import os
import uvicorn
import pandas as pd
import gradio as gr
import plotly.graph_objects as go
from datetime import datetime

# Import your local files
import database, schemas, moderator

# Initialize Database
database.init_db()

# --- DASHBOARD LOGIC ---
def update_dashboard(user, text):
    # 1. Run AI Analysis
    analysis = moderator.moderator.analyze(text)
    
    # 2. Save to DB
    db = database.SessionLocal()
    db_comment = database.Comment(
        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()

    # 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 = int((safe_count / total_count * 100)) if total_count > 0 else 100
    
    # 4. Chart Logic
    reasons = {"Identity Hate": 0, "Insult": 0, "Online Harassment": 0, "Threat": 0}
    for c in toxic_comments:
        cat = c.flagged_reason if c.flagged_reason in reasons else "Online Harassment"
        reasons[cat] += 1

    # Create 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)')

    # 5. Create the "Visual Block" HTML (Your requested feature)
    current_date = datetime.now().strftime("%m/%d/%Y")
    if analysis["is_toxic"]:
        block_html = f"""
        <div style="background-color: #ffebee; border-radius: 10px; padding: 15px; border-left: 5px solid #f44336; margin-top: 20px;">
            <div style="display: flex; justify-content: space-between; align-items: center;">
                <strong style="color: #333;">👤 {user}</strong>
                <span style="color: #888; font-size: 0.8em;">{current_date}</span>
            </div>
            <p style="color: #d32f2f; font-weight: bold; margin: 10px 0;">⚠️ Comment flagged as Toxic (Confidence: {analysis['score']*100:.1f}%)</p>
            <p style="color: #f44336; font-size: 0.9em;">AI Reason: Classified as {analysis['reason'].upper()} with confidence {analysis['score']:.2f}</p>
        </div>
        """
    else:
        block_html = f"""
        <div style="background-color: #e8f5e9; border-radius: 10px; padding: 15px; border-left: 5px solid #4caf50; margin-top: 20px;">
            <strong style="color: #333;">👤 {user}</strong>
            <p style="color: #2e7d32; margin: 10px 0;">✅ Comment allowed: {text}</p>
        </div>
        """

    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_label = "🔴 High Toxicity" if analysis["is_toxic"] else "🟢 NORMAL"
    
    return safety_score, total_count, toxic_count, status_label, fig, shame_data, block_html

def clear_db():
    db = database.SessionLocal()
    db.query(database.Comment).delete()
    db.commit()
    db.close()
    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:
    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")
    
    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():
        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="Analyze text or emojis...")
                submit_btn = gr.Button("ANALYZE SIGNAL", variant="primary")
            
            # This is where the red block notification will appear
            moderation_alert = gr.HTML() 
        
        with gr.Column(scale=1):
            gr.Markdown("### 📊 Toxicity Classification")
            donut_chart = gr.Plot(label="Toxicity Distribution")
            current_alert = gr.Label(label="Current Threat Level")

    gr.Markdown("### 💀 Strategic Insights & History")
    shame_table = gr.Dataframe(headers=["User", "Comment", "Score", "Reason"], interactive=False)

    submit_btn.click(
        update_dashboard, 
        inputs=[user_input, msg_input], 
        outputs=[safety_score, total_signals, threats_found, current_alert, donut_chart, shame_table, moderation_alert]
    )
    
    clear_btn.click(
        clear_db,
        outputs=[safety_score, total_signals, threats_found, current_alert, donut_chart, shame_table, moderation_alert]
    )

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)