"""
Health Matrix AI Command Center - Streamlit Application
Modern AI-inspired healthcare workforce management platform
"""

import streamlit as st
import pandas as pd
import time
from datetime import datetime
import os
from typing import Dict, List, Any, Optional, Iterable
import requests
from io import StringIO
import streamlit.components.v1 as components

# Optional: OpenAI for GPT decisions (fails gracefully if missing)
try:
    import openai
    HAS_OPENAI = True
except Exception:
    openai = None
    HAS_OPENAI = False

# =============================================================================
# Minimal embedded CSS (fallback if style.css not found)
# =============================================================================
_EMBEDDED_CSS = """
/* ---- Layout helpers ---- */
.hero-section{padding:40px 0 12px}
.hero-content{text-align:center}
.hero-logo img{height:60px}
.hero-title{font-weight:900;margin:10px 0 8px}
.hero-title .gradient-text{background:linear-gradient(90deg,#0ea5e9,#22c55e);
-webkit-background-clip:text;background-clip:text;color:transparent}
.hero-title .subtitle{font-size:18px;color:#475569}
.hero-description{max-width:900px;margin:8px auto 0;color:#334155}

.hero-stats{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:12px;max-width:750px;margin:18px auto 0}
.stat-item{border:1px solid #e5e7eb;border-radius:14px;padding:14px;background:#fff}
.stat-value{font-size:22px;font-weight:800}
.stat-label{font-size:12px;color:#64748b}

/* KPI cards */
.kpi-grid{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:14px;margin:10px 0 6px}
.kpi-card{border-radius:16px;border:1px solid #e5e7eb;background:#fff;padding:14px;box-shadow:0 1px 2px rgba(0,0,0,.04)}
.kpi-icon{font-size:22px}
.kpi-value{font-size:26px;font-weight:900;margin-top:6px}
.kpi-label{font-size:12px;color:#64748b}
.kpi-change{font-size:12px;margin-top:6px;color:#0f172a}

/* Status indicator */
.status-indicator{display:flex;align-items:center;gap:10px;padding:10px 12px;border-radius:12px;border:1px solid #e5e7eb;background:#fff}
.status-dot{width:10px;height:10px;border-radius:50%}
.status-indicator.ready .status-dot{background:#10b981}
.status-indicator.completed .status-dot{background:#3b82f6}
.status-indicator.processing .status-dot{background:#f59e0b}
.pulsing{animation:pulse 1.6s infinite}
@keyframes pulse{0%{transform:scale(1);opacity:1}70%{transform:scale(1.35);opacity:.35}100%{transform:scale(1);opacity:1}}

/* Timeline (for components.html) */
.timeline-wrap{margin-top:6px}
.timeline{position:relative;margin:8px 0 0 22px;padding-left:18px}
.timeline:before{content:"";position:absolute;left:0;top:0;bottom:0;width:2px;background:#e5e7eb}
.timeline-item{position:relative;display:flex;gap:12px;margin:14px 0;padding:12px 14px;background:#fff;border:1px solid #e5e7eb;border-radius:12px;box-shadow:0 1px 2px rgba(0,0,0,.04)}
.timeline-item.done{border-color:#d1fae5;background:#f0fdf4}
.timeline-item.current{border-color:#93c5fd;background:#eff6ff}
.timeline-marker{position:absolute;left:-23px;top:18px;width:10px;height:10px;border-radius:50%;background:#10b981;border:2px solid #fff;box-shadow:0 0 0 2px #e5e7eb}
.timeline-item.current .timeline-marker{background:#3b82f6}
.timeline-title{font-weight:800;color:#0f172a}
.timeline-meta{font-size:12px;color:#64748b;margin-top:2px}
.timeline-desc{font-size:13px;color:#1f2937;margin-top:6px}

/* Kanban */
.kanban-board{display:grid;grid-template-columns:repeat(3,minmax(0,1fr));gap:16px}
.kanban-lane{background:#fff;border:1px solid #e5e7eb;border-radius:16px;overflow:hidden}
.lane-header{display:flex;align-items:center;gap:8px;padding:10px 12px;border-bottom:1px solid #e5e7eb;font-weight:800}
.assigned-header{background:#f0fdf4}
.notify-header{background:#eff6ff}
.skipped-header{background:#fff7ed}
.lane-count{margin-left:auto;background:#0f172a;color:#fff;font-size:12px;padding:2px 8px;border-radius:999px}
.lane-content{padding:10px}

