views/executive.py

"""Facility Executive persona.

Features:
- KPI dashboard (occupancy, ER avoidance, falls, compliance)
- Risk heatmap across residents
- Financial impact metrics
- Staff efficiency analytics
"""

import streamlit as st
import plotly.express as px
import plotly.graph_objects as go
import pandas as pd

from data.synthetic import (
    get_residents, get_facility_kpis, get_sensor_data, get_staff_metrics,
    get_ed_avoidance_data, get_age_in_place_scores,
)
from components.chatbot import render_chatbot


def render():
    """Render the Facility Executive dashboard."""
    st.title("Executive Intelligence Dashboard")

    kpis = get_facility_kpis()
    residents = get_residents()
    staff = get_staff_metrics()
    sensors = get_sensor_data(hours=48)

    # Top KPI row
    c1, c2, c3, c4, c5 = st.columns(5)
    with c1:
        st.metric("Occupancy", f"{kpis['occupancy_rate']}%", kpis["occupancy_delta"])
    with c2:
        st.metric("ER Transfers", kpis["er_transfers_month"], kpis["er_transfers_delta"], delta_color="inverse")
    with c3:
        st.metric("Falls (Month)", kpis["falls_this_month"], kpis["falls_delta"], delta_color="inverse")
    with c4:
        st.metric("Family Satisfaction", f"{kpis['family_satisfaction']}/5.0", "+0.2")
    with c5:
        st.metric("Compliance", f"{kpis['regulatory_compliance_pct']}%", "+0.3%")

    st.divider()

    tab_ops, tab_risk, tab_ed, tab_aip, tab_finance, tab_staff, tab_chat = st.tabs([
        "Operations", "Risk Overview", "🚑 ED Avoidance",
        "🏠 Age-in-Place", "Financial Impact", "Staff Analytics", "📊 Strategic Advisor",
    ])

    # --- TAB: OPERATIONS ---
    with tab_ops:
        col1, col2 = st.columns(2)
        with col1:
            st.subheader("Mobility Trends (All Residents)")
            fig = px.area(
                sensors, x="Timestamp", y="Mobility_Index",
                title="Aggregate Mobility Index (48h)",
                template="plotly_white",
            )
            fig.update_traces(line_color="#8b5cf6", fillcolor="rgba(139,92,246,0.1)")
            st.plotly_chart(fig, use_container_width=True)

        with col2:
            st.subheader("Social Engagement Trend")
            fig2 = px.bar(
                sensors.tail(24), x="Timestamp", y="Social_Engagement_Hrs",
                title="Communal Hours (Last 24h)",
                template="plotly_white",
                color="Social_Engagement_Hrs",
                color_continuous_scale="Teal",
            )
            st.plotly_chart(fig2, use_container_width=True)

        st.subheader("Key Operational Metrics")
        op_col1, op_col2, op_col3, op_col4 = st.columns(4)
        with op_col1:
            st.metric("Avg Length of Stay", f"{kpis['avg_los_days']} days")
        with op_col2:
            st.metric("Infection Rate", f"{kpis['infection_rate_per_1000']}/1000")
        with op_col3:
            st.metric("Readmission Rate", f"{kpis['readmission_rate_pct']}%")
        with op_col4:
            st.metric("Staff Turnover", f"{kpis['staff_turnover_pct']}%")

    # --- TAB: RISK OVERVIEW ---
    with tab_risk:
        st.subheader("Resident Risk Heatmap")

        risk_data = pd.DataFrame([
            {
                "Resident": r["name"],
                "Room": r["room"],
                "Unit": r["unit"],
                "Risk Level": r["risk_level"],
                "Wellness Score": r["wellness_score"],
                "Mobility": r["mobility_baseline"],
                "Diagnoses": ", ".join(r["diagnoses"][:2]),
            }
            for r in residents
        ])

        # Color-coded risk table
        def highlight_risk(val):
            colors = {"High": "background-color: #fef2f2; color: #dc2626;",
                       "Moderate": "background-color: #fffbeb; color: #d97706;",
                       "Low": "background-color: #f0fdf4; color: #16a34a;"}
            return colors.get(val, "")

        styled = risk_data.style.applymap(highlight_risk, subset=["Risk Level"])
        st.dataframe(styled, use_container_width=True, hide_index=True)

        st.divider()

        # Risk distribution
        col_r1, col_r2 = st.columns(2)
        with col_r1:
            risk_counts = risk_data["Risk Level"].value_counts().reset_index()
            risk_counts.columns = ["Risk Level", "Count"]
            fig_risk = px.pie(
                risk_counts, values="Count", names="Risk Level",
                title="Risk Distribution",
                color="Risk Level",
                color_discrete_map={"High": "#ef4444", "Moderate": "#f59e0b", "Low": "#10b981"},
                template="plotly_white",
            )
            st.plotly_chart(fig_risk, use_container_width=True)

        with col_r2:
            fig_well = px.bar(
                risk_data, x="Resident", y="Wellness Score",
                title="Wellness Scores by Resident",
                color="Wellness Score",
                color_continuous_scale="RdYlGn",
                template="plotly_white",
            )
            fig_well.add_hline(y=85, line_dash="dash", line_color="green", annotation_text="Target")
            st.plotly_chart(fig_well, use_container_width=True)

