Update app.py

app.py

@@ -2,29 +2,20 @@ import os
 import uvicorn
 import pandas as pd
 import gradio as gr
+import plotly.graph_objects as go
 
 # Import your local files
 import database, schemas, moderator
 
-# 1. Initialize Database
+# Initialize Database
 database.init_db()
 
 # --- DASHBOARD LOGIC ---
 def update_dashboard(user, text):
-    # EMOJI ANALYSIS: Quick check for known aggressive emojis
-    bad_emojis = ["🔪", "😡", "👊", "🖕", "🔫", "🤮"]
-    emoji_flag = any(e in text for e in bad_emojis)
-    
-    # AI ANALYSIS
+    # 1. Run AI Analysis
     analysis = moderator.moderator.analyze(text)
     
-    # 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
+    # 2. Save to DB
     db = database.SessionLocal()
     db_comment = database.Comment(
         video_id=1, user=user, text=text,
@@ -35,24 +26,40 @@ def update_dashboard(user, text):
     db.add(db_comment)
     db.commit()
 
-    # Calculate Global Metrics
+    # 3. 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
     
-    # FIX: Safety Score as a raw number (0-100)
-    safety_score = (safe_count / total_count * 100) if total_count > 0 else 100
+    # Safety Score calculation
+    safety_score = int((safe_count / total_count * 100)) if total_count > 0 else 100
     
-    # Toxicity Classification breakdown for the Label component
+    # 4. FIXED CHART LOGIC: Ensure data is never zero if toxic comments exist
     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 visual bars (Confidence score style)
-    chart_data = {k: v / toxic_count if toxic_count > 0 else 0 for k, v in reasons.items()}
+        # If the AI reason isn't in our list, default to Online Harassment so it shows on chart
+        cat = c.flagged_reason if c.flagged_reason in reasons else "Online Harassment"
+        reasons[cat] += 1
+
+    # CREATE REAL DONUT CHART
+    colors = ['#7B68EE', '#FF4500', '#1E90FF', '#FFA500']
+    fig = go.Figure(data=[go.Pie(
+        labels=list(reasons.keys()), 
+        values=list(reasons.values()), 
+        hole=.6,
+        marker_colors=colors,
+        textinfo='label+percent'
+    )])
+    fig.update_layout(
+        showlegend=True, 
+        margin=dict(t=10, b=10, l=10, r=10),
+        height=300,
+        paper_bgcolor='rgba(0,0,0,0)',
+        plot_bgcolor='rgba(0,0,0,0)',
+        font=dict(color="white") if os.getenv("GRADIO_THEME") == "dark" else dict(color="black")
+    )
 
     # 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()
@@ -60,16 +67,15 @@ def update_dashboard(user, text):
     
     db.close()
 
-    status_label = "🔴 High Toxicity" if analysis["is_toxic"] else "🟢 Safe"
+    status_label = "🔴 High Toxicity" if analysis["is_toxic"] else "🟢 NORMAL"
     
-    # FIX: Return raw numbers for gr.Number to avoid rounding errors
     return (
-        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
+        safety_score,      # gr.Number
+        total_count,       # gr.Number
+        toxic_count,       # gr.Number
+        status_label,      # gr.Label
+        fig,               # gr.Plot (Donut Chart)
+        shame_data         # gr.Dataframe
     )
 
 def clear_db():
@@ -77,7 +83,8 @@ def clear_db():
     db.query(database.Comment).delete()
     db.commit()
     db.close()
-    return 100, 0, 0, "🟢 NORMAL", {}, []
+    empty_fig = go.Figure(data=[go.Pie(labels=['No Data'], values=[1], hole=.6)])
+    return 100, 0, 0, "🟢 NORMAL", empty_fig, []
 
 # --- UI LAYOUT ---
 with gr.Blocks(theme=gr.themes.Default(), title="Admin Intelligence Hub") as demo:
@@ -85,14 +92,12 @@ with gr.Blocks(theme=gr.themes.Default(), title="Admin Intelligence Hub") as dem
         gr.Markdown("# 🛡️ Admin Intelligence Hub\n*Real-time threat monitoring and classification*")
         clear_btn = gr.Button("🗑️ Clear Database", variant="stop", size="sm")
     
-    # 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 = gr.Number(label="✅ Safety Score (%)", value=100, precision=0)
 
     with gr.Row():
-        # 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():
@@ -100,16 +105,14 @@ with gr.Blocks(theme=gr.themes.Default(), title="Admin Intelligence Hub") as dem
                 msg_input = gr.Textbox(label="Message Inference", placeholder="Analyze text or emojis...")
                 submit_btn = gr.Button("ANALYZE SIGNAL", variant="primary")
         
-        # Classification & Alerts
         with gr.Column(scale=1):
-            gr.Markdown("### Toxicity Classification")
-            donut_chart = gr.Label(num_top_classes=4, label="Distribution")
+            gr.Markdown("### 📊 Toxicity Classification")
+            donut_chart = gr.Plot(label="Toxicity Distribution") # Changed to gr.Plot
             current_alert = gr.Label(label="Current Threat Level")
 
     gr.Markdown("### 💀 Strategic Insights & History")
     shame_table = gr.Dataframe(headers=["User", "Comment", "Score", "Reason"], interactive=False)
 
-    # Event Triggers
     submit_btn.click(
         update_dashboard, 
         inputs=[user_input, msg_input],