.employee-card{border:1px solid #e5e7eb;border-radius:14px;background:#fff;padding:12px;margin-bottom:12px}
.emp-header{display:flex;align-items:center;gap:10px}
.emp-avatar{width:34px;height:34px;border-radius:50%;background:#0ea5e9;color:#fff;display:flex;align-items:center;justify-content:center;font-weight:800}
.emp-info{flex:1}
.emp-name{font-weight:800}
.emp-shift{font-size:12px;color:#64748b}
.emp-chip{font-size:12px;padding:4px 8px;border-radius:999px;border:1px solid #e5e7eb}
.chip-ok{background:#f0fdf4}
.chip-warn{background:#eff6ff}
.chip-fail{background:#fff7ed}
.emp-divider{height:1px;background:#e5e7eb;margin:10px 0}
.emp-badges{display:flex;flex-wrap:wrap;gap:8px}
.emp-badge{display:inline-flex;align-items:center;gap:6px;font-size:12px;border:1px solid #e5e7eb;border-radius:999px;padding:2px 8px}
.badge-ok{background:#f0fdf4}
.badge-fail{background:#fff7ed}
.badge-info{background:#f1f5f9}
.badge-dot{width:6px;height:6px;border-radius:50%;background:#0f172a}
.emp-footer{display:flex;justify-content:space-between;margin-top:8px;font-size:12px;color:#64748b}

/* Overview */
.overview-metrics{display:flex;flex-wrap:wrap;gap:10px;margin:12px 0}
.metric-chip{border:1px solid #e5e7eb;border-radius:999px;background:#fff;padding:6px 10px;font-size:13px}
.business-case{border:1px solid #e5e7eb;border-radius:12px;background:#fff;padding:12px;margin:10px 0}
.footer{margin-top:24px}
.footer-content{font-size:12px;color:#64748b}
@media (max-width:900px){
  .kpi-grid,.hero-stats,.kanban-board{grid-template-columns:1fr}
}
"""

# =============================================================================
# Configuration & Setup
# =============================================================================

def setup_page():
    """Configure Streamlit page settings and load custom CSS"""
    st.set_page_config(
        page_title="Health Matrix AI Command Center",
        page_icon="🏥",
        layout="wide",
        initial_sidebar_state="collapsed"
    )
    # Try external CSS, otherwise fallback
    css_loaded = False
    try:
        if os.path.exists("src/style.css"):
            with open("src/style.css", "r", encoding="utf-8") as f:
                st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
                css_loaded = True
    except Exception:
        css_loaded = False
    if not css_loaded:
        st.markdown(f"<style>{_EMBEDDED_CSS}</style>", unsafe_allow_html=True)

# =============================================================================
# UKG API helpers (unchanged logic)
# =============================================================================

def _get_auth_header() -> Dict[str, str]:
    """Get UKG API authentication headers"""
    app_key = os.environ.get("UKG_APP_KEY")
    token = os.environ.get("UKG_AUTH_TOKEN")
    if not app_key or not token:
        st.warning("UKG authentication variables (UKG_APP_KEY / UKG_AUTH_TOKEN) are not set. API calls may fail.")
    return {
        "Content-Type": "application/json",
        "appkey": app_key or "",
        "Authorization": f"Bearer {token}" if token else "",
    }

def fetch_open_shifts(
    start_date: str = "2000-01-01",
    end_date: str = "3000-01-01",
    location_ids: Optional[Iterable[str]] = None,
) -> pd.DataFrame:
    """Fetch open shifts from UKG API"""
    if location_ids is None:
        location_ids = ["2401", "2402", "2953", "2955", "2927", "2928", "2401", "2955"]
    
    url = "https://partnerdemo-019.cfn.mykronos.com/api/v1/scheduling/schedule/multi_read"
    headers = _get_auth_header()
    payload = {
        "select": ["OPENSHIFTS"],
        "where": {
            "locations": {
                "dateRange": {"startDate": start_date, "endDate": end_date},
                "includeEmployeeTransfer": False,
                "locations": {"ids": list(location_ids)},
            }
        },
    }
    
    try:
        r = requests.post(url, headers=headers, json=payload, timeout=30)
        r.raise_for_status()
        data = r.json()
        rows: List[Dict[str, Any]] = []
        
        for shift in data.get("openShifts", []):
            rows.append({
                "ID": shift.get("id"),
                "Start": shift.get("startDateTime"),
                "End": shift.get("endDateTime"),
                "Label": shift.get("label"),
                "Org Job": (shift.get("segments", [{}])[0].get("orgJobRef", {}).get("qualifier", "")
                           if shift.get("segments") else ""),
                "Posted": shift.get("posted"),
                "Self Serviced": shift.get("selfServiced"),
                "Locked": shift.get("locked"),
            })
        return pd.DataFrame(rows)
    except Exception as e:
        st.error(f"❌ UKG Open Shifts API call failed: {e}")
        return pd.DataFrame()

def fetch_location_data(date: str = "2025-07-13", query: str = "Medsurg") -> pd.DataFrame:
    """Fetch location data from UKG API"""
    url = "https://partnerdemo-019.cfn.mykronos.com/api/v1/commons/locations/multi_read"
    headers = _get_auth_header()
    payload = {
        "multiReadOptions": {"includeOrgPathDetails": True},
        "where": {"query": {"context": "ORG", "date": date, "q": query}}
    }
    
    try:
        r = requests.post(url, headers=headers, json=payload, timeout=30)
        r.raise_for_status()
        data = r.json()
        rows = []
        
