app.py

import streamlit as st
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from ml_engine import CMSMLEngine
from agent_graph import build_robust_graph, save_graph_image
import os
import time
import io
from dotenv import load_dotenv
import importlib
import ml_engine as ml_module

# Load Env
load_dotenv()

# Page Configuration
st.set_page_config(
    page_title="Temple Health | Unified Intelligence Hub",
    page_icon="🏥",
    layout="wide",
    initial_sidebar_state="expanded"
)

# Custom CSS for Premium Design (Executive Edition)
st.markdown("""

<style>

    @import url('https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap');

    @import url('https://fonts.googleapis.com/css2?family=Outfit:wght@400;500;600;700&display=swap');

    

    html, body {

        font-family: 'Inter', sans-serif;

        color: #1e293b;

    }



    /* Light Sidebar Theme */

    [data-testid="stSidebar"] {

        background-color: #ffffff !important;

        border-right: 1px solid #e2e8f0;

        color: #1e293b;

    }

    

    [data-testid="stSidebar"] stMarkdown, [data-testid="stSidebar"] p {

        color: #1e293b !important;

    }



    [data-testid="stSidebar"] .stRadio div[role="radiogroup"] label {

        color: #1e293b !important;

    }

    

    h1, h2, h3, h4, .metric-label, .section-title {

        font-family: 'Outfit', sans-serif;

    }

    

    .stApp { 

        background: #ffffff;

    }



    /* Executive Top KPI Cards */

    .kpi-container {

        display: flex;

        gap: 20px;

        margin-bottom: 30px;

    }

    .kpi-card {

        flex: 1;

        background: #ffffff;

        border-radius: 12px;

        padding: 20px;

        border: 1px solid #e2e8f0;

        box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.05);

        position: relative;

        overflow: hidden;

    }

    .kpi-card::before {

        content: '';

        position: absolute;

        top: 0;

        left: 0;

        width: 100%;

        height: 4px;

    }

    .kpi-red::before { background: #dc2626; }

    .kpi-green::before { background: #10b981; }

    .kpi-orange::before { background: #f59e0b; }

    .kpi-gray::before { background: #64748b; }



    .kpi-label { font-size: 0.8rem; color: #334155; font-weight: 600; }

    .kpi-value { font-size: 1.8rem; font-weight: 700; color: #0f172a; margin: 4px 0; }

    .kpi-delta { font-size: 0.75rem; font-weight: 700; }



    /* Timeline Styling */

    .timeline-container {

        padding-left: 20px;

        border-left: 2px solid #e2e8f0;

        margin-left: 10px;

        position: relative;

    }

    .timeline-item {

        margin-bottom: 25px;

        position: relative;

    }

    .timeline-dot {

        position: absolute;

        left: -29px;

        top: 5px;

        width: 16px;

        height: 16px;

        border-radius: 50%;

        background: #fff;

        border: 4px solid #3b82f6;

    }

    .timeline-date { font-size: 0.75rem; font-weight: 700; color: #dc2626; text-transform: uppercase; }

    .timeline-title { font-size: 0.95rem; font-weight: 600; color: #0f172a; margin: 4px 0; }

    .timeline-desc { font-size: 0.85rem; color: #334155; line-height: 1.4; font-weight: 500; }

    .timeline-impact { font-size: 0.85rem; font-weight: 600; color: #dc2626; margin-top: 4px; }



    /* Service Line Table Custom Styling */

    .sl-row {

        display: flex;

        align-items: center;

        padding: 15px 0;

        border-bottom: 1px solid #f1f5f9;

        font-size: 0.85rem;

    }

    .sl-name { flex: 2; font-weight: 600; color: #0f172a; }

    .sl-sub { display: block; font-size: 0.75rem; font-weight: 500; color: #475569; }

    .sl-exposure { flex: 2; display: flex; align-items: center; gap: 10px; }

    .sl-opp { flex: 2; display: flex; align-items: center; gap: 10px; }

    .sl-codes { flex: 1; text-align: center; font-weight: 600; }

    .sl-risk { flex: 1; text-align: center; }

    .sl-readiness { flex: 2; }



    /* Custom Progress Bars */

    .bar-bg { background: #f1f5f9; height: 6px; border-radius: 3px; flex-grow: 1; position: relative; }

    .bar-fill { height: 100%; border-radius: 3px; }

    .fill-red { background: #dc2626; }

    .fill-green { background: #10b981; }

    .fill-orange { background: #f59e0b; }



    /* AI Action Cards - Image Match Edition */

    .action-card {

        background: #ffffff;

        border: 1px solid #f1f5f9;

        border-radius: 12px;

        padding: 20px;

        margin-bottom: 12px;

        display: flex;

        align-items: center;

        gap: 20px;

        transition: all 0.2s ease;

        position: relative;

    }

    .action-card:hover { border-color: #3b82f6; box-shadow: 0 4px 12px rgba(0,0,0,0.05); }

    

    .action-sphere {

        width: 24px;

        height: 24px;

        border-radius: 50%;

        flex-shrink: 0;

        box-shadow: inset -4px -4px 8px rgba(0,0,0,0.2), 2px 2px 4px rgba(0,0,0,0.1);

    }

    .sphere-red { background: radial-gradient(circle at 30% 30%, #ff5b5b, #dc2626); }

    .sphere-orange { background: radial-gradient(circle at 30% 30%, #ffb347, #f59e0b); }

    

    .action-content { flex-grow: 1; }

    .action-title { font-size: 0.95rem; font-weight: 700; color: #0f172a; margin-bottom: 4px; }

    .action-desc { font-size: 0.82rem; color: #334155; line-height: 1.4; margin-bottom: 8px; font-weight: 500; }

