Update app.py

app.py

@@ -1,64 +1,90 @@
 import os
 import uvicorn
-from fastapi import FastAPI, Depends, HTTPException
+import pandas as pd
+from fastapi import FastAPI, Depends
 from fastapi.middleware.cors import CORSMiddleware
-from sqlalchemy.orm import Session
-from typing import List
 import gradio as gr
 
 # Import your local files
-import database
-import schemas
-import moderator
+import database, schemas, moderator
 
-# 1. Initialize Database
+# Initialize Database
 database.init_db()
 
-# 2. Setup FastAPI (for your API endpoints)
-app = FastAPI(title="SafeStream API")
-app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"])
+# --- 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
+    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)
 
-def get_db():
+    # Save to Database
     db = database.SessionLocal()
-    try: yield db
-    finally: db.close()
-
-@app.post("/comments", response_model=schemas.Comment)
-def create_comment(comment: schemas.CommentCreate, db: Session = Depends(get_db)):
-    analysis = moderator.moderator.analyze(comment.text)
     db_comment = database.Comment(
-        video_id=comment.video_id, user=comment.user, text=comment.text,
-        timestamp=comment.timestamp, is_toxic=analysis["is_toxic"],
-        toxicity_score=analysis["score"], flagged_reason=analysis["reason"]
+        video_id=1, user=user, text=text,
+        is_toxic=analysis["is_toxic"],
+        toxicity_score=analysis["score"], 
+        flagged_reason=analysis["reason"]
     )
     db.add(db_comment)
     db.commit()
-    db.refresh(db_comment)
-    return db_comment
+    db.close()
 
-# 3. Setup Gradio UI
-def moderate_chat(user, text):
-    analysis = moderator.moderator.analyze(text)
-    if analysis["is_toxic"]:
-        return f"🚫 BLOCKED: {analysis['reason']} (Score: {analysis['score']:.2f})", "🔴 High Toxicity"
-    return f"✅ Posted: {text}", "🟢 Safe"
+    # Get updated history (Last 5 toxic)
+    db = database.SessionLocal()
+    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
 
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🛡️ SafeStream AI Moderator")
-    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):
-            gr.Markdown("### Live Chat Moderation")
-            user_input = gr.Textbox(label="Username", value="Guest")
-            msg_input = gr.Textbox(label="Type a comment...", placeholder="Try typing something mean or nice!")
-            status_box = gr.Label(label="Moderation Status")
-            output_text = gr.Textbox(label="Chat Feed", interactive=False)
-            submit_btn = gr.Button("Post Comment")
-            submit_btn.click(moderate_chat, inputs=[user_input, msg_input], outputs=[output_text, status_box])
+# --- UI LAYOUT ---
+with gr.Blocks(theme=gr.themes.Soft(), fill_width=True) as demo:
+    gr.Markdown("# 🛡️ SafeStream AI Pro Dashboard")
+    
+    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)
+
+            gr.Markdown("### 💀 The Hall of Shame (Recent Flagged Comments)")
+            shame_table = gr.Dataframe(headers=["User", "Comment", "Score", "Reason"], datatype=["str", "str", "str", "str"])
+
+        # 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"})
+
+    # Event Trigger
+    submit_btn.click(
+        moderate_and_update, 
+        inputs=[user_input, msg_input], 
+        outputs=[output_feed, status_box, shame_table, stats_plot]
+    )
 
-# 4. LAUNCH (The Fix)
-# Instead of mounting, we run Gradio directly on port 7860.
-# Hugging Face will automatically find it.
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)