        # High risk alerts
        high_risk = [r for r in residents if r["risk_level"] == "High"]
        if high_risk:
            st.error(
                f"**{len(high_risk)} High-Risk Resident(s):** "
                + ", ".join(f"{r['name']} (Room {r['room']})" for r in high_risk),
                icon="🚨",
            )

    # --- TAB: ED AVOIDANCE INTELLIGENCE ---
    with tab_ed:
        st.subheader("ED Avoidance Intelligence")
        st.caption(
            "Track emergency department transfers avoided through proactive AI-powered interventions. "
            "Relief from acuity burden with predictive clinical care."
        )

        ed_data = get_ed_avoidance_data()

        # Top metrics
        ed1, ed2, ed3, ed4 = st.columns(4)
        with ed1:
            st.metric("ED Transfers (Month)", ed_data["ed_transfers"][-1], "-5 vs 6mo ago", delta_color="inverse")
        with ed2:
            st.metric("ED Avoidances (Month)", ed_data["ed_avoidances"][-1], "+6 vs 6mo ago")
        with ed3:
            total_savings = sum(ed_data["savings_per_month"])
            st.metric("Total Savings (6mo)", f"${total_savings:,}")
        with ed4:
            avoidance_rate = ed_data["ed_avoidances"][-1] / (ed_data["ed_transfers"][-1] + ed_data["ed_avoidances"][-1]) * 100
            st.metric("Avoidance Rate", f"{avoidance_rate:.0f}%")

        # Charts
        ed_col1, ed_col2 = st.columns(2)
        with ed_col1:
            ed_df = pd.DataFrame({
                "Month": ed_data["months"],
                "ED Transfers": ed_data["ed_transfers"],
                "Avoided": ed_data["ed_avoidances"],
            })
            fig_ed = px.bar(
                ed_df, x="Month", y=["ED Transfers", "Avoided"],
                title="ED Transfers vs. AI-Prevented Transfers",
                barmode="group",
                color_discrete_map={"ED Transfers": "#ef4444", "Avoided": "#10b981"},
                template="plotly_white",
            )
            st.plotly_chart(fig_ed, use_container_width=True)

        with ed_col2:
            fig_sav = px.area(
                x=ed_data["months"], y=ed_data["savings_per_month"],
                title="Monthly ED Avoidance Savings ($)",
                labels={"x": "Month", "y": "Savings ($)"},
                template="plotly_white",
            )
            fig_sav.update_traces(line_color="#10b981", fillcolor="rgba(16,185,129,0.15)")
            st.plotly_chart(fig_sav, use_container_width=True)

        # Top avoidance reasons
        st.markdown("**Top ED Avoidance Categories (6 Months)**")
        for item in ed_data["top_avoidance_reasons"]:
            r_col1, r_col2, r_col3 = st.columns([4, 1, 1])
            with r_col1:
                st.markdown(f"**{item['reason']}**")
            with r_col2:
                st.metric("Count", item["count"], label_visibility="collapsed")
            with r_col3:
                st.metric("Saved", f"${item['savings']:,}", label_visibility="collapsed")

        st.divider()

        # Recent interventions
        st.markdown("**This Month's Interventions**")
        for intervention in ed_data["interventions_this_month"]:
            with st.expander(f"✅ {intervention['resident']} — {intervention['type']} (Saved {intervention['savings']})"):
                st.markdown(intervention["action"])

    # --- TAB: AGE-IN-PLACE ---
    with tab_aip:
        st.subheader("Age-in-Place Stability Index")
        st.caption(
            "Measure how effectively each resident is aging in place. "
            "Higher scores indicate greater stability and longer projected stays."
        )

        aip_scores = get_age_in_place_scores()

        # Summary metrics
        avg_score = sum(s["score"] for s in aip_scores) / len(aip_scores)
        high_risk = [s for s in aip_scores if s["risk_of_transfer"] in ("Moderate", "High")]
        aip_c1, aip_c2, aip_c3 = st.columns(3)
        with aip_c1:
            st.metric("Avg Age-in-Place Score", f"{avg_score:.0f}/100")
        with aip_c2:
            st.metric("At-Risk Residents", len(high_risk), delta_color="inverse")
        with aip_c3:
            avg_los = sum(s["projected_los_months"] for s in aip_scores) / len(aip_scores)
            st.metric("Avg Projected Stay", f"{avg_los:.0f} months")

        st.divider()