        for item in data if isinstance(data, list) else data.get("locations", []):
            rows.append({
                "Node ID": item.get("nodeId", ""),
                "Name": item.get("name", ""),
                "Full Name": item.get("fullName", ""),
                "Description": item.get("description", ""),
                "Org Path": item.get("orgPath", ""),
                "Persistent ID": item.get("persistentId", ""),
            })
        return pd.DataFrame(rows)
    except Exception as e:
        st.error(f"❌ UKG Location API call failed: {e}")
        return pd.DataFrame()

def fetch_employees(employee_ids: Iterable[int]) -> pd.DataFrame:
    """Fetch employee data from UKG API"""
    base_url = "https://partnerdemo-019.cfn.mykronos.com/api/v1/commons/persons/"
    headers = _get_auth_header()

    def fetch_employee_data(emp_id: int) -> Optional[Dict[str, Any]]:
        try:
            resp = requests.get(f"{base_url}{emp_id}", headers=headers, timeout=30)
            if resp.status_code != 200:
                st.warning(f"⚠️ Could not fetch employee {emp_id}: {resp.status_code}")
                return None
            
            data = resp.json()
            person_info = data.get("personInformation", {}).get("person", {})
            person_number = person_info.get("personNumber")
            full_name = person_info.get("fullName", "")
            
            org_path = ""
            primary_accounts = data.get("jobAssignment", {}).get("primaryLaborAccounts", [])
            if primary_accounts:
                org_path = primary_accounts[0].get("organizationPath", "")
            
            phone = ""
            phones = data.get("personInformation", {}).get("telephoneNumbers", [])
            if phones:
                phone = phones[0].get("phoneNumber", "")

            # Fetch certifications
            cert_url = f"{base_url}certifications/{emp_id}"
            cert_resp = requests.get(cert_url, headers=headers, timeout=30)
            certs: List[str] = []
            if cert_resp.status_code == 200:
                cert_data = cert_resp.json()
                if isinstance(cert_data, list):
                    for c in cert_data:
                        qual = c.get("qualifier")
                        if qual: certs.append(qual)
                elif isinstance(cert_data, dict) and "assignments" in cert_data:
                    for a in cert_data.get("assignments", []):
                        qual = a.get("certification", {}).get("qualifier")
                        if qual: certs.append(qual)

            certs = [str(x).strip() for x in certs if x]

            return {
                "personNumber": person_number,
                "organizationPath": org_path,
                "phoneNumber": phone,
                "fullName": full_name,
                "Certifications": certs
            }
        except Exception as e:
            st.error(f"❌ Error fetching employee {emp_id}: {e}")
        return None

    records = []
    for emp_id in employee_ids:
        row = fetch_employee_data(emp_id)
        if row: 
            records.append(row)

    df = pd.DataFrame(records)
    # Ensure mandatory columns exist with proper lengths
    n = len(df)
    for col, default in [
        ("personNumber", ""),
        ("organizationPath", ""),
        ("phoneNumber", ""),
        ("fullName", ""),
        ("Certifications", []),
    ]:
        if col not in df.columns:
            df[col] = [default] * n

    if "Languages" not in df.columns:
        df["Languages"] = [[] for _ in range(n)]
    
    df["JobRole"] = df["organizationPath"].apply(
        lambda p: p.split("/")[-1] if isinstance(p, str) and p else ""
    )
    return df

# =============================================================================
# Decision Logic (unchanged)
# =============================================================================

def _has_any(tokens: List[str], haystack: str) -> bool:
    hay = (haystack or "").lower()
    return any((t or "").strip().lower() in hay for t in tokens if t and t.strip())

def _has_all_in_list(need: List[str], have: List[str]) -> bool:
    have_l = [h.lower() for h in (have or [])]
    return all(n.strip().lower() in have_l for n in need if n and n.strip())

def is_eligible_strict(row: pd.Series, shift: pd.Series) -> bool:
    role_req = str(shift.get("RoleRequired","")).strip().lower()
    specialty = str(shift.get("Specialty","")).replace("-", " ").strip().lower()
    mandatory = [t.strip() for t in str(shift.get("MandatoryTraining","")).replace(",", "/").split("/") if t.strip()]
    ward = str(shift.get("WardDepartment","")).strip().lower()
    lang_req = str(shift.get("LanguageRequirement","")).strip().lower()

    role_ok = (row.get("JobRole","").strip().lower() == role_req) or _has_any([role_req], row.get("organizationPath",""))
    spec_ok = (not specialty) or _has_any([specialty], row.get("JobRole","")) \
              or _has_any([specialty], row.get("organizationPath","")) \
              or _has_any([specialty], " ".join(row.get("Certifications", [])))
    training_ok = (not mandatory) or _has_all_in_list(mandatory, row.get("Certifications", []))
    ward_ok = (not ward) or _has_any([ward], row.get("organizationPath",""))
    lang_ok = (not lang_req) or (lang_req in [l.lower() for l in (row.get("Languages") or [])])
    
    return role_ok and spec_ok and training_ok and ward_ok and lang_ok

def is_eligible_lenient(row: pd.Series, shift: pd.Series) -> bool:
    role_req = str(shift.get("RoleRequired","")).strip().lower()
    specialty = str(shift.get("Specialty","")).replace("-", " ").strip().lower()
    mandatory_raw = str(shift.get("MandatoryTraining",""))
    mandatory = [t.strip() for t in mandatory_raw.replace(",", "/").split("/") if t.strip()]
    ward = str(shift.get("WardDepartment","")).strip().lower()
    lang_req = str(shift.get("LanguageRequirement","")).strip().lower()