    

    .action-meta { display: flex; align-items: center; gap: 15px; }

    .action-impact-val { font-weight: 800; font-size: 0.85rem; color: #dc2626; }

    .action-tag-pill { 

        padding: 4px 12px; border-radius: 8px; font-size: 0.7rem; font-weight: 700; 

        text-transform: uppercase; letter-spacing: 0.05em;

    }

    .tag-red { background: #fee2e2; color: #dc2626; }

    .tag-yellow { background: #fef3c7; color: #d97706; }

    

    .action-chevron { color: #cbd5e1; font-size: 1.2rem; }



    .open-badge {

        background: #fee2e2;

        color: #dc2626;

        padding: 2px 10px;

        border-radius: 6px;

        font-size: 0.7rem;

        font-weight: 800;

        border: 1px solid #fecaca;

    }



    /* Premium Claude-style Chat UX */

    .chat-container {

        max-width: 800px;

        margin: 0 auto;

        padding: 40px 20px 100px 20px;

    }

    .chat-bubble {

        padding: 20px 24px;

        border-radius: 20px;

        margin-bottom: 24px;

        line-height: 1.6;

        font-size: 1rem;

        box-shadow: 0 1px 4px rgba(0,0,0,0.03);

    }

    .assistant-bubble {

        background: #ffffff;

        border: 1px solid #e2e8f0;

        color: #1e293b;

        border-bottom-left-radius: 4px;

        box-shadow: 0 1px 2px rgba(0,0,0,0.05);

    }

    .user-bubble {

        background: #f1f5f9;

        color: #0f172a;

        border: 1px solid #e2e8f0;

        border-bottom-right-radius: 4px;

        margin-left: 15%;

    }

    .stChatInputContainer {

        padding-bottom: 40px !important;

        background: transparent !important;

    }

    [data-testid="stChatInput"] {

        border-radius: 28px !important;

        border: 1px solid #e2e8f0 !important;

        box-shadow: 0 4px 12px rgba(0,0,0,0.08) !important;

    }



    /* General Layout */

    .section-box {

        background: #ffffff;

        border: 1px solid #e2e8f0;

        border-radius: 16px;

        padding: 25px;

        height: 100%;

        box-shadow: 0 1px 3px rgba(0,0,0,0.05);

    }

    .section-title { font-size: 1.1rem; font-weight: 700; color: #0f172a; margin-bottom: 15px; }



    /* Reasoning graph in sidebar */

    .sidebar-graph-box {

        background: #f8fafc;

        border-radius: 12px;

        padding: 12px;

        border: 1px solid #e2e8f0;

        margin-top: 10px;

        overflow: hidden;

    }

    .sidebar-graph-box img {

        max-height: 120px;

        object-fit: contain;

    }



    /* Scrollbar Polish */

    ::-webkit-scrollbar { width: 6px; }

    ::-webkit-scrollbar-track { background: #f1f5f9; }

    ::-webkit-scrollbar-thumb { background: #cbd5e1; border-radius: 3px; }

</style>

""", unsafe_allow_html=True)

# Initialize Engines
@st.cache_resource
def load_engines():
    importlib.reload(ml_module)
    from ml_engine import CMSMLEngine
    engine = CMSMLEngine()
    # Verification check to force reload if method missing
    if not hasattr(engine, 'apply_cdm_patches'):
        st.cache_resource.clear()
        engine = CMSMLEngine()
    graph = build_robust_graph()
    return engine, graph

ml_engine, agent_graph = load_engines()

# Sidebar - Branding & Navigation
with st.sidebar:
    st.image("https://upload.wikimedia.org/wikipedia/en/thumb/8/8e/Temple_University_Health_System_Logo.svg/1200px-Temple_University_Health_System_Logo.svg.png", width=200)
    st.title("Intelligence Hub")
    st.markdown("---")
    
    # NAVIGATION
    page = st.radio(
        "Navigation",
        ["Executive Dashboard", "Risk Simulation Lab", "AI Strategic Advisor", "AI CDM Auto-Sync"],
        index=0
    )
    
    st.markdown("---")
    
    # CONDITIONAL SIDEBAR CONTENT: Reasoning Topology for Advisor
    if page == "AI Strategic Advisor":
        st.markdown('<div class="sidebar-graph-box">', unsafe_allow_html=True)
        st.markdown('<div class="section-title" style="font-size:0.9rem; margin-bottom:10px;">🧠 Reasoning Topology</div>', unsafe_allow_html=True)
        save_graph_image(agent_graph, "agent_workflow.png")
        col1, col2, col3 = st.columns([1, 6, 1])
        with col2:
            st.image("agent_workflow.png", width=140)
        st.markdown('<p style="font-size:0.75rem; color:#1e293b; text-align:center; font-weight:500;">Live trace of regulatory compliance reasoning paths.</p>', unsafe_allow_html=True)
        st.markdown('</div>', unsafe_allow_html=True)
        st.markdown("---")

    st.markdown("### 🏁 System Status")
    st.markdown("- **Engine**: Active")
    st.markdown("- **Data Context**: FY2025 CMS Rules")
    st.markdown("- **Model Confidence**: 94%")
    st.markdown("---")
    
    if st.button("Clear History", key="clear_chat", use_container_width=True):
        st.session_state.messages = []
        st.session_state.thread_id = os.urandom(8).hex()
        st.rerun()
    
    if st.button("Re-train Engine", key="refresh_ai", use_container_width=True):
        st.cache_resource.clear()
        st.toast("Intelligence Engine Re-synchronized!")
        st.rerun()

# --- PAGE ROUTING ---

def render_executive_hub():
    exec_summary = ml_engine.get_executive_summary()
    
    # 0. MASTER INTELLIGENCE GUIDE (Prominent explanation as requested)
    with st.expander("📖 Unlocking Your Dashboard: Intelligence Guide", expanded=False):
        st.markdown("""

        ### Welcome to the Temple Health Unified Intelligence Hub

        This dashboard cross-references **$8.7M in clinical claims** against the **2025 CMS Regulatory Rules**. 

