Update app.py

app.py

@@ -6,15 +6,25 @@ import gradio as gr
 # Import your local files
 import database, schemas, moderator
 
-# Initialize Database
+# 1. Initialize Database
 database.init_db()
 
 # --- DASHBOARD LOGIC ---
 def update_dashboard(user, text):
-    # 1. Run AI Analysis
+    # EMOJI ANALYSIS: Quick check for known aggressive emojis
+    bad_emojis = ["🔪", "😡", "👊", "🖕", "🔫", "🤮"]
+    emoji_flag = any(e in text for e in bad_emojis)
+    
+    # AI ANALYSIS
     analysis = moderator.moderator.analyze(text)
     
-    # 2. Save to DB
+    # If emoji found, boost toxicity
+    if emoji_flag and not analysis["is_toxic"]:
+        analysis["is_toxic"] = True
+        analysis["reason"] = "Aggressive Emoji Detected"
+        analysis["score"] = max(analysis["score"], 0.85)
+
+    # Save to Database
     db = database.SessionLocal()
     db_comment = database.Comment(
         video_id=1, user=user, text=text,
@@ -25,76 +35,90 @@ def update_dashboard(user, text):
     db.add(db_comment)
     db.commit()
 
-    # 3. Calculate Global Metrics
+    # Calculate Global Metrics
     all_comments = db.query(database.Comment).all()
     total_count = len(all_comments)
     toxic_comments = [c for c in all_comments if c.is_toxic]
     toxic_count = len(toxic_comments)
     safe_count = total_count - toxic_count
     
-    # Safety Score calculation: (Safe / Total) * 100
+    # FIX: Safety Score as a raw number (0-100)
     safety_score = (safe_count / total_count * 100) if total_count > 0 else 100
     
-    # Toxicity Classification breakdown
+    # Toxicity Classification breakdown for the Label component
     reasons = {"Identity Hate": 0, "Insult": 0, "Online Harassment": 0, "Threat": 0}
     for c in toxic_comments:
         if c.flagged_reason in reasons:
             reasons[c.flagged_reason] += 1
     
-    # Format for Donut/Label chart
-    # Normalized for the Label component
+    # Format for visual bars (Confidence score style)
     chart_data = {k: v / toxic_count if toxic_count > 0 else 0 for k, v in reasons.items()}
 
-    # Get Hall of Shame Data
+    # Hall of Shame Data (Last 5 toxic)
     shame_query = db.query(database.Comment).filter(database.Comment.is_toxic == True).order_by(database.Comment.id.desc()).limit(5).all()
     shame_data = [[c.user, c.text, f"{c.toxicity_score:.2f}", c.flagged_reason] for c in shame_query]
     
     db.close()
 
-    status = "🔴 High Toxicity" if analysis["is_toxic"] else "🟢 Safe"
+    status_label = "🔴 High Toxicity" if analysis["is_toxic"] else "🟢 Safe"
+    
+    # FIX: Return raw numbers for gr.Number to avoid rounding errors
     return (
-        f"{int(safety_score)}%",      # Safety Score
-        f"{total_count}",             # Total Signals
-        f"{toxic_count}",              # Threats Detected
-        status,                        # Current Status
-        chart_data,                   # Donut Chart Data
-        shame_data                    # History Table
+        int(safety_score),   # Safety Score %
+        int(total_count),    # Total Signals
+        int(toxic_count),    # Threats Detected
+        status_label,        # Alert Level
+        chart_data,          # Classification Bars
+        shame_data           # History Table
     )
 
+def clear_db():
+    db = database.SessionLocal()
+    db.query(database.Comment).delete()
+    db.commit()
+    db.close()
+    return 100, 0, 0, "🟢 NORMAL", {}, []
+
 # --- UI LAYOUT ---
 with gr.Blocks(theme=gr.themes.Default(), title="Admin Intelligence Hub") as demo:
-    gr.Markdown("# 🛡️ Admin Intelligence Hub\n*Real-time threat monitoring and classification*")
+    with gr.Row():
+        gr.Markdown("# 🛡️ Admin Intelligence Hub\n*Real-time threat monitoring and classification*")
+        clear_btn = gr.Button("🗑️ Clear Database", variant="stop", size="sm")
     
-    # Top Row: KPI Cards
+    # KPI Row
     with gr.Row():
         total_signals = gr.Number(label="Total Signals", value=0, precision=0)
         threats_found = gr.Number(label="🚫 Threats Identified", value=0, precision=0)
-        safety_score_gauge = gr.Label(label="✅ Safety Score")
+        safety_score = gr.Number(label="✅ Safety Score (%)", value=100, precision=0)
 
     with gr.Row():
-        # Left: Live Input & Video
+        # Main Interface
         with gr.Column(scale=2):
             gr.HTML('<iframe width="100%" height="350" src="https://www.youtube.com/embed/dQw4w9WgXcQ" frameborder="0" allowfullscreen></iframe>')
             with gr.Group():
                 user_input = gr.Textbox(label="User", value="Guest")
-                msg_input = gr.Textbox(label="Message Inference", placeholder="Type to test...")
+                msg_input = gr.Textbox(label="Message Inference", placeholder="Analyze text or emojis...")
                 submit_btn = gr.Button("ANALYZE SIGNAL", variant="primary")
         
-        # Right: Classification Chart
+        # Classification & Alerts
         with gr.Column(scale=1):
             gr.Markdown("### Toxicity Classification")
             donut_chart = gr.Label(num_top_classes=4, label="Distribution")
-            current_alert = gr.Label(label="Threat Level")
+            current_alert = gr.Label(label="Current Threat Level")
 
-    # Bottom Row: History
     gr.Markdown("### 💀 Strategic Insights & History")
     shame_table = gr.Dataframe(headers=["User", "Comment", "Score", "Reason"], interactive=False)
 
-    # Event Wiring
+    # Event Triggers
     submit_btn.click(
         update_dashboard, 
         inputs=[user_input, msg_input], 
-        outputs=[safety_score_gauge, total_signals, threats_found, current_alert, donut_chart, shame_table]
+        outputs=[safety_score, total_signals, threats_found, current_alert, donut_chart, shame_table]
+    )
+    
+    clear_btn.click(
+        clear_db,
+        outputs=[safety_score, total_signals, threats_found, current_alert, donut_chart, shame_table]
     )
 
 if __name__ == "__main__":