    text_all = " ".join([
        row.get("JobRole",""), row.get("organizationPath",""),
        " ".join((row.get("Certifications") or []))
    ]).lower()

    role_ok = (row.get("JobRole","").strip().lower() == role_req) or (role_req and role_req in text_all)
    spec_ok = (not specialty) or any(tok in text_all for tok in [specialty, specialty.replace(" ", ""), "icu"])
    training_ok = (not mandatory) or all(any(mtok in text_all for mtok in [m.lower(), m.lower().replace(" certified","")]) for m in mandatory)
    ward_ok = (not ward) or (ward in text_all)
    lang_ok = (not lang_req) or (lang_req in [l.lower() for l in (row.get("Languages") or [])])
    
    core_ok = role_ok or spec_ok or training_ok
    return core_ok and (not ward or ward_ok) and (not lang_req or lang_ok)

def gpt_decide(shift: pd.Series, eligible_df: pd.DataFrame) -> Dict[str, str]:
    if not HAS_OPENAI or openai is None or not os.getenv("OPENAI_API_KEY"):
        return {"action": "skip"}
    
    emp_list = []
    for _, r in eligible_df.iterrows():
        role_match = str(shift.get('RoleRequired','')) in (r.get("organizationPath") or "") or \
                     (r.get("JobRole","") == str(shift.get('RoleRequired','')))
        mand_tokens = [t.strip() for t in str(shift.get("MandatoryTraining","")).replace(",", "/").split("/") if t.strip()]
        cert_ok = _has_all_in_list(mand_tokens, r.get("Certifications") or [])
        score = int(bool(role_match)) + int(bool(cert_ok))
        
        emp_list.append({
            "fullName": r.get("fullName",""),
            "phoneNumber": r.get("phoneNumber",""),
            "organizationPath": r.get("organizationPath",""),
            "Certifications": r.get("Certifications",[]),
            "matchScore": score
        })
    
    prompt = f"""
You are an intelligent shift assignment assistant:
Shift Details:
- Department: {shift.get('Department','')}
- Required Role: {shift.get('RoleRequired','')}
- Specialty/Experience: {shift.get('Specialty','')}
- Mandatory Training: {shift.get('MandatoryTraining','')}
- Shift Type/Duration: {shift.get('ShiftType','')} / {shift.get('ShiftDuration','')}
- Ward/Department: {shift.get('WardDepartment','')}
- Language Required: {shift.get('LanguageRequirement','')}
- Time: {shift.get('ShiftTime','')}

Eligible Employees (matchScore=0..2): {emp_list if emp_list else 'None'}

Choose JSON only:
{{"action":"assign","employee":"Name"}} or {{"action":"notify","employee":"Name"}} or {{"action":"skip"}}
""".strip()
    
    try:
        resp = openai.ChatCompletion.create(
            model="gpt-4",
            messages=[
                {"role":"system","content":"You are an intelligent healthcare shift management assistant"},
                {"role":"user","content":prompt}
            ],
            temperature=0.4, 
            max_tokens=200,
        )
        import ast
        return ast.literal_eval(resp["choices"][0]["message"]["content"])
    except Exception as e:
        st.error(f"❌ GPT Error: {e}")
        return {"action": "skip"}

# =============================================================================
# Showcase helpers (unchanged)
# =============================================================================

def _append_demo_employee(df_employees: pd.DataFrame) -> pd.DataFrame:
    demo = {
        "personNumber": "D-1001",
        "organizationPath": "Hospital/A&E/ICU/Registered Nurse",
        "phoneNumber": "966500001234",
        "fullName": "Ali Mansour",
        "Certifications": ["BLS certified", "ALS certified", "ICU-trained"],
        "Languages": ["Arabic"],
        "JobRole": "Registered Nurse",
    }
    extra = pd.DataFrame([demo])
    cols = list(set(df_employees.columns) | set(extra.columns))
    df_employees = df_employees.reindex(columns=cols)
    extra = extra.reindex(columns=cols)
    return pd.concat([df_employees, extra], ignore_index=True)

def _score_match(row: pd.Series, shift: pd.Series) -> int:
    text_all = " ".join([
        row.get("JobRole",""), row.get("organizationPath",""),
        " ".join((row.get("Certifications") or [])), " ".join((row.get("Languages") or []))
    ]).lower()
    
    role_req = str(shift.get("RoleRequired","")).strip().lower()
    specialty = str(shift.get("Specialty","")).replace("-", " ").strip().lower()
    ward = str(shift.get("WardDepartment","")).strip().lower()
    lang = str(shift.get("LanguageRequirement","")).strip().lower()
    
    pts = 0
    if role_req and role_req in text_all: pts += 3
    if specialty and ("icu" in text_all or specialty.replace(" ", "") in text_all): pts += 2
    if ward and ward in text_all: pts += 1
    if lang and lang in text_all: pts += 1
    if any(tok in text_all for tok in ["bls", "als"]): pts += 2
    
    return pts

# =============================================================================
# Agent runner (same logic, safer ops)
# =============================================================================

def run_agent(
    employee_ids: Iterable[int],
    df_shifts: pd.DataFrame,
    showcase: bool = False,
    lenient: bool = False,
):
    events: List[Dict[str, Any]] = []
    shift_assignment_results: List[tuple] = []
    reasoning_rows: List[Dict[str, Any]] = []

