"""
CXInsights Dashboard - Main Application
Rich visualization dashboard for call analysis results.
Following Beyond Brand Identity Guidelines v1.0
"""

import sys
from pathlib import Path
from datetime import datetime

# Add parent directory to path for imports
sys.path.insert(0, str(Path(__file__).parent))

import streamlit as st
import pandas as pd

from config import COLORS, apply_custom_css
from data_loader import (
    load_batch_data,
    get_available_batches,
    calculate_kpis,
    aggregate_drivers,
)
from components import (
    render_kpi_cards,
    render_outcome_chart,
    render_driver_analysis,
    render_driver_detail,
    render_call_explorer,
    render_agent_performance,
    render_fcr_analysis,
    render_churn_risk_analysis,
    render_driver_correlation_heatmap,
    render_driver_outcome_heatmap,
    render_rca_sankey,
    render_outcome_deep_dive,
)
from exports import render_export_section

# =============================================================================
# PAGE CONFIG
# =============================================================================

st.set_page_config(
    page_title="CXInsights Dashboard | Beyond",
    page_icon="📊",
    layout="wide",
    initial_sidebar_state="expanded",
)

# Apply Beyond brand CSS
apply_custom_css()


# =============================================================================
# MAIN APP
# =============================================================================

def main():
    """Main dashboard application."""

    # -------------------------------------------------------------------------
    # SIDEBAR
    # -------------------------------------------------------------------------
    with st.sidebar:
        # Logo/Brand
        st.markdown(
            f"""
            <div style="padding: 1rem 0; margin-bottom: 1rem;">
                <span style="font-size: 24px; font-weight: 700; color: {COLORS['black']};">
                    beyond
                </span>
                <sup style="font-size: 12px; color: {COLORS['blue']};">cx</sup>
                <div style="font-size: 12px; color: {COLORS['grey']}; margin-top: 4px;">
                    CXInsights Dashboard
                </div>
            </div>
            """,
            unsafe_allow_html=True,
        )

        st.markdown("---")

        # Batch selector
        data_dir = Path(__file__).parent.parent / "data" / "output"

        batches = get_available_batches(data_dir)

        if not batches:
            st.error("No batch data found.")
            st.markdown(
                "Run the pipeline first:\n"
                "```bash\n"
                "python cli.py run <batch_id> -i <audio_dir>\n"
                "```"
            )
            st.stop()

        selected_batch = st.selectbox(
            "Select Batch",
            batches,
            index=len(batches) - 1,  # Most recent
            help="Select a completed analysis batch to visualize",
        )

        st.markdown("---")

        # Navigation
        st.markdown("### Navigation")
        page = st.radio(
            "Section",
            [
                "📊 Overview",
                "📈 Outcomes",
                "😞 Poor CX Analysis",
                "🎯 FCR Analysis",
                "⚠️ Churn Risk",
                "👤 Agent Performance",
                "🔍 Call Explorer",
                "📥 Export Insights",
            ],
            label_visibility="collapsed",
        )

        st.markdown("---")

        # Metadata
        st.markdown(
            f"""
            <div style="font-size: 11px; color: {COLORS['grey']};">
                <strong>Last updated:</strong><br>
                {datetime.now().strftime('%Y-%m-%d %H:%M')}<br><br>
                <strong>Powered by:</strong><br>
                Beyond CXInsights v1.0
            </div>
            """,
            unsafe_allow_html=True,
        )

    # -------------------------------------------------------------------------
    # LOAD DATA
    # -------------------------------------------------------------------------
    batch_path = data_dir / selected_batch
    batch_data = load_batch_data(batch_path)

    if batch_data is None:
        st.error(f"Failed to load batch: {selected_batch}")
        st.stop()

    summary = batch_data["summary"]
    analyses = batch_data["analyses"]

    # -------------------------------------------------------------------------
    # HEADER
    # -------------------------------------------------------------------------
    st.markdown(
        f"""
        <h1 style="margin-bottom: 0.25rem;">📊 CXInsights Dashboard</h1>
        <p style="color: {COLORS['grey']}; margin-bottom: 2rem;">
            <strong>Batch:</strong> {selected_batch} &nbsp;|&nbsp;
            <strong>Calls:</strong> {summary['summary']['total_calls']} &nbsp;|&nbsp;
            <strong>Generated:</strong> {summary.get('generated_at', 'N/A')[:10]}
        </p>
        """,
        unsafe_allow_html=True,
    )

    # -------------------------------------------------------------------------
    # PAGE ROUTING
    # -------------------------------------------------------------------------
    if page == "📊 Overview":
        render_overview_page(summary, analyses)

    elif page == "📈 Outcomes":
        render_outcomes_page(summary, analyses)

    elif page == "😞 Poor CX Analysis":
        render_poor_cx_page(summary, analyses)

    elif page == "🎯 FCR Analysis":
        render_fcr_page(summary, analyses)

    elif page == "⚠️ Churn Risk":
        render_churn_page(summary, analyses)

    elif page == "👤 Agent Performance":
        render_agent_page(analyses)

    elif page == "🔍 Call Explorer":
        render_call_explorer(analyses)

    elif page == "📥 Export Insights":
        render_export_page(summary, analyses, selected_batch)


# =============================================================================
# PAGE RENDERS
# =============================================================================

def render_overview_page(summary: dict, analyses: list[dict]):
    """Render overview page with executive summary."""