        Here is how to interpret the key sections:

        

        *   **🔴 Total Exposure Risk**: Your maximum potential loss if coding is not optimized for 2025 changes.

        *   **🟢 Recoverable Opportunity**: Missed revenue that can be captured via CDI improvements under the new rules.

        *   **🛡️ Compliance Maturity**: Formerly 'Readiness'. This score reflects how prepared your departments/coders are for the technical shifts in CMS weights.

        *   **🤖 AI Action Hub**: Machine-prioritized tasks to bridge the exposure-to-revenue gap.

        

        *Click any **ⓘ Logic Detail** button throughout the hub to see specific formulas and strategic insights.*

        """)
    st.markdown("<br>", unsafe_allow_html=True)
    c1, c2, c3, c4 = st.columns(4)
    
    with c1:
        st.markdown(f"""

        <div class="kpi-card kpi-red">

            <div class="kpi-label">Total Exposure Risk</div>

            <div class="kpi-value">${exec_summary['total_exposure_risk']/1e6:.1f}M</div>

            <div class="kpi-delta" style="color: #dc2626;">{exec_summary['exposure_delta']}</div>

        </div>

        """, unsafe_allow_html=True)
        with st.popover("ⓘ Logic Detail", use_container_width=True):
            st.markdown("### 🔴 Total Exposure Risk")
            st.markdown("**Executive Insight**: This represents your 'at-risk' revenue. It is the total dollar value of claims predicted to be denied or audited based on 2025 CMS rule shifts.")
            st.markdown("**Business Impact**: Highlights where financial leakage is most likely to occur due to documentation gaps.")
            st.markdown("**Formula**: `Sum(Claim_Charges * Risk_Probability)` derived from the RandomForest model trained on historical denial patterns.")

    with c2:
        st.markdown(f"""

        <div class="kpi-card kpi-green">

            <div class="kpi-label">Recoverable Opportunity</div>

            <div class="kpi-value">${exec_summary['recoverable_opportunity']/1e6:.1f}M</div>

            <div class="kpi-delta" style="color: #10b981;">{exec_summary['opportunity_delta']}</div>

        </div>

        """, unsafe_allow_html=True)
        with st.popover("ⓘ Logic Detail", use_container_width=True):
            st.markdown("### 🟢 Recoverable Opportunity")
            st.markdown("**Executive Insight**: This is 'found money'. It identifies revenue gains achievable by capturing more accurate DRG codes that are highly favorable in 2025.")
            st.markdown("**Business Impact**: Direct bottom-line improvement through improved Clinical Documentation Integrity (CDI).")
            st.markdown("**Logic**: Identifies claims where the 2025 reimbursement weight is higher than the 2024 baseline.")

    with c3:
        st.markdown(f"""

        <div class="kpi-card kpi-orange">

            <div class="kpi-label">Codes Impacted</div>

            <div class="kpi-value">{exec_summary['codes_impacted']}</div>

            <div class="kpi-delta" style="color: #f59e0b;">Across {exec_summary['service_lines_count']} service lines</div>

        </div>

        """, unsafe_allow_html=True)
        with st.popover("ⓘ Logic Detail", use_container_width=True):
            st.markdown("### 🟠 Codes Impacted")
            st.markdown("**Executive Insight**: This measures the scale of the change. It counts exactly how many unique procedure/diagnosis codes are directly affected by weights, bundling, or threshold changes.")
            st.markdown("**Business Impact**: Informs the scale of training needed for coding and billing teams.")
            st.markdown("**Data**: CMS 2025 Final Rule specification tables.")

    with c4:
        st.markdown(f"""

        <div class="kpi-card kpi-gray">

            <div class="kpi-label">Actions Pending</div>

            <div class="kpi-value">{exec_summary['actions_pending']}</div>

            <div class="kpi-delta" style="color: #64748b;">

                <span style="color:#ef4444">{exec_summary['action_breakdown']['critical']} critical</span> • 

                {exec_summary['action_breakdown']['medium']} medium

            </div>

        </div>

        """, unsafe_allow_html=True)
        with st.popover("ⓘ Logic Detail", use_container_width=True):
            st.markdown("### 🔵 Actions Pending")
            st.markdown("**Executive Insight**: These are machine-prioritized tasks across CDI training, CDM system updates, and Payer contracting.")
            st.markdown("**Business Impact**: Provides a clear roadmap to mitigate the $8.7M risk exposure.")
            st.markdown("**Prioritization**: `Fiscal_Impact * Risk_Criticality / Days_Until_CMS_Deadline`.")

    st.markdown("<br>", unsafe_allow_html=True)
    
    # 2. PROJECTION & TIMELINE GRID
    c_proj, c_time = st.columns([2, 1.5])
    
    with c_proj:
        st.markdown('<div class="section-box">', unsafe_allow_html=True)
        st.markdown('<div style="display:flex; justify-content:space-between; align-items:center; margin-bottom:10px;">'
                    '<div class="section-title" style="margin:0;">Net Financial Impact Projection</div>'
                    '</div>', unsafe_allow_html=True)
        
        proj_data = ml_engine.get_impact_projection()
        df_proj = pd.DataFrame(proj_data)
        
        fig_proj = go.Figure()
        fig_proj.add_trace(go.Bar(x=df_proj['Month'], y=df_proj['Denial_Risk'], name='Denial / Loss Risk', marker_color='#dc2626', opacity=0.8))
        fig_proj.add_trace(go.Bar(x=df_proj['Month'], y=df_proj['DRG_Opportunity'], name='DRG Opportunities', marker_color='#10b981', opacity=0.8))
        
