"""
ARF UI Components
Gradio-first, Hugging Face Spaces safe
Compatible with app.py v3.8.0
"""

from __future__ import annotations

import gradio as gr
import plotly.graph_objects as go
from typing import Dict, Any, List


# ============================================================
# HEADER / FOOTER
# ============================================================

def create_header(version: str, enterprise: bool = False):
    badge = "ENTERPRISE EDITION" if enterprise else "OSS EDITION"
    return gr.HTML(
        f"""
        <div style="padding:20px;border-bottom:1px solid #eee">
            <h1>🚀 Agentic Reliability Framework</h1>
            <p><b>Version:</b> {version} · <b>{badge}</b></p>
        </div>
        """
    )


def create_footer():
    return gr.HTML(
        """
        <div style="padding:20px;border-top:1px solid #eee;text-align:center;color:#666">
            ARF © 2025 · Self-Healing Agentic Systems
        </div>
        """
    )


def create_status_bar():
    return gr.HTML(
        """
        <div style="padding:10px;background:#f5f7fa;border-radius:8px">
            ✅ System Online · 🧠 Agentic Core Active · 📦 OSS Mode
        </div>
        """
    )


# ============================================================
# TAB 1 — INCIDENT DEMO
# ============================================================

def create_tab1_incident_demo(scenarios: Dict[str, Any], default: str):
    scenario_dropdown = gr.Dropdown(
        choices=list(scenarios.keys()),
        value=default,
        label="Incident Scenario"
    )

    scenario_description = gr.Markdown("Select a scenario to begin analysis.")
    metrics_display = gr.JSON(label="Live Metrics")
    impact_display = gr.Markdown("### Estimated Business Impact")
    timeline_output = gr.Markdown("Timeline will render here")

    oss_btn = gr.Button("Run OSS Analysis")
    enterprise_btn = gr.Button("Execute Enterprise Healing", variant="primary")
    approval_toggle = gr.Checkbox(label="Require Human Approval", value=True)

    demo_btn = gr.Button("Run Demo")
    approval_display = gr.HTML()
    oss_results_display = gr.JSON(label="OSS Results")
    enterprise_results_display = gr.JSON(label="Enterprise Results")

    return (
        scenario_dropdown,
        scenario_description,
        metrics_display,
        impact_display,
        timeline_output,
        oss_btn,
        enterprise_btn,
        approval_toggle,
        demo_btn,
        approval_display,
        oss_results_display,
        enterprise_results_display,
    )


# ============================================================
# TAB 2 — ROI / BUSINESS IMPACT
# ============================================================

def create_tab2_business_roi(scenarios: Dict[str, Any]):
    dashboard_output = gr.Plot(label="Executive ROI Dashboard")

    roi_scenario_dropdown = gr.Dropdown(
        choices=list(scenarios.keys()),
        label="Scenario"
    )

    monthly_slider = gr.Slider(
        1, 100, value=15, step=1, label="Monthly Incidents"
    )

    team_slider = gr.Slider(
        1, 50, value=5, step=1, label="On-call Team Size"
    )

    calculate_btn = gr.Button("Calculate ROI", variant="primary")
    roi_output = gr.JSON(label="ROI Breakdown")
    roi_chart = gr.Plot(label="ROI Multiplier")

    return (
        dashboard_output,
        roi_scenario_dropdown,
        monthly_slider,
        team_slider,
        calculate_btn,
        roi_output,
        roi_chart,
    )


# ============================================================
# TAB 3 — ENTERPRISE FEATURES
# ============================================================

def create_tab3_enterprise_features():
    license_display = gr.JSON(label="License Status")

    validate_btn = gr.Button("Validate License")
    trial_btn = gr.Button("Start Trial")
    upgrade_btn = gr.Button("Upgrade")

    mcp_mode = gr.Radio(
        ["advisory", "approval", "autonomous"],
        value="advisory",
        label="MCP Execution Mode"
    )

    mcp_mode_info = gr.JSON(label="Mode Details")

    features_table = gr.Dataframe(
        headers=["Feature", "Available"],
        value=[
            ["Autonomous Healing", "Enterprise"],
            ["Audit Trail", "Enterprise"],
            ["Compliance", "Enterprise"],
        ],
        interactive=False
    )

    integrations_table = gr.Dataframe(
        headers=["Integration", "Status"],
        value=[
            ["Kubernetes", "Ready"],
            ["AWS", "Ready"],
            ["GCP", "Ready"],
        ],
        interactive=False
    )

    return (
        license_display,
        validate_btn,
        trial_btn,
        upgrade_btn,
        mcp_mode,
        mcp_mode_info,
        features_table,
        integrations_table,
    )


# ============================================================
# TAB 4 — AUDIT TRAIL
# ============================================================

def create_tab4_audit_trail():
    refresh_btn = gr.Button("Refresh")
    clear_btn = gr.Button("Clear")
    export_btn = gr.Button("Export")

    execution_table = gr.Dataframe(
        headers=["Time", "Scenario", "Mode", "Status", "Savings", "Details"],
        interactive=False
    )

    incident_table = gr.Dataframe(
        headers=["Time", "Component", "Scenario", "Severity", "Status"],
        interactive=False
    )

    export_text = gr.Textbox(
        label="Exported Audit JSON",
        lines=15
    )

    return (
        refresh_btn,
        clear_btn,
        export_btn,
        execution_table,
        incident_table,
        export_text,
    )


# ============================================================
# TAB 5 — LEARNING ENGINE
# ============================================================

def create_tab5_learning_engine():
    learning_graph = gr.Plot(label="Learning Graph")

    graph_type = gr.Dropdown(
        ["patterns", "performance", "confidence"],
        value="patterns",
        label="Graph Type"
    )

    show_labels = gr.Checkbox(value=True, label="Show Labels")

    search_query = gr.Textbox(label="Search Incidents")
    search_btn = gr.Button("Search")
    clear_btn_search = gr.Button("Clear")

    search_results = gr.JSON(label="Search Results")
    stats_display = gr.JSON(label="Learning Stats")
    patterns_display = gr.JSON(label="Detected Patterns")
    performance_display = gr.JSON(label="Performance Metrics")

    return (
        learning_graph,
        graph_type,
        show_labels,
        search_query,
        search_btn,
        clear_btn_search,
        search_results,
        stats_display,
        patterns_display,
        performance_display,
    )