    # 1) Fetch employees
    t0 = time.perf_counter()
    df_employees = fetch_employees(employee_ids)
    if showcase and len(df_employees) < 3:
        df_employees = _append_demo_employee(df_employees)
    dur = (time.perf_counter() - t0) * 1000
    
    loaded_txt = f"{len(df_employees)} employee(s) loaded successfully." if not df_employees.empty else "No employees returned or credentials invalid."
    events.append({
        "title": "Fetch employees", 
        "desc": loaded_txt, 
        "status": "done", 
        "dur_ms": max(int(dur), 1)
    })

    # 2) Evaluate shifts
    t_eval = time.perf_counter()
    events.append({
        "title": "Evaluate open shifts", 
        "desc": "Matching employees vs. role/specialty/training/ward…", 
        "status": "done"
    })

    elig_fn = is_eligible_lenient if lenient else is_eligible_strict

    for _, shift in df_shifts.iterrows():
        eligible = df_employees[df_employees.apply(lambda r: elig_fn(r, shift), axis=1)] if not df_employees.empty else pd.DataFrame()

        # 3) AI decision (or showcase distribution)
        t_ai = time.perf_counter()
        if showcase:
            ranked = df_employees.copy()
            ranked["__score"] = ranked.apply(lambda r: _score_match(r, shift), axis=1)
            ranked = ranked.sort_values("__score", ascending=False).reset_index(drop=True)
            
            cand_assign = ranked.iloc[0] if len(ranked) > 0 else None
            cand_notify = ranked.iloc[1] if len(ranked) > 1 else None
            cand_skip = ranked.iloc[-1] if len(ranked) > 2 else None

            # add explicit "AI decision" event first
            ai_dur = (time.perf_counter() - t_ai) * 1000
            events.append({
                "title": "AI decision", 
                "desc": "Showcase distribution Assign/Notify/Skip", 
                "status": "done", 
                "dur_ms": max(int(ai_dur), 1)
            })

            if cand_assign is not None:
                name = cand_assign.get("fullName", "Candidate A")
                events.append({
                    "title": f"Assigned {name}", 
                    "desc": f"{name} → {shift.get('Department','')} ({shift.get('ShiftType','')}, {shift.get('ShiftDuration','')})", 
                    "status": "done"
                })
                shift_assignment_results.append((name, shift["ShiftID"], "✅ Auto-Filled"))

            if cand_notify is not None:
                name = cand_notify.get("fullName", "Candidate B")
                events.append({
                    "title": f"Notify {name}", 
                    "desc": f"Send notification for {shift['ShiftID']}", 
                    "status": "done"
                })
                shift_assignment_results.append((name, shift["ShiftID"], "📨 Notify"))

            if cand_skip is not None:
                name = cand_skip.get("fullName", "Candidate C")
                events.append({
                    "title": "Skipped", 
                    "desc": f"No eligible employees or decision skipped (e.g., low match for {name})", 
                    "status": "done"
                })
                shift_assignment_results.append((name, shift["ShiftID"], "⚠️ Skipped"))

        else:
            events.append({
                "title": "AI decision", 
                "desc": "Select assign / notify / skip", 
                "status": "done"
            })
            decision = gpt_decide(shift, eligible)
            ai_dur = (time.perf_counter() - t_ai) * 1000
            events[-1]["dur_ms"] = max(int(ai_dur), 1)

            if decision.get("action") == "assign":
                emp = decision.get("employee")
                events.append({
                    "title": f"Assigned {emp}", 
                    "desc": f"{emp} → {shift.get('Department','')} ({shift.get('ShiftType','')}, {shift.get('ShiftDuration','')})", 
                    "status": "done"
                })
                shift_assignment_results.append((emp, shift["ShiftID"], "✅ Auto-Filled"))
            elif decision.get("action") == "notify":
                emp = decision.get("employee")
                events.append({
                    "title": f"Notify {emp}", 
                    "desc": f"Send notification for {shift['ShiftID']}", 
                    "status": "done"
                })
                shift_assignment_results.append((emp, shift["ShiftID"], "📨 Notify"))
            else:
                events.append({
                    "title": "Skipped", 
                    "desc": "No eligible employees or decision skipped", 
                    "status": "done"
                })
                shift_assignment_results.append(("❌ No eligible", shift["ShiftID"], "⚠️ Skipped"))