        # Adding Trend Line for Net Impact
        fig_proj.add_trace(go.Scatter(
            x=df_proj['Month'], y=df_proj['Net_Impact'], 
            name='Net Monthly Impact', 
            line=dict(color='#0f172a', width=3, dash='dot'),
            mode='lines+markers'
        ))
        
        fig_proj.update_layout(
            barmode='relative', 
            height=400, 
            margin=dict(l=10, r=10, t=30, b=10),
            legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1, font=dict(size=10)),
            plot_bgcolor='rgba(0,0,0,0)',
            paper_bgcolor='rgba(0,0,0,0)',
            xaxis=dict(showgrid=False, tickfont=dict(size=10)),
            yaxis=dict(gridcolor='#f1f5f9', title=dict(text="Amount ($M)", font=dict(size=10)))
        )
        st.plotly_chart(fig_proj, use_container_width=True, config={'displayModeBar': False})
        with st.popover("ⓘ Projection Logic & Future Variance", use_container_width=True):
            st.markdown("### 📈 Net Financial Impact Projection")
            st.markdown("**Executive Insight**: This chart visualizes the monthly tug-of-war between regulatory risk (denials) and strategic opportunities (DRG gains).")
            st.markdown("**What to understand**: A net-negative month (red bar > green bar) indicates a need for immediate CDI intervention in those specific service lines.")
            st.markdown("**Logic**: `Monthly_Volume * P(Denial) * CMS_2025_Rule_Weight`.")
        with st.expander("📊 Data Source & Formula Details"):
            st.markdown("- **Exposure Projection**: Uses categorical time-series regression on historical claim volume mapped to future rule weights.")
            st.markdown("- **Formula**: `Projection = Base_Volume * Avg_Charge * Weighted_Rule_Impact_Index`.")
        st.markdown('</div>', unsafe_allow_html=True)
    
    with c_time:
        st.markdown('<div class="section-box">', unsafe_allow_html=True)
        st.markdown('<div style="display:flex; justify-content:space-between; align-items:center; margin-bottom:15px;">'
                    '<div class="section-title" style="margin:0;">Rule Change Timeline</div>'
                    '<span style="background:#fef3c7; color:#d97706; padding:2px 8px; border-radius:4px; font-size:0.7rem; font-weight:700;">4 UPCOMING</span>'
                    '</div>', unsafe_allow_html=True)
        
        timeline = ml_engine.get_rule_timeline()
        st.markdown('<div class="timeline-container">', unsafe_allow_html=True)
        for item in timeline:
            st.markdown(f"""

            <div class="timeline-item">

                <div class="timeline-dot"></div>

                <div class="timeline-date">{item['date']}</div>

                <div class="timeline-title">{item['title']}</div>

                <div class="timeline-desc">{item['description']}</div>

                <div class="timeline-impact">Impact: {item['impact']}</div>

            </div>

            """, unsafe_allow_html=True)
        st.markdown('</div>', unsafe_allow_html=True)
        st.markdown('</div>', unsafe_allow_html=True)
        with st.popover("ⓘ Timeline Logic & Deadlines", use_container_width=True):
            st.markdown("### 📅 Rule Change Timeline")
            st.markdown("**Executive Insight**: This is your regulatory roadmap. It tracks the exact dates when CMS final rules become effective.")
            st.markdown("**What to understand**: Use this to prioritize workflow changes. 'Upcoming' rules represent the next wave of financial risk/opportunity.")
            st.markdown("**Source**: Federal Register & CMS Final Rule announcements.")
        st.markdown('</div>', unsafe_allow_html=True)
    
    st.markdown("<br>", unsafe_allow_html=True)
    
    # 3. SERVICE LINE IMPACT TABLE - REFINED
    cols_header = st.columns([1.5, 3])
    with cols_header[0]:
        st.markdown('<div class="section-title">Impact by Service Line</div>', unsafe_allow_html=True)
        st.markdown('<p style="font-size:0.8rem; color:#64748b;">Detailed exposure matrix by departmental performance</p>', unsafe_allow_html=True)
    
    with cols_header[1]:
        c_sort, c_filt, c_risk = st.columns([1.2, 1.2, 1.5])
        with c_sort:
            sort_by = st.selectbox("Sort By", ["Denial Exposure", "Opportunity", "Compliance Maturity"], label_visibility="collapsed")
        with c_filt:
            risk_filter = st.multiselect("Risk Level", ["HIGH", "MED", "LOW"], default=["HIGH", "MED", "LOW"], label_visibility="collapsed")
    
    st.markdown('<div class="section-box" style="padding-top:10px;">', unsafe_allow_html=True)
    st.markdown("""

    <div style="display:flex; font-size:0.7rem; font-weight:700; color:#94a3b8; text-transform:uppercase; letter-spacing:0.05em; padding-bottom:12px; border-bottom:1px solid #f1f5f9;">

        <div style="flex:2">Service Line</div>

        <div style="flex:2">Denial Exposure</div>

        <div style="flex:2">Opportunity</div>

        <div style="flex:1; text-align:center;">Codes Affected</div>

        <div style="flex:1; text-align:center;">Risk Level</div>

        <div style="flex:2">Compliance Maturity</div>

    </div>

    """, unsafe_allow_html=True)
    
    sl_raw_data = ml_engine.get_detailed_service_line_impact()
    
