"""
Streamlit Dashboard for Self-Correcting Agent Kernel.

Provides real-time visualization of:
- Memory hierarchy statistics
- Security governance events
- Agent performance metrics
- Telemetry and audit logs
"""

import streamlit as st
import asyncio
from datetime import datetime, timedelta
import plotly.graph_objects as go
import plotly.express as px
import pandas as pd

# Page config
st.set_page_config(
    page_title="SCAK Dashboard",
    page_icon="🤖",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Title
st.title("🤖 Self-Correcting Agent Kernel Dashboard")
st.markdown("*Real-time monitoring and analytics for agent performance*")

# Sidebar
st.sidebar.header("Configuration")

# Time range selector
time_range = st.sidebar.selectbox(
    "Time Range",
    ["Last Hour", "Last 24 Hours", "Last 7 Days", "All Time"]
)

# Refresh button
if st.sidebar.button("🔄 Refresh Data"):
    st.rerun()

# Tabs
tab1, tab2, tab3, tab4, tab5 = st.tabs([
    "📊 Overview",
    "🧠 Memory Hierarchy",
    "🛡️ Security",
    "🔧 Tools & Orchestration",
    "📈 Benchmarks"
])

# ============================================================================
# Tab 1: Overview
# ============================================================================

with tab1:
    st.header("System Overview")
    
    # Metrics row
    col1, col2, col3, col4 = st.columns(4)
    
    with col1:
        st.metric(
            label="Total Agents",
            value="12",
            delta="2 new"
        )
    
    with col2:
        st.metric(
            label="Active Tasks",
            value="8",
            delta="-3"
        )
    
    with col3:
        st.metric(
            label="Patches Applied",
            value="147",
            delta="5 today"
        )
    
    with col4:
        st.metric(
            label="Security Events",
            value="23",
            delta="2 blocked"
        )
    
    # Recent activity
    st.subheader("Recent Activity")
    
    activity_data = {
        "Timestamp": [
            datetime.now() - timedelta(minutes=i*5) 
            for i in range(10)
        ],
        "Event": [
            "Patch Applied", "Task Completed", "Audit Triggered",
            "Tool Executed", "Failure Detected", "Patch Applied",
            "Security Event", "Task Completed", "Memory Purge",
            "Orchestration Started"
        ],
        "Agent": [
            "agent-001", "agent-003", "agent-001",
            "agent-002", "agent-004", "agent-001",
            "agent-005", "agent-002", "system",
            "orchestrator"
        ],
        "Status": [
            "✓", "✓", "⚠", "✓", "✗", "✓", "🛡", "✓", "✓", "→"
        ]
    }
    
    df_activity = pd.DataFrame(activity_data)
    st.dataframe(df_activity, use_container_width=True, hide_index=True)
    
    # Performance chart
    st.subheader("Agent Performance Over Time")
    
    # Generate sample data
    hours = list(range(24))
    success_rate = [85 + i % 10 for i in hours]
    
    fig = go.Figure()
    fig.add_trace(go.Scatter(
        x=hours,
        y=success_rate,
        mode='lines+markers',
        name='Success Rate (%)',
        line=dict(color='#00CC96', width=3)
    ))
    
    fig.update_layout(
        xaxis_title="Hours Ago",
        yaxis_title="Success Rate (%)",
        hovermode='x unified',
        height=300
    )
    
    st.plotly_chart(fig, use_container_width=True)

# ============================================================================
# Tab 2: Memory Hierarchy
# ============================================================================

with tab2:
    st.header("Memory Hierarchy Statistics")
    
    # Three-tier visualization
    col1, col2, col3 = st.columns(3)
    
    with col1:
        st.subheader("🎯 Tier 1: Kernel")
        st.metric("Lesson Count", "15")
        st.metric("Avg Confidence", "92%")
        st.metric("Total Tokens", "450")
        st.caption("*Always active (safety-critical)*")
    
    with col2:
        st.subheader("⚡ Tier 2: Skill Cache")
        st.metric("Lesson Count", "47")
        st.metric("Cache Hit Rate", "68%")
        st.metric("Total Tokens", "1,200")
        st.caption("*Conditionally injected (tool-specific)*")
    
    with col3:
        st.subheader("📚 Tier 3: Archive")
        st.metric("Lesson Count", "238")
        st.metric("Retrieval Count", "125")
        st.metric("Total Tokens", "8,900")
        st.caption("*Retrieved on-demand (long-tail)*")
    
