"""
Enhanced components with real ARF integration
(Streamlit-optional, Hugging Face safe)
"""
from __future__ import annotations

import logging
from datetime import datetime
from typing import List, Dict, Any
import time

import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
import numpy as np

logger = logging.getLogger(__name__)

# --- OPTIONAL Streamlit import (HF-safe) ---
try:
    import streamlit as st  # type: ignore
except Exception:
    st = None
    logger.info("Streamlit not available; ui.components running in headless mode.")


# -----------------------------
# Helpers
# -----------------------------
def _require_streamlit() -> bool:
    """Guard to prevent crashes when Streamlit is unavailable."""
    return st is not None


# -----------------------------
# Mock ARF object (demo-safe)
# -----------------------------
class MockHealingIntent:
    def __init__(self, action, component, confidence, status, rag_similarity_score=None):
        self.action = action
        self.component = component
        self.confidence = confidence
        self.status = status
        self.rag_similarity_score = rag_similarity_score
        self.deterministic_id = f"intent_{int(time.time())}"
        self.created_at = time.time()

    def get_execution_summary(self):
        return {
            "intent_id": self.deterministic_id,
            "action": self.action,
            "component": self.component,
            "confidence": self.confidence,
            "status": self.status.value if hasattr(self.status, "value") else self.status,
            "rag_similarity_score": self.rag_similarity_score,
        }


# -----------------------------
# UI Components (SAFE)
# -----------------------------
def create_arf_enhanced_timeline(
    incident_data: Dict[str, Any],
    healing_intents: List[Dict[str, Any]] | None = None,
):
    if not _require_streamlit():
        return

    col1, col2 = st.columns([2, 1])

    with col1:
        st.markdown("### 📈 ARF-Enhanced Incident Timeline")

        events = [
            {"time": "-5m", "event": "📡 Alert Triggered", "phase": "detection", "color": "#FF6B6B"},
            {"time": "-4m", "event": "🧠 ARF Analysis Started", "phase": "analysis", "color": "#4ECDC4"},
            {"time": "-3.5m", "event": "🔍 RAG Similarity Search", "phase": "rag", "color": "#1E90FF"},
            {"time": "-2.5m", "event": "🎯 Pattern Detection", "phase": "pattern", "color": "#9D4EDD"},
            {"time": "-1.5m", "event": "💡 HealingIntent Generated", "phase": "intent", "color": "#FFD166"},
            {"time": "-1m", "event": "⚡ MCP Execution", "phase": "execution", "color": "#06D6A0"},
            {"time": "Now", "event": "✅ Resolution Complete", "phase": "resolution", "color": "#118AB2"},
        ]

        fig = go.Figure()

        fig.add_trace(
            go.Scatter(
                x=list(range(len(events))),
                y=[0] * len(events),
                mode="lines+markers",
                marker=dict(size=16, color=[e["color"] for e in events]),
                line=dict(width=2),
                hovertext=[e["event"] for e in events],
                hoverinfo="text",
            )
        )

        fig.update_layout(
            height=250,
            showlegend=False,
            xaxis=dict(visible=False),
            yaxis=dict(visible=False),
            margin=dict(l=20, r=20, t=20, b=20),
        )

        st.plotly_chart(fig, use_container_width=True)

    with col2:
        st.markdown("### 🎯 ARF Pattern Detection")

        rag_score = (
            healing_intents[0].get("rag_similarity_score", 0.85)
            if healing_intents
            else 0.85
        )

        fig = go.Figure(
            go.Indicator(
                mode="gauge+number",
                value=rag_score * 100,
                gauge={"axis": {"range": [0, 100]}},
                title={"text": "RAG Similarity"},
            )
        )

        fig.update_layout(height=200)
        st.plotly_chart(fig, use_container_width=True)


def create_healing_intent_visualizer(healing_intent: Dict[str, Any]):
    if not _require_streamlit():
        return

    st.markdown("### 💡 ARF HealingIntent")

    confidence = healing_intent.get("confidence", 0.85)

    fig = go.Figure(
        go.Indicator(
            mode="gauge+number",
            value=confidence * 100,
            gauge={"axis": {"range": [0, 100]}},
            title={"text": "Confidence"},
        )
    )

    fig.update_layout(height=180)
    st.plotly_chart(fig, use_container_width=True)

    st.json(healing_intent)


def create_rag_similarity_panel(query: str, similar_incidents: List[Dict[str, Any]]):
    if not _require_streamlit():
        return

    st.markdown("### 🔍 RAG Similarity Search")

    if not similar_incidents:
        st.info("No similar incidents found")
        return

    df = pd.DataFrame(similar_incidents)
    st.dataframe(df, use_container_width=True)


def create_learning_engine_panel(learning_stats: Dict[str, Any]):
    if not _require_streamlit():
        return

    st.markdown("### 🧠 ARF Learning Engine")
    st.json(learning_stats)


def create_execution_mode_toggle(current_mode: str = "advisory") -> str:
    if not _require_streamlit():
        return current_mode

    modes = ["advisory", "approval", "autonomous"]
    return st.selectbox("Execution Mode", modes, index=modes.index(current_mode))