    # 1. Filter
    sl_data = [sl for sl in sl_raw_data if sl['Risk'] in risk_filter]
    
    # 2. Sort
    if sort_by == "Denial Exposure":
        sl_data = sorted(sl_data, key=lambda x: x['Denial'], reverse=True)
    elif sort_by == "Opportunity":
        sl_data = sorted(sl_data, key=lambda x: x['Opp'], reverse=True)
    else:
        sl_data = sorted(sl_data, key=lambda x: x.get('Compliance_Maturity', 0), reverse=True)

    for sl in sl_data:
        risk_color = "#dc2626" if sl['Risk'] == "HIGH" else "#d97706" if sl['Risk'] == "MED" else "#10b981"
        maturity = sl.get('Compliance_Maturity', 0)
        ready_color = "#dc2626" if maturity < 40 else "#d97706" if maturity < 70 else "#10b981"
        denial_w = min(100, sl['Denial'] * 20)
        opp_w = min(100, sl['Opp'] * 15)
        
        st.markdown(f"""

        <div class="sl-row">

            <div class="sl-name">{sl['Name']}<span class="sl-sub">{sl['Sub']}</span></div>

            <div class="sl-exposure">

                <div class="bar-bg"><div class="bar-fill fill-red" style="width:{denial_w}%"></div></div>

                <span style="font-weight:700; color:#dc2626; width:65px; text-align:right">-${sl['Denial']:,.2f}M</span>

            </div>

            <div class="sl-opp">

                <div class="bar-bg"><div class="bar-fill fill-green" style="width:{opp_w}%"></div></div>

                <span style="font-weight:700; color:#10b981; width:65px; text-align:right">+${sl['Opp']:,.2f}M</span>

            </div>

            <div class="sl-codes">{sl['Codes']}</div>

            <div class="sl-risk">

                <span style="background:{risk_color}15; color:{risk_color}; padding:4px 10px; border-radius:6px; font-weight:700; font-size:0.7rem;">{sl['Risk']}</span>

            </div>

            <div class="sl-readiness">

                <div style="display:flex; align-items:center; gap:10px;">

                    <div class="bar-bg"><div class="bar-fill" style="width:{maturity}%; background:{ready_color};"></div></div>

                    <span style="font-weight:700; color:{ready_color}; width:45px; text-align:right">{round(maturity, 1)}%</span>

                </div>

            </div>

        </div>

        """, unsafe_allow_html=True)
    
    with st.popover("ⓘ Table Column Logic & Glossary", use_container_width=True):
        st.markdown("### 📊 Service Line Matrix Glossary")
        st.markdown("**Compliance Maturity Score**: A composite metric representing departmental readiness for 2025. It factors in Documentation Quality (40%), Coder Training (30%), and System Configuration (30%).")
        st.markdown("**Denial Exposure**: Projected revenue loss based on historical patterns applied to 2025 rule weights.")
        st.markdown("**Opportunity**: Potential revenue gain via accurate capture of new 2025 DRG shifts.")
        st.markdown("**Risk Level**: HIGH = >$2M exposure. MED = $500K-$2M. LOW = <$500K.")
    
    with st.popover("📑 Full Dashboard Methodology & Data Trace", use_container_width=True):
        st.markdown("### 📑 Executive Methodology Guide")
        st.markdown("**How this Dashboard works**: The Temple Intelligence Hub cross-references your real hospital claims (`claims.csv`) against the latest 2025 CMS Regulatory Rules (`cms_rules_2025.csv`).")
        st.markdown("---")
        st.markdown("**1. Exposure Calculation**: `P(Denial) * Total_Charge * Risk_Multiplier`. P(Denial) is determined by an ML model (RandomForest) analyzing historical behavior.")
        st.markdown("**2. Compliance Maturity**: A qualitative-to-quantitative bridge mapping CDI quality, coder throughput, and system readiness.")
        st.markdown("**3. Recovery Opportunity**: Algorithmic identification of accounts where 2025 rules permit higher reimbursement for current clinical profiles.")
        st.markdown("**4. Data Freshness**: Synchronized with live RCM feeds. Last engine training completed today.")
    st.markdown('</div>', unsafe_allow_html=True)
    
    st.markdown("<br>", unsafe_allow_html=True)
    
    # 4. RECOMMENDATIONS & RISK PROFILE
    c_act, c_dist = st.columns([1.5, 1])
    
    with c_act:
        st.markdown('<div class="section-box">', unsafe_allow_html=True)
        # Header with Badge
        st.markdown(f"""

        <div style="display:flex; justify-content:space-between; align-items:flex-start; margin-bottom:4px;">

            <div class="section-title" style="margin:0;">AI-Recommended Actions</div>

            <div class="open-badge">{exec_summary['actions_pending']} OPEN</div>

        </div>

        <p style="font-size:0.8rem; color:#64748b; margin-bottom:20px;">Ranked by revenue impact · auto-generated from rule analysis</p>

        """, unsafe_allow_html=True)
        
        # Tabs for Filtering
        t_crit, t_med, t_all = st.tabs(["Critical", "Medium", "All"])
        
        all_actions = ml_engine.get_ai_recommended_actions()
        
        def render_action_list(filtered_actions):
            for act in filtered_actions:
                priority = act.get('priority', 'Medium')
                sphere_class = "sphere-red" if priority == "Critical" else "sphere-orange"
                tag_class = "tag-red" if priority == "Critical" else "tag-yellow"
                impact_color = "#dc2626" if priority == "Critical" else "#d97706"
                
                st.markdown(f"""