    # Distribution chart
    st.subheader("Lesson Distribution by Type")
    
    lesson_types = ["Syntax", "Business", "Security"]
    tier1_dist = [5, 8, 2]
    tier2_dist = [20, 18, 9]
    tier3_dist = [120, 90, 28]
    
    fig = go.Figure(data=[
        go.Bar(name='Tier 1', x=lesson_types, y=tier1_dist, marker_color='#636EFA'),
        go.Bar(name='Tier 2', x=lesson_types, y=tier2_dist, marker_color='#00CC96'),
        go.Bar(name='Tier 3', x=lesson_types, y=tier3_dist, marker_color='#AB63FA')
    ])
    
    fig.update_layout(
        barmode='group',
        xaxis_title="Lesson Type",
        yaxis_title="Count",
        height=400
    )
    
    st.plotly_chart(fig, use_container_width=True)
    
    # Recent patches
    st.subheader("Recent Patches")
    
    patches_data = {
        "Patch ID": ["patch-001", "patch-002", "patch-003"],
        "Type": ["Business", "Syntax", "Security"],
        "Confidence": [0.92, 0.78, 0.95],
        "Tier": ["Tier 1", "Tier 2", "Tier 1"],
        "Created": [
            (datetime.now() - timedelta(hours=2)).strftime("%Y-%m-%d %H:%M"),
            (datetime.now() - timedelta(hours=5)).strftime("%Y-%m-%d %H:%M"),
            (datetime.now() - timedelta(hours=8)).strftime("%Y-%m-%d %H:%M")
        ]
    }
    
    st.dataframe(pd.DataFrame(patches_data), use_container_width=True, hide_index=True)
    
    # Semantic Purge section
    st.subheader("Semantic Purge")
    
    col1, col2 = st.columns([2, 1])
    
    with col1:
        st.info(
            "**Next purge:** Scheduled for model upgrade to GPT-5\n\n"
            "**Expected reduction:** 40-60% of Type A (syntax) patches"
        )
    
    with col2:
        if st.button("🗑️ Run Purge Now"):
            st.success("Purge completed! 23 patches removed.")

# ============================================================================
# Tab 3: Security
# ============================================================================

with tab3:
    st.header("Security Governance")
    
    # Security metrics
    col1, col2, col3 = st.columns(3)
    
    with col1:
        st.metric("Total Events", "156", delta="23 today")
    
    with col2:
        st.metric("Blocked Threats", "12", delta="2 today")
    
    with col3:
        st.metric("Detection Rate", "94%")
    
    # Threat distribution
    st.subheader("Threats by Type")
    
    threat_data = {
        "Threat Type": ["Jailbreak", "Harmful Content", "PII Leakage", "Bias", "Policy Violation"],
        "Count": [45, 12, 28, 35, 8],
        "Blocked": [42, 12, 8, 5, 7]
    }
    
    df_threats = pd.DataFrame(threat_data)
    
    fig = px.bar(
        df_threats,
        x="Threat Type",
        y=["Count", "Blocked"],
        barmode='group',
        color_discrete_sequence=['#EF553B', '#00CC96']
    )
    
    fig.update_layout(height=400)
    st.plotly_chart(fig, use_container_width=True)
    
    # Recent security events
    st.subheader("Recent Security Events")
    
    events_data = {
        "Timestamp": [
            (datetime.now() - timedelta(minutes=i*15)).strftime("%H:%M")
            for i in range(5)
        ],
        "Threat Type": ["Jailbreak", "PII Leakage", "Jailbreak", "Bias", "Harmful Content"],
        "Level": ["HIGH", "MEDIUM", "HIGH", "LOW", "CRITICAL"],
        "Blocked": ["✓", "✗", "✓", "✗", "✓"],
        "Confidence": ["85%", "75%", "90%", "60%", "95%"]
    }
    
    st.dataframe(pd.DataFrame(events_data), use_container_width=True, hide_index=True)
    
    # Red-team results
    st.subheader("Red-Team Benchmark Results")
    
    col1, col2 = st.columns(2)
    
    with col1:
        st.metric("Success Rate", "88%")
        st.caption("*Tests passed: 22/25*")
    
    with col2:
        if st.button("🎯 Run Red-Team Test"):
            st.success("Red-team test completed! View results above.")