    # KPI Cards
    render_kpi_cards(summary, analyses)

    st.markdown("---")

    # Two column layout
    col1, col2 = st.columns(2)

    with col1:
        st.markdown("### Call Outcomes Distribution")
        render_outcome_chart(summary, height=350)

    with col2:
        st.markdown("### Top Poor CX Drivers")
        render_driver_analysis(summary, "poor_cx", limit=5)

    st.markdown("---")

    # Second row
    col1, col2 = st.columns(2)

    with col1:
        st.markdown("### First Call Resolution")
        render_fcr_analysis(analyses, compact=True)

    with col2:
        st.markdown("### Churn Risk Distribution")
        render_churn_risk_analysis(analyses, compact=True)

    # Executive Summary Box
    st.markdown("---")
    st.markdown("### Executive Summary")

    kpis = calculate_kpis(summary, analyses)

    # Generate insights
    insights = []

    if kpis["poor_cx_rate"] > 30:
        insights.append(
            f"⚠️ **High Poor CX Rate:** {kpis['poor_cx_rate']:.1f}% of calls show "
            f"customer experience issues requiring attention."
        )

    if kpis["churn_risk_rate"] > 20:
        insights.append(
            f"⚠️ **Elevated Churn Risk:** {kpis['churn_risk_rate']:.1f}% of customers "
            f"show elevated churn risk signals."
        )

    if kpis["fcr_rate"] < 70:
        insights.append(
            f"📉 **FCR Below Target:** First call resolution at {kpis['fcr_rate']:.1f}% "
            f"suggests process improvement opportunities."
        )

    top_drivers = summary.get("poor_cx", {}).get("top_drivers", [])
    if top_drivers:
        top = top_drivers[0]
        insights.append(
            f"🔍 **Top Driver:** {top['driver_code']} detected in "
            f"{top['occurrences']} calls ({top.get('call_rate', 0)*100:.0f}% of total)."
        )

    if insights:
        for insight in insights:
            st.markdown(insight)
    else:
        st.success("✅ No critical issues detected. Performance within expected parameters.")

    st.caption(
        f"Source: CXInsights Analysis | Generated: {summary.get('generated_at', 'N/A')}"
    )


def render_outcomes_page(summary: dict, analyses: list[dict]):
    """Render detailed outcome analysis page."""

    st.markdown("## 📈 Outcome Analysis")
    st.markdown(
        "Understanding call outcomes helps identify resolution patterns and opportunities."
    )

    st.markdown("---")

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

    with col1:
        render_outcome_chart(summary, height=450)

    with col2:
        st.markdown("### Outcome Breakdown")
        outcomes = summary.get("outcomes", {})
        total = sum(outcomes.values())

        for outcome, count in sorted(outcomes.items(), key=lambda x: -x[1]):
            pct = (count / total * 100) if total > 0 else 0
            st.metric(
                label=outcome,
                value=f"{count}",
                delta=f"{pct:.1f}%",
            )

    st.markdown("---")

    # Calls by outcome table
    st.markdown("### Calls by Outcome")

    outcome_filter = st.multiselect(
        "Filter outcomes",
        list(summary.get("outcomes", {}).keys()),
        default=list(summary.get("outcomes", {}).keys()),
    )

    filtered = [a for a in analyses if a.get("outcome") in outcome_filter]

    if filtered:
        df = pd.DataFrame([
            {
                "Call ID": a["call_id"],
                "Outcome": a["outcome"],
                "FCR Status": a.get("fcr_status", "N/A"),
                "Churn Risk": a.get("churn_risk", "N/A"),
                "Agent": a.get("agent_classification", "N/A"),
                "CX Issues": len(a.get("poor_cx_drivers", [])),
            }
            for a in filtered
        ])
        st.dataframe(df, use_container_width=True, hide_index=True)
    else:
        st.info("No calls match the selected filters.")

    # ---------------------------------------------------------------------
    # DEEP DIVE SECTION
    # ---------------------------------------------------------------------
    st.markdown("---")
    st.markdown("## Deep Dive: Outcome Analysis")

    outcomes_list = list(summary.get("outcomes", {}).keys())
    if outcomes_list:
        # Default to the most problematic outcome (not RESOLVED/POSITIVE)
        problematic = [o for o in outcomes_list if "UNRESOLVED" in o or "COMPLAINT" in o]
        default_idx = outcomes_list.index(problematic[0]) if problematic else 0

        selected_outcome = st.selectbox(
            "Select an outcome to analyze in depth",
            outcomes_list,
            index=default_idx,
            help="Choose an outcome to see root causes, driver correlation, and duration analysis.",
        )

        render_outcome_deep_dive(analyses, selected_outcome)


def render_poor_cx_page(summary: dict, analyses: list[dict]):
    """Render detailed Poor CX analysis page."""