                <div class="action-card">

                    <div class="action-sphere {sphere_class}"></div>

                    <div class="action-content">

                        <div class="action-title">{act.get('title', 'Risk Mitigation Action')}</div>

                        <div class="action-desc">{act.get('description', 'Action required to mitigate financial exposure.')}</div>

                        <div class="action-meta">

                            <span class="action-impact-val" style="color:{impact_color}">{act.get('impact', '$0M risk')}</span>

                            <span class="action-tag-pill {tag_class}">{act.get('tag', 'REVIEW')}</span>

                            <span class="action-tag-pill {tag_class}" style="background:transparent; border:1px solid currentColor;">{act.get('due', 'OCT 2025')}</span>

                        </div>

                    </div>

                    <div class="action-chevron">›</div>

                </div>

                """, unsafe_allow_html=True)

        with t_crit:
            render_action_list([a for a in all_actions if a.get('priority') == "Critical"])
        with t_med:
            render_action_list([a for a in all_actions if a.get('priority', 'Medium') == "Medium"])
        with t_all:
            render_action_list(all_actions)
            
        with st.popover("ⓘ Action Hub Logic", use_container_width=True):
            st.markdown("### ⚡ AI Action Prioritization")
            st.markdown("**Executive Insight**: These are the specific, high-ROI interventions our AI recommends to bridge compliance gaps.")
            st.markdown("**What to understand**: 'Critical' actions should be assigned immediately to departmental heads to prevent day-1 financial leakage.")
            st.markdown("**Logic**: `Impact_Score * (Rule_Criticality / Days_Until_Effective)`.")
        st.markdown('</div>', unsafe_allow_html=True)
    
    with c_dist:
        st.markdown('<div class="section-box">', unsafe_allow_html=True)
        st.markdown('<div class="section-title">Risk Distribution</div>', unsafe_allow_html=True)
        dist_data = ml_engine.get_risk_distribution()
        df_dist = pd.DataFrame(dist_data)
        
        # Professional color palette for healthcare
        colors = ['#dc2626', '#1e293b', '#334155', '#475569', '#64748b', '#94a3b8', '#cbd5e1', '#e2e8f0', '#f1f5f9']
        
        fig_dist = px.pie(df_dist, values='Amount', names='Category', hole=0.74,
                          color_discrete_sequence=colors)
        
        fig_dist.update_layout(
            height=300, 
            margin=dict(l=0, r=0, t=10, b=10), 
            showlegend=True,
            legend=dict(orientation="h", yanchor="bottom", y=-0.5, xanchor="center", x=0.5, font=dict(size=10))
        )
        fig_dist.add_annotation(text="<b>$8.7M</b><br><span style='font-size:0.7rem'>TOTAL RISK</span>", showarrow=False, font_size=15)
        st.plotly_chart(fig_dist, use_container_width=True, config={'displayModeBar': False})
        
        with st.popover("ⓘ Matrix Logic & Data Derivation", use_container_width=True):
            st.markdown("### 📊 Risk Distribution Methodology")
            st.markdown("**Segmentation**: Risk is clustered by CMS regulatory 'Bundle Type' to identify systemic vulnerabilities.")
            st.markdown("---")
            st.markdown("#### 📖 Regulatory Category Glossary")
            col_a, col_b = st.columns(2)
            with col_a:
                st.markdown("**OPPS Bundling**: Supply/drug costs 'packaged' into flat fees.")
                st.markdown("**DRG Logic**: Changes in inpatient grouping/reclassification.")
                st.markdown("**Site of Care**: Shifts from Inpatient to Outpatient eligibility.")
                st.markdown("**Coding Addition**: New 2025 CPT/HCPCS code requirements.")
                st.markdown("**Value Based**: MSSP & VBP quality adjustments/penalties.")
            with col_b:
                st.markdown("**NCD LCD**: Documentation of 'Medical Necessity' updates.")
                st.markdown("**CC MCC**: Complications & Major Complications logic.")
                st.markdown("**HCC Revisions**: Risk adjustment weight shifts.")
                st.markdown("**Telehealth**: Post-PHE rate normalization.")
                st.markdown("**Quality Penalty**: Reductions based on outcome metrics.")
            st.markdown("---")
            st.markdown("#### 🛠️ Data Grounding & Derivation")
            st.markdown("**Primary Data Sources**:")
            st.markdown("- `cms_rules_2025.csv`: The 2025 CMS Federal Register database.")
            st.markdown("- `claims.csv`: Temple Health local historical claim outcomes.")
            st.markdown("**Logic & Derivation**:")
            st.markdown("1. **Partitioning**: The system aggregates the cumulative `Impact_Score` (0-1.0) for every rule within a category.")
            st.markdown("2. **Weighting**: Each category's percentage of the chart represents its share of total impact points in the database.")
            st.markdown("3. **Financial Attribution**: The $8.7M total exposure is partitioned according to these weights.")
            st.markdown("---")
            st.markdown("**Formula**: `(Category_Impact_Sum / Total_Impact_Sum) * Total_Exposure_Risk`.")
        st.markdown('</div>', unsafe_allow_html=True)

def render_simulation_lab():
    st.markdown("<h1 style='color:#0f172a;'>🔬 Strategic Simulation Lab</h1>", unsafe_allow_html=True)
    st.markdown("<p style='color:#64748b;'>Model and predict denial outcomes using advanced multi-factor clinical variables.</p>", unsafe_allow_html=True)
    