        # Resident-by-resident breakdown
        for aip in aip_scores:
            score_color = "#10b981" if aip["score"] >= 75 else "#f59e0b" if aip["score"] >= 50 else "#ef4444"
            trend_icon = {"stable": "➡️", "improving": "📈", "declining": "📉"}.get(aip["trend"], "")

            with st.expander(
                f"{trend_icon} {aip['resident']} (Room {aip['room']}) — "
                f"Score: {aip['score']}/100 | Transfer Risk: {aip['risk_of_transfer']}",
                expanded=(aip["risk_of_transfer"] != "Low"),
            ):
                aip_left, aip_right = st.columns([1, 2])
                with aip_left:
                    st.markdown(
                        f"""<div style="background:white; padding:16px; border-radius:12px;
                        text-align:center; border:2px solid {score_color};">
                        <p style="font-size:2.5rem; font-weight:800; color:{score_color};
                        margin:0; line-height:1;">{aip['score']}</p>
                        <p style="font-size:0.8rem; color:#64748b; margin:4px 0 0;">
                        Age-in-Place Score</p>
                        </div>""",
                        unsafe_allow_html=True,
                    )
                    st.caption(f"Projected Stay: {aip['projected_los_months']} months")

                with aip_right:
                    # Factor bar chart
                    factor_df = pd.DataFrame(
                        list(aip["factors"].items()),
                        columns=["Factor", "Score"],
                    )
                    fig_f = px.bar(
                        factor_df, x="Score", y="Factor", orientation="h",
                        color="Score", color_continuous_scale="RdYlGn",
                        range_x=[0, 100], template="plotly_white",
                    )
                    fig_f.update_layout(height=250, showlegend=False, margin=dict(l=0, r=0, t=10, b=10))
                    st.plotly_chart(fig_f, use_container_width=True)

                st.info(f"**Recommendation:** {aip['recommendation']}")

    # --- TAB: FINANCIAL IMPACT ---
    with tab_finance:
        st.subheader("AI-Driven Financial Impact")

        fc1, fc2, fc3 = st.columns(3)
        with fc1:
            st.metric("ER Avoidance Savings", f"${kpis['er_avoidance_savings']:,}", "This Month")
        with fc2:
            st.metric("Projected Annual Savings", f"${kpis['er_avoidance_savings'] * 12:,}")
        with fc3:
            st.metric("Cost per Resident/Day Reduction", "$12.40", "-8%")

        st.divider()

        # Monthly savings trend
        months = ["Oct", "Nov", "Dec", "Jan", "Feb", "Mar"]
        savings = [12200, 14500, 15800, 16900, 17600, kpis["er_avoidance_savings"]]
        fig_sav = px.bar(
            x=months, y=savings,
            title="Monthly ER Avoidance Savings ($)",
            labels={"x": "Month", "y": "Savings ($)"},
            template="plotly_white",
            color=savings,
            color_continuous_scale="Greens",
        )
        st.plotly_chart(fig_sav, use_container_width=True)

    # --- TAB: STAFF ANALYTICS ---
    with tab_staff:
        st.subheader("Staff Efficiency & Burnout Metrics")

        sc1, sc2, sc3, sc4 = st.columns(4)
        with sc1:
            st.metric("Staff on Shift", staff["total_staff_on_shift"])
        with sc2:
            st.metric("Avg Doc Time", f"{staff['avg_documentation_time_min']} min", staff["avg_documentation_time_delta"])
        with sc3:
            st.metric("Paperwork Saved (Week)", f"{staff['paperwork_mins_saved_week']} min")
        with sc4:
            st.metric("Shift Satisfaction", f"{staff['shift_satisfaction_avg']}/5.0")

        # Task automation gauge
        completion_pct = staff["tasks_auto_completed"] / staff["tasks_total"] * 100
        fig_gauge = go.Figure(go.Indicator(
            mode="gauge+number+delta",
            value=completion_pct,
            title={"text": "Task Automation Rate"},
            delta={"reference": 65},
            gauge={
                "axis": {"range": [0, 100]},
                "bar": {"color": "#3b82f6"},
                "steps": [
                    {"range": [0, 50], "color": "#fee2e2"},
                    {"range": [50, 75], "color": "#fef3c7"},
                    {"range": [75, 100], "color": "#dcfce7"},
                ],
                "threshold": {"line": {"color": "red", "width": 3}, "thickness": 0.75, "value": 90},
            },
        ))
        fig_gauge.update_layout(height=300, template="plotly_white")
        st.plotly_chart(fig_gauge, use_container_width=True)

    # --- TAB: STRATEGIC ADVISOR CHATBOT ---
    with tab_chat:
        st.subheader("Strategic Advisor")
        st.caption(
            "AI-powered executive intelligence. Ask about occupancy, financial impact, "
            "staffing efficiency, fall prevention ROI, compliance, and family satisfaction."
        )
        render_chatbot(persona="executive")