        # Generate reasoning rows
        for _, emp_row in df_employees.iterrows():
            role_match = str(shift.get("RoleRequired","")).strip().lower() == emp_row.get("JobRole","").strip().lower()
            spec = str(shift.get("Specialty","")).replace("-", " ").strip().lower()
            text_all = (emp_row.get("JobRole","")+" "+emp_row.get("organizationPath","")+" "+" ".join(emp_row.get("Certifications",[]))).lower()
            spec_ok = (spec and ("icu" in text_all or spec.replace(" ", "") in text_all))
            
            training_need = [t.strip().lower() for t in str(shift.get("MandatoryTraining","")).replace(",", "/").split("/") if t.strip()]
            training_ok_strict = all(t in [c.lower() for c in (emp_row.get("Certifications",[]) or [])] for t in training_need)
            training_ok_len = all(any(mtok in text_all for mtok in [t, t.replace(" certified","")]) for t in training_need) if training_need else True
            
            ward_ok = not str(shift.get("WardDepartment","")).strip() or str(shift.get("WardDepartment","")).lower() in (emp_row.get("organizationPath","").lower())
            lang_req = str(shift.get("LanguageRequirement","")).strip().lower()
            lang_ok = (not lang_req) or (lang_req in [l.lower() for l in (emp_row.get("Languages") or [])])

            if lenient:
                status = "✅ Eligible" if (role_match or spec_ok or training_ok_len) and ward_ok and lang_ok else "❌ Not Eligible"
            else:
                status = "✅ Eligible" if ((role_match or spec_ok) and training_ok_strict and ward_ok and lang_ok) else "❌ Not Eligible"

            reasoning_rows.append({
                "Employee": emp_row.get("fullName",""),
                "ShiftID": shift["ShiftID"],
                "Eligible": status,
                "Reasoning": " | ".join([
                    "✅ Role" if role_match else "❌ Role",
                    "✅ Specialty" if spec_ok else "❌ Specialty",
                    "✅ Training" if (training_ok_len if lenient else training_ok_strict) else "❌ Training",
                    "✅ Ward" if ward_ok else "❌ Ward",
                    f"Lang: {shift.get('LanguageRequirement','') or '—'}" + (" ✅" if lang_ok else " ❌")
                ]),
                "Certifications": ", ".join([str(x).strip() for x in (emp_row.get("Certifications", []) or []) if x])
            })

    eval_dur = (time.perf_counter() - t_eval) * 1000
    for e in events:
        if e["title"] == "Evaluate open shifts":
            e["dur_ms"] = max(int(eval_dur), 1)
            break

    events.append({
        "title": "Summary ready", 
        "desc": "AI finished processing shifts", 
        "status": "current"
    })
    
    return events, shift_assignment_results, reasoning_rows

# =============================================================================
# UI Components
# =============================================================================

def render_hero():
    st.markdown("""
    <div class="hero-section">
        <div class="hero-content">
            <div class="hero-logo">
                <img src="https://www.healthmatrixcorp.com/web/image/website/1/logo/Health%20Matrix?unique=956ad7b" alt="Health Matrix" />
            </div>
            <h1 class="hero-title">
                <span class="gradient-text">AI Command Center</span><br>
                <span class="subtitle">Smart Staffing & Actions</span>
            </h1>
            <p class="hero-description">
                Transform healthcare delivery with AI-driven workforce management, predictive analytics, 
                and intelligent automation that ensures compliance while optimizing patient care.
            </p>
            <div class="hero-stats">
                <div class="stat-item"><div class="stat-value">99.9%</div><div class="stat-label">Compliance Rate</div></div>
                <div class="stat-item"><div class="stat-value">45%</div><div class="stat-label">Efficiency Gain</div></div>
                <div class="stat-item"><div class="stat-value">&lt;2min</div><div class="stat-label">Response Time</div></div>
            </div>
        </div>
    </div>
    """, unsafe_allow_html=True)

def render_kpi_cards(assigned: int, notified: int, skipped: int):
    st.markdown(f"""
    <div class="kpi-grid">
        <div class="kpi-card kpi-assigned">
            <div class="kpi-icon">✅</div>
            <div class="kpi-value">{assigned}</div>
            <div class="kpi-label">Auto-Assigned</div>
            <div class="kpi-change">+15%</div>
        </div>
        <div class="kpi-card kpi-notify">
            <div class="kpi-icon">📨</div>
            <div class="kpi-value">{notified}</div>
            <div class="kpi-label">Notifications Sent</div>
            <div class="kpi-change">-8%</div>
        </div>
        <div class="kpi-card kpi-skipped">
            <div class="kpi-icon">⚠️</div>
            <div class="kpi-value">{skipped}</div>
            <div class="kpi-label">Skipped</div>
            <div class="kpi-change">-23%</div>
        </div>
    </div>
    """, unsafe_allow_html=True)

def _timeline_html(events: List[Dict[str, Any]]) -> str:
    items = []
    for event in events:
        status_class = event.get("status", "done")
        duration = event.get("dur_ms", 0)
        duration_text = f"{int(duration)} ms" if duration else "—"
        items.append(f"""
        <div class="timeline-item {status_class}">
            <div class="timeline-marker"></div>
            <div class="timeline-content">
                <div class="timeline-title">{event.get('title', '')}</div>
                <div class="timeline-meta">Duration: {duration_text}</div>
                <div class="timeline-desc">{event.get('desc', '')}</div>
            </div>
        </div>""")
    return f"""<div class="timeline-wrap">
        <h3>🕒 Agent Timeline</h3>
        <div class="timeline">{''.join(items)}</div>
    </div>"""