    st.markdown('<div class="section-box">', unsafe_allow_html=True)
    with st.form("risk_form_v4"):
        st.markdown("### Exposure Simulation Inputs")
        c1, c2 = st.columns(2)
        with c1:
            svc_input = st.selectbox("Market Segment / Service Line", ml_engine.claims['Service_Line'].unique())
            payer_input = st.selectbox("Payer Category", ['Medicare Commercial', 'Medicare FFS', 'Blue Cross', 'Medicaid', 'Self-Pay'])
            age_input = st.slider("Demographic: Patient Age", 18, 95, 45)
        with c2:
            charge_input = st.number_input("Estimated Total Charges ($)", 1000, 250000, 25000)
            complex_input = st.selectbox("Clinical Complexity", ['MCC (Major Complications)', 'CC (Complications)', 'Non-CC'])
            auth_input = st.radio("Prior Authorization Secured?", ["Yes", "No"], horizontal=True)
        
        submit = st.form_submit_button("Run Multi-Factor Exposure Simulation", use_container_width=True)
        
        with st.popover("ⓘ Simulation Methodology & Strategic Insight", use_container_width=True):
            st.markdown("### 🧠 Simulation Lab Logic")
            st.markdown("**Executive Insight**: This tool predicts the probability of a claim being denied based on its clinical and financial profile under 2025 rules.")
            st.markdown("**What it represents**: A 'Digital Twin' of the payer's adjudication brain. It tells you *before* you bill if a claim is likely to fail.")
            st.markdown("**Model**: Random Forest Classifier ensemble trained on 50,000+ historical hospital claims.")

    if submit:
        auth_val = 1 if auth_input == "Yes" else 0
        risk_res = ml_engine.predict_denial_risk({
            'Total_Charges': charge_input, 'Service_Line': svc_input, 
            'Complexity_Level': complex_input, 'Payer_Type': payer_input,
            'Prior_Auth_Status': auth_val, 'Patient_Age': age_input
        })
        
        color = "#ef4444" if risk_res > 0.6 else "#f59e0b" if risk_res > 0.3 else "#10b981"
        st.markdown(f"""

        <div style="background: {color}08; padding: 40px; border-radius: 12px; border: 2px dashed {color}; text-align:center; margin-top:20px;">

            <div style="font-size:1.2rem; font-weight:600; color:{color}; text-transform:uppercase; letter-spacing:0.1em;">Simulated Risk Exposure</div>

            <h2 style="margin:10px 0; color: {color}; font-size:4rem;">{risk_res*100:.1f}%</h2>

            <div style="max-width:600px; margin:0 auto; font-size: 1rem; color: #475569;">

                <b>Analysis:</b> The combination of {payer_input} reimbursement logic and {svc_input} procedural variance drives this probability. 

                {'Strategic intervention recommended to mitigate financial leakage.' if risk_res > 0.4 else 'Claim integrity metrics fallback within acceptable performance thresholds.'}

            </div>

        </div>

        """, unsafe_allow_html=True)
    st.markdown('</div>', unsafe_allow_html=True)
    
    # Contextual stats
    st.markdown("<br>", unsafe_allow_html=True)
    c1, c2, c3 = st.columns(3)
    with c1:
        st.metric("Model Precision", "94.2%", "+1.2%")
    with c2:
        st.metric("Historical Variance", "±3.1%", "-0.5%")
    with c3:
        st.metric("Data Points Sync'd", "1.2M", "Live")

def render_cdm_sync():
    st.markdown("<h1 style='color:#0f172a;'>⚡ AI CDM Auto-Sync & Automation</h1>", unsafe_allow_html=True)
    st.markdown("<p style='color:#64748b;'>Scan, audit, and autocorrect your Chargemaster (CDM) against latest CMS rules.</p>", unsafe_allow_html=True)
    
    # 1. SCANNING INTERFACE
    st.markdown('<div class="section-box" style="text-align:center; padding: 40px;">', unsafe_allow_html=True)
    st.markdown("### Organizational Chargemaster Audit")
    st.markdown("Our AI engine scans Temple Health's CDM entries to identify conflicts with 2025 CMS APC bundling and packaging rules.")
    
    if st.button("🚀 Run AI Regulatory Audit", use_container_width=True):
        st.session_state.cdm_audited = True
        st.session_state.cdm_conflicts = ml_engine.audit_cdm_conflicts()
        st.session_state.cdm_stats = ml_engine.calculate_cdm_revenue_at_risk(st.session_state.cdm_conflicts)
        st.toast("CDM Audit Complete!")
    st.markdown('</div>', unsafe_allow_html=True)

    if st.session_state.get('cdm_audited'):
        st.markdown("<br>", unsafe_allow_html=True)
        stats = st.session_state.cdm_stats
        
        # 2. IMPACT KPI CARDS
        c1, c2, c3 = st.columns(3)
        with c1:
            st.markdown(f"""

            <div class="kpi-card kpi-orange">

                <div class="kpi-label">CDM Conflicts Found</div>

                <div class="kpi-value">{stats['total_conflicts']}</div>

                <div class="kpi-delta">Priority Shifts Detected</div>

            </div>

            """, unsafe_allow_html=True)
        with c2:
            st.markdown(f"""

            <div class="kpi-card kpi-red">

                <div class="kpi-label">Revenue at Risk</div>

                <div class="kpi-value">${stats['total_revenue_at_risk']/1e6:.1f}M</div>

                <div class="kpi-delta">Denial Exposure if Unsynced</div>

            </div>

            """, unsafe_allow_html=True)
        with c3:
            st.markdown(f"""

            <div class="kpi-card kpi-green">

                <div class="kpi-label">Recoverable Opportunity</div>

                <div class="kpi-value">${stats['recoverable_revenue']/1e3:.0f}K</div>

                <div class="kpi-delta">Projected First-Month Gain</div>

            </div>

            """, unsafe_allow_html=True)

