Update app.py

app.py

@@ -1,8 +1,6 @@
 import os
 import uvicorn
 import pandas as pd
-from fastapi import FastAPI, Depends
-from fastapi.middleware.cors import CORSMiddleware
 import gradio as gr
 
 # Import your local files
@@ -11,22 +9,12 @@ import database, schemas, moderator
 # Initialize Database
 database.init_db()
 
-# --- MODERATION LOGIC ---
-def moderate_and_update(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
+# --- DASHBOARD LOGIC ---
+def update_dashboard(user, text):
+    # 1. Run AI Analysis
     analysis = moderator.moderator.analyze(text)
     
-    # If emoji found, override or 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,
@@ -36,54 +24,77 @@ def moderate_and_update(user, text):
     )
     db.add(db_comment)
     db.commit()
-    db.close()
 
-    # Get updated history (Last 5 toxic)
-    db = database.SessionLocal()
+    # 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
+    
+    # Safety Score calculation: (Safe / Total) * 100
+    safety_score = (safe_count / total_count * 100) if total_count > 0 else 100
+    
+    # Toxicity Classification breakdown
+    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
+    chart_data = {k: v / toxic_count if toxic_count > 0 else 0 for k, v in reasons.items()}
+
+    # Get Hall of Shame Data
     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]
     
-    # Get Stats for Chart
-    total_safe = db.query(database.Comment).filter(database.Comment.is_toxic == False).count()
-    total_toxic = db.query(database.Comment).filter(database.Comment.is_toxic == True).count()
-    stats_df = pd.DataFrame({"Status": ["Safe ✅", "Toxic 🚫"], "Count": [total_safe, total_toxic]})
     db.close()
 
     status = "🔴 High Toxicity" if analysis["is_toxic"] else "🟢 Safe"
-    msg = f"🚫 BLOCKED: {analysis['reason']}" if analysis["is_toxic"] else f"✅ Posted: {text}"
-    
-    return msg, status, shame_data, stats_df
+    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
+    )
 
 # --- UI LAYOUT ---
-with gr.Blocks(theme=gr.themes.Soft(), fill_width=True) as demo:
-    gr.Markdown("# 🛡️ SafeStream AI Pro Dashboard")
+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.Tabs():
-        # TAB 1: LIVE MODERATOR
-        with gr.Tab("Live Chat"):
-            with gr.Row():
-                with gr.Column(scale=2):
-                    gr.HTML('<iframe width="100%" height="400" src="https://www.youtube.com/embed/dQw4w9WgXcQ" frameborder="0" allowfullscreen></iframe>')
-                with gr.Column(scale=1):
-                    user_input = gr.Textbox(label="Username", value="Guest")
-                    msg_input = gr.Textbox(label="Type a comment...", placeholder="Try a mean emoji like 🔪 or text!")
-                    submit_btn = gr.Button("Post Comment", variant="primary")
-                    status_box = gr.Label(label="Current Moderation Status")
-                    output_feed = gr.Textbox(label="Last Action", interactive=False)
+    # Top Row: KPI Cards
+    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")
 
-            gr.Markdown("### 💀 The Hall of Shame (Recent Flagged Comments)")
-            shame_table = gr.Dataframe(headers=["User", "Comment", "Score", "Reason"], datatype=["str", "str", "str", "str"])
+    with gr.Row():
+        # Left: Live Input & Video
+        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...")
+                submit_btn = gr.Button("ANALYZE SIGNAL", variant="primary")
+        
+        # Right: Classification Chart
+        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")
 
-        # TAB 2: ANALYTICS
-        with gr.Tab("Safety Analytics"):
-            gr.Markdown("### Real-time Toxicity Distribution")
-            stats_plot = gr.BarPlot(x="Status", y="Count", title="Community Health Overview", color="Status", color_map={"Safe ✅": "green", "Toxic 🚫": "red"})
+    # Bottom Row: History
+    gr.Markdown("### 💀 Strategic Insights & History")
+    shame_table = gr.Dataframe(headers=["User", "Comment", "Score", "Reason"], interactive=False)
 
-    # Event Trigger
+    # Event Wiring
     submit_btn.click(
-        moderate_and_update, 
+        update_dashboard, 
         inputs=[user_input, msg_input], 
-        outputs=[output_feed, status_box, shame_table, stats_plot]
+        outputs=[safety_score_gauge, total_signals, threats_found, current_alert, donut_chart, shame_table]
     )
 
 if __name__ == "__main__":