    st.markdown("## 😞 Poor CX Driver Analysis")
    st.markdown(
        "Root cause analysis of customer experience issues detected across calls."
    )

    st.markdown("---")

    # Summary metrics
    poor_cx_data = summary.get("poor_cx", {})
    total_drivers = poor_cx_data.get("total_drivers_found", 0)
    unique_drivers = len(poor_cx_data.get("top_drivers", []))

    col1, col2 = st.columns(2)
    with col1:
        st.metric("Total Driver Instances", total_drivers)
    with col2:
        st.metric("Unique Driver Types", unique_drivers)

    st.markdown("---")

    # RCA Sankey Diagram
    st.markdown("### Root Cause Analysis Flow")
    st.markdown(
        "Visual flow showing how Poor CX drivers lead to outcomes and churn risk. "
        "Wider bands indicate more frequent paths."
    )
    render_rca_sankey(analyses)

    st.markdown("---")

    # Driver chart
    st.markdown("### Driver Frequency")
    render_driver_analysis(summary, "poor_cx", limit=None)

    st.markdown("---")

    # Correlation heatmaps
    st.markdown("### Driver Correlation Analysis")
    st.markdown(
        "Identify patterns where certain drivers frequently appear together "
        "(e.g., 'LONG_WAIT' always with 'POOR_EMPATHY')."
    )

    tab1, tab2 = st.tabs(["Driver Co-occurrence", "Driver by Outcome"])

    with tab1:
        render_driver_correlation_heatmap(analyses, "poor_cx_drivers")

    with tab2:
        render_driver_outcome_heatmap(analyses)

    st.markdown("---")

    # Detailed evidence explorer
    st.markdown("### Driver Evidence Explorer")
    render_driver_detail(analyses, "poor_cx_drivers")


def render_fcr_page(summary: dict, analyses: list[dict]):
    """Render FCR analysis page."""

    st.markdown("## 🎯 First Call Resolution Analysis")
    st.markdown(
        "Analyzing resolution efficiency and identifying callbacks drivers."
    )

    st.markdown("---")

    render_fcr_analysis(analyses, compact=False)

    st.markdown("---")

    # FCR failure drivers
    st.markdown("### FCR Failure Root Causes")

    fcr_drivers = aggregate_drivers(analyses, "fcr_failure_drivers")

    if fcr_drivers:
        df = pd.DataFrame([
            {
                "Driver": code,
                "Instances": data["count"],
                "Calls Affected": data["call_count"],
                "Avg Confidence": f"{data['avg_confidence']:.0%}",
            }
            for code, data in sorted(fcr_drivers.items(), key=lambda x: -x[1]["count"])
        ])
        st.dataframe(df, use_container_width=True, hide_index=True)

        st.markdown("---")

        # Evidence
        st.markdown("### Evidence & Recommendations")
        render_driver_detail(analyses, "fcr_failure_drivers")
    else:
        st.success("✅ No FCR failures detected. Excellent first-call resolution!")


def render_churn_page(summary: dict, analyses: list[dict]):
    """Render churn risk analysis page."""

    st.markdown("## ⚠️ Churn Risk Analysis")
    st.markdown(
        "Identifying customers at risk of churning based on conversation signals."
    )

    st.markdown("---")

    render_churn_risk_analysis(analyses, compact=False)

    st.markdown("---")

    # High risk calls
    st.markdown("### High Risk Customer Calls")

    high_risk = [
        a for a in analyses
        if a.get("churn_risk") in ["HIGH", "AT_RISK"]
    ]

    if high_risk:
        st.warning(
            f"⚠️ {len(high_risk)} calls show elevated churn risk requiring follow-up."
        )

        for analysis in high_risk:
            with st.expander(
                f"📞 {analysis['call_id']} — Risk: {analysis.get('churn_risk', 'N/A')}"
            ):
                st.markdown(f"**Outcome:** {analysis.get('outcome', 'N/A')}")

                drivers = analysis.get("churn_risk_drivers", [])
                if drivers:
                    st.markdown("**Risk Drivers:**")
                    for d in drivers:
                        st.markdown(
                            f"- **{d.get('driver_code')}** "
                            f"({d.get('confidence', 0):.0%}): "
                            f"{d.get('reasoning', 'N/A')}"
                        )

                        if d.get("corrective_action"):
                            st.success(f"Action: {d['corrective_action']}")
    else:
        st.success("✅ No high churn risk calls detected.")


def render_agent_page(analyses: list[dict]):
    """Render agent performance page."""

    st.markdown("## 👤 Agent Performance Analysis")
    st.markdown(
        "Evaluating agent skills and identifying coaching opportunities."
    )

    st.markdown("---")

    render_agent_performance(analyses)


def render_export_page(summary: dict, analyses: list[dict], batch_id: str):
    """Render export insights page."""

    st.markdown("## 📥 Export Insights")
    st.markdown(
        "Download analysis results in multiple formats for reporting and integration."
    )

    st.markdown("---")

    render_export_section(summary, analyses, batch_id)


# =============================================================================
# RUN
# =============================================================================

if __name__ == "__main__":
    main()