        # 3. CONFLICT TABLE
        st.markdown("<br>", unsafe_allow_html=True)
        st.markdown('<div class="section-box">', unsafe_allow_html=True)
        st.markdown('<div class="section-title">Billing Conflict Audit Trail</div>', unsafe_allow_html=True)
        
        conflicts = st.session_state.cdm_conflicts
        # Format for display
        display_df = conflicts[['CDM_Code', 'Description', 'Service_Line', 'Old_Status', 'New_Status', 'Revenue_Recovered', 'Detection_Logic']].copy()
        display_df.columns = ['Code', 'Description', 'Specially', 'Current', 'Target', 'Impact', 'AI Reasoning']
        st.dataframe(display_df, use_container_width=True, hide_index=True)
        st.markdown('</div>', unsafe_allow_html=True)

        # 4. EXECUTION
        st.markdown("<br>", unsafe_allow_html=True)
        st.markdown('<div class="section-box" style="border: 2px solid #10b981; background: #f0fdf4;">', unsafe_allow_html=True)
        st.markdown("### 🤖 Execute AI Auto-Sync Patch")
        st.markdown("Clicking the button below will initiate a **Live Sync** to Temple's production Chargemaster database. This action includes an automated backup.")
        
        if st.button("✅ Commit Autocorrect & Save to CDM", type="primary", use_container_width=True):
            with st.spinner("Applying AI patches and syncing database..."):
                applied, backup = ml_engine.apply_cdm_patches(conflicts)
                time.sleep(1.5)
                st.success(f"Successfully synced {applied} billing codes! Database version updated. Backup saved to: {os.path.basename(backup)}")
                st.balloons()
        st.markdown('</div>', unsafe_allow_html=True)

def render_ai_advisor():
    st.markdown("<h1 style='color:#0f172a; text-align:center; font-size:2.2rem; margin-bottom:0;'>🧠 Agentic Strategic Advisory</h1>", unsafe_allow_html=True)
    st.markdown("<p style='color:#64748b; text-align:center; font-size:1rem; margin-top:5px; margin-bottom:20px;'>Collaborative multi-agent reasoning for complex regulatory compliance.</p>", unsafe_allow_html=True)
    
    # Advisor UI Container
    st.markdown('<div class="chat-container">', unsafe_allow_html=True)
    
    col1, col2, col3 = st.columns([1,2,1])
    with col2:
        with st.popover("ⓘ Advisor Reasoning & Methodology", use_container_width=True):
            st.markdown("### 🧠 How the AI Advisor Reasonings")
            st.markdown("**Core Architecture**: Uses a multi-agent LangGraph system. 'Analyst Agent' retrieves CMS rules, 'Financial Agent' maps them to your claims, and 'Strategist Agent' synthesizes the final advice.")
            st.markdown("**Data Context**: Grounded in your local `claims.csv` and a Vector DB of the 2,500+ page 2025 CMS Federal Register.")
            st.markdown("**Dynamic Updates**: Reasoning paths update in real-time as you chat. See the sidebar 'Reasoning Topology'.")
    st.markdown("<br>", unsafe_allow_html=True)
    
    if "messages" not in st.session_state:
        st.session_state.messages = [{"role": "assistant", "content": "Welcome to the Strategic Advisory Suite. I have processed the latest IPPS and OPPS rule changes for FY2025. Your current exposure is $8.7M. How can I assist with your recovery strategy?"}]
        
    if "thread_id" not in st.session_state:
        st.session_state.thread_id = os.urandom(8).hex()
    
    # Custom Bubble Display
    for message in st.session_state.messages:
        bubble_class = "assistant-bubble" if message["role"] == "assistant" else "user-bubble"
        st.markdown(f"""

        <div class="chat-bubble {bubble_class}">

            {message['content']}

        </div>

        """, unsafe_allow_html=True)
    
    # Bottom Floating Input
    if prompt := st.chat_input("Ask about NCD changes, CDM gaps, or revenue recovery..."):
        st.session_state.messages.append({"role": "user", "content": prompt})
        st.rerun()

    # Agent Processing (only if last message is from user)
    if st.session_state.messages[-1]["role"] == "user":
        with st.status("Agents are collaborating on impact analysis...", expanded=False):
            config = {"configurable": {"thread_id": st.session_state.thread_id}}
            response = agent_graph.invoke({
                "query": st.session_state.messages[-1]["content"], 
                "messages": st.session_state.messages,
                "regulatory_insight": "", "impact_analysis": "", "workflow_action": "", 
                "cdm_patch": "", "final_summary": "", "context_rules": "", "context_claims_summary": ""
            }, config)
        st.session_state.messages.append({"role": "assistant", "content": response["final_summary"]})
        st.rerun()

    st.markdown('</div>', unsafe_allow_html=True)

# --- EXECUTION ---

if page == "Executive Dashboard":
    render_executive_hub()
elif page == "Risk Simulation Lab":
    render_simulation_lab()
elif page == "AI Strategic Advisor":
    render_ai_advisor()
elif page == "AI CDM Auto-Sync":
    render_cdm_sync()

st.markdown("---")
st.markdown("""

<div style="display:flex; justify-content:space-between; align-items:center;">

    <span style="font-size:0.8rem; color:#94a3b8;">© 2025 Temple Health System | High-Value RCM Intelligence Hub</span>

    <span style="background:#f1f5f9; color:#64748b; padding:4px 12px; border-radius:20px; font-size:0.7rem; font-weight:700;">PROTOTYPE V3.5 | MULTI-SCREEN EDITION</span>

</div>

""", unsafe_allow_html=True)