def render_timeline(events: List[Dict[str, Any]]):
    html = f"""<!doctype html>
<html><head><meta charset="utf-8"><style>{_EMBEDDED_CSS}</style></head>
<body>{_timeline_html(events)}</body></html>"""
    # Use components.html to avoid any Markdown escaping
    components.html(html, height=520, scrolling=True)

def render_employee_board(shift_results: List[tuple], reasoning_rows: List[Dict[str, Any]]):
    action_by_emp, shift_by_emp = {}, {}
    for emp, sid, status in shift_results:
        if emp and not str(emp).startswith("❌"):
            action_by_emp[str(emp)] = status
            shift_by_emp[str(emp)] = str(sid)

    seen = set()
    employees = []
    for r in reasoning_rows:
        emp = (r.get("Employee") or "").strip()
        if not emp or emp in seen:
            continue
        seen.add(emp)
        employees.append({
            "name": emp,
            "shift": r.get("ShiftID", ""),
            "eligible": r.get("Eligible", ""),
            "reasoning": r.get("Reasoning", ""),
            "certs": r.get("Certifications", ""),
            "action": action_by_emp.get(emp, "")
        })

    if not employees:
        st.info("No employee details available.")
        return

    lanes = {"assigned": [], "notify": [], "skipped": []}
    for emp in employees:
        action = (emp["action"] or "").lower()
        if "auto-filled" in action or "assign" in action:
            lane = "assigned"
        elif "notify" in action:
            lane = "notify"
        else:
            lane = "notify" if emp["eligible"].startswith("✅") else "skipped"
        lanes[lane].append(emp)

    st.markdown(f"""
    <div class="kanban-board">
        <div class="kanban-lane">
            <div class="lane-header assigned-header"><span class="lane-icon">✅</span>
                <span class="lane-title">Assigned</span><span class="lane-count">{len(lanes["assigned"])}</span></div>
            <div class="lane-content">
    """, unsafe_allow_html=True)
    for emp in lanes["assigned"]:
        render_employee_card(emp, "assigned")

    st.markdown(f"""
            </div>
        </div>
        <div class="kanban-lane">
            <div class="lane-header notify-header"><span class="lane-icon">📨</span>
                <span class="lane-title">Notify</span><span class="lane-count">{len(lanes["notify"])}</span></div>
            <div class="lane-content">
    """, unsafe_allow_html=True)
    for emp in lanes["notify"]:
        render_employee_card(emp, "notify")

    st.markdown(f"""
            </div>
        </div>
        <div class="kanban-lane">
            <div class="lane-header skipped-header"><span class="lane-icon">⚠️</span>
                <span class="lane-title">Skipped</span><span class="lane-count">{len(lanes["skipped"])}</span></div>
            <div class="lane-content">
    """, unsafe_allow_html=True)
    for emp in lanes["skipped"]:
        render_employee_card(emp, "skipped")
    st.markdown("</div></div></div>", unsafe_allow_html=True)

def render_employee_card(emp: Dict[str, Any], lane: str):
    name = emp["name"]
    shift = emp["shift"]
    reasoning = emp["reasoning"]
    certs = emp["certs"]
    action = emp["action"]
    initials = "".join([w[0] for w in name.split()[:2]]).upper() if name else "?"
    checks = [x.strip() for x in str(reasoning or "").split("|") if x and x.strip()]
    badges_html = ""
    for check in checks:
        is_ok = "✅" in check
        label = check.replace("✅", "").replace("❌", "").strip()
        badge_class = "badge-ok" if is_ok else "badge-fail"
        badges_html += f'<div class="emp-badge {badge_class}"><span class="badge-dot"></span><span>{label}</span></div>'
    chip_text = action or ("✅ Eligible" if emp["eligible"].startswith("✅") else "❌ Not Eligible")
    chip_class = "chip-ok" if ("✅" in chip_text or "Auto-Filled" in chip_text) else ("chip-warn" if "Notify" in chip_text else "chip-fail")

    st.markdown(f"""
    <div class="employee-card {lane}-card">
        <div class="emp-header">
            <div class="emp-avatar">{initials}</div>
            <div class="emp-info">
                <div class="emp-name">{name}</div>
                <div class="emp-shift">Shift: {shift}</div>
            </div>
            <div class="emp-chip {chip_class}">{chip_text}</div>
        </div>
        <div class="emp-divider"></div>
        <div class="emp-badges">
            {badges_html if badges_html else '<div class="emp-badge badge-info"><span class="badge-dot"></span><span>No reasoning available</span></div>'}
        </div>
        <div class="emp-footer">
            <div class="emp-certs">🪪 Certifications: {certs or "—"}</div>
            <div class="emp-meta">Updated just now</div>
        </div>
    </div>
    """, unsafe_allow_html=True)

def render_status_indicator(status: str, message: str):
    if status == "processing":
        st.markdown(f"""
        <div class="status-indicator processing">
            <div class="status-dot pulsing"></div><span>{message}</span>
        </div>""", unsafe_allow_html=True)
    elif status == "completed":
        st.markdown(f"""
        <div class="status-indicator completed">
            <div class="status-dot completed"></div><span>{message}</span>
        </div>""", unsafe_allow_html=True)
    else:
        st.markdown(f"""
        <div class="status-indicator ready">
            <div class="status-dot ready"></div><span>{message}</span>
        </div>""", unsafe_allow_html=True)