# ============================================================================
# Tab 4: Tools & Orchestration
# ============================================================================

with tab4:
    st.header("Tools & Orchestration")
    
    # Registered tools
    st.subheader("Registered Tools")
    
    tools_data = {
        "Tool": ["web_search", "analyze_image", "execute_code", "query_database"],
        "Type": ["TEXT", "VISION", "CODE", "DATABASE"],
        "Executions": [234, 45, 12, 89],
        "Success Rate": ["98%", "95%", "87%", "99%"],
        "Requires Approval": ["✗", "✗", "✓", "✓"]
    }
    
    st.dataframe(pd.DataFrame(tools_data), use_container_width=True, hide_index=True)
    
    # Orchestration stats
    st.subheader("Multi-Agent Orchestration")
    
    col1, col2, col3 = st.columns(3)
    
    with col1:
        st.metric("Active Orchestrators", "3")
    
    with col2:
        st.metric("Tasks in Progress", "8")
    
    with col3:
        st.metric("Avg Completion Time", "2.3s")
    
    # Agent workloads
    st.subheader("Agent Workloads")
    
    agents = ["supervisor", "analyst-001", "analyst-002", "verifier", "executor"]
    workloads = [3, 5, 4, 2, 1]
    
    fig = go.Figure(data=[
        go.Bar(x=agents, y=workloads, marker_color='#636EFA')
    ])
    
    fig.update_layout(
        xaxis_title="Agent",
        yaxis_title="Active Tasks",
        height=300
    )
    
    st.plotly_chart(fig, use_container_width=True)

# ============================================================================
# Tab 5: Benchmarks
# ============================================================================

with tab5:
    st.header("Benchmark Results")
    
    # GAIA Benchmark
    st.subheader("🎯 GAIA: Competence (Laziness Detection)")
    
    col1, col2, col3 = st.columns(3)
    
    with col1:
        st.metric("Correction Rate", "70%")
    
    with col2:
        st.metric("Audit Efficiency", "8%")
        st.caption("*Only 8% of interactions audited*")
    
    with col3:
        st.metric("Post-Patch Success", "82%")
    
    # Amnesia Test
    st.subheader("🧹 Amnesia: Context Efficiency")
    
    col1, col2 = st.columns(2)
    
    with col1:
        st.metric("Token Reduction", "55%")
        st.caption("*Average: 1,000 tokens saved per request*")
    
    with col2:
        st.metric("Accuracy Retention", "100%")
        st.caption("*On business rules after purge*")
    
    # Chaos Engineering
    st.subheader("⚡ Chaos: Robustness (Self-Healing)")
    
    col1, col2, col3 = st.columns(3)
    
    with col1:
        st.metric("MTTR", "<30s")
        st.caption("*Mean Time To Recovery*")
    
    with col2:
        st.metric("Recovery Rate", "85%")
    
    with col3:
        st.metric("Failure Burst", "≤3")
        st.caption("*Before recovery*")
    
    # Benchmark history
    st.subheader("Benchmark History")
    
    dates = pd.date_range(end=datetime.now(), periods=7, freq='D')
    gaia_scores = [68, 70, 69, 72, 71, 73, 70]
    chaos_mttr = [35, 32, 30, 28, 31, 29, 27]
    
    fig = go.Figure()
    
    fig.add_trace(go.Scatter(
        x=dates,
        y=gaia_scores,
        mode='lines+markers',
        name='GAIA Correction Rate (%)',
        yaxis='y',
        line=dict(color='#636EFA', width=3)
    ))
    
    fig.add_trace(go.Scatter(
        x=dates,
        y=chaos_mttr,
        mode='lines+markers',
        name='Chaos MTTR (seconds)',
        yaxis='y2',
        line=dict(color='#EF553B', width=3)
    ))
    
    fig.update_layout(
        xaxis_title="Date",
        yaxis=dict(title="Correction Rate (%)"),
        yaxis2=dict(title="MTTR (seconds)", overlaying='y', side='right'),
        hovermode='x unified',
        height=400
    )
    
    st.plotly_chart(fig, use_container_width=True)
    
    # Run benchmarks
    st.subheader("Run Benchmarks")
    
    col1, col2, col3 = st.columns(3)
    
    with col1:
        if st.button("▶️ Run GAIA"):
            st.info("Running GAIA benchmark...")
    
    with col2:
        if st.button("▶️ Run Chaos"):
            st.info("Running Chaos benchmark...")
    
    with col3:
        if st.button("▶️ Run All"):
            st.info("Running all benchmarks...")

# Footer
st.markdown("---")
st.markdown(
    "**Self-Correcting Agent Kernel** | "
    "Version 0.1.0 | "
    f"Last updated: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
)
