Sentence-level analysis. **Score is hidden (*) if at or below 40%.**")
+    gr.Markdown("Sentence-level analysis. **Min 300 words required.** Score masked (*) if ≤ 40%.")
     
     with gr.Row():
         with gr.Column(scale=3):
-            text_input = gr.Textbox(label="Paste Text", lines=12)
+            text_input = gr.Textbox(label="Paste Text", lines=12, placeholder="Minimum 300 words required for analysis...")
             run_btn = gr.Button("Analyze", variant="primary")
         with gr.Column(scale=1):
             verdict_out = gr.Label(label="Verdict")