# =============================================================================
# Main Application
# =============================================================================

def main():
    setup_page()
    render_hero()

    st.markdown("""
    <div class="business-case">
        <h4>Business Case — Open Shifts Auto-Fulfillment</h4>
        <ul>
            <li>✅ Reduce time to fill critical shifts</li>
            <li>📜 Enforce mandatory certifications & policies</li>
            <li>📊 Transparent Agent Timeline (steps + durations)</li>
        </ul>
    </div>
    """, unsafe_allow_html=True)
    
    with st.expander("🔧 Demo / Showcase Controls", expanded=False):
        col1, col2 = st.columns(2)
        with col1:
            show_showcase = st.checkbox("Ensure Assign + Notify + Skip (adds a demo nurse if needed)", value=True)
            lenient_mode = st.checkbox("Lenient eligibility (OR) for demo", value=True)
        with col2:
            st.caption("When these options are disabled, behavior returns to original strict logic (AND).")
    
    status_placeholder = st.empty()
    with status_placeholder.container():
        render_status_indicator("ready", "Ready – Click 'Start AI Agent' to evaluate shifts")
    
    # Demo data
    employee_ids_default = [850, 825, 4503]
    shift_data = """ShiftID,Department,RoleRequired,Specialty,MandatoryTraining,ShiftType,ShiftDuration,WardDepartment,LanguageRequirement,ShiftTime
S101,ICU,Registered Nurse,ICU-trained,BLS/ALS certified,Night,12h,A&E,Arabic,2025-06-04 07:00"""
    df_shifts_default = pd.read_csv(StringIO(shift_data))
    
    st.markdown('<div class="cta-container">', unsafe_allow_html=True)
    if st.button("▶️ Start AI Agent", key="start_agent", help="Let the AI handle the work"):
        with status_placeholder.container():
            render_status_indicator("processing", "Processing – contacting UKG APIs and evaluating shifts...")
        try:
            events, shift_assignment_results, reasoning_rows = run_agent(
                employee_ids_default, 
                df_shifts_default,
                showcase=show_showcase, 
                lenient=lenient_mode
            )
            with status_placeholder.container():
                render_status_indicator("completed", "Completed – results below")
            
            assigned = sum(1 for e in shift_assignment_results if "Auto-Filled" in e[2])
            notified = sum(1 for e in shift_assignment_results if "Notify" in e[2])
            skipped = sum(1 for e in shift_assignment_results if "Skip" in e[2] or "Skipped" in e[2])
            
            col1, col2 = st.columns([1, 2], gap="large")
            with col1:
                render_timeline(events)
            with col2:
                total_steps = len(events)
                total_ms = sum(int(e.get("dur_ms", 0) or 0) for e in events)
                st.markdown(f"""
                <div class="overview-metrics">
                    <div class="metric-chip">🧠 <strong>Agent Steps:</strong> {total_steps}</div>
                    <div class="metric-chip">⏱️ <strong>Total Duration:</strong> {total_ms} ms</div>
                    <div class="metric-chip">✅ <strong>Assigned:</strong> {assigned}</div>
                    <div class="metric-chip">📬 <strong>Notify:</strong> {notified}</div>
                    <div class="metric-chip">⚠️ <strong>Skipped:</strong> {skipped}</div>
                </div>""", unsafe_allow_html=True)
                render_kpi_cards(assigned, notified, skipped)
                st.markdown("### 👥 Employee Results")
                render_employee_board(shift_assignment_results, reasoning_rows)
                with st.expander("Raw Summary (optional)", expanded=False):
                    if shift_assignment_results:
                        st.markdown("**Assignment Summary:**")
                        st.dataframe(pd.DataFrame(shift_assignment_results, columns=["Employee", "ShiftID", "Status"]), use_container_width=True)
                    if reasoning_rows:
                        st.markdown("**Detailed Reasoning:**")
                        st.dataframe(pd.DataFrame(reasoning_rows), use_container_width=True)
        except Exception as e:
            st.error(f"Unexpected error: {e}")
            with status_placeholder.container():
                render_status_indicator("ready", "Error occurred – please try again")
    st.markdown('</div>', unsafe_allow_html=True)
    
    st.markdown("""
    <div class="footer">
        <hr/>
        <div class="footer-content">
            © 2025 Health Matrix Corp – Empowering Digital Health Transformation · 
            <a href="mailto:info@healthmatrixcorp.com">info@healthmatrixcorp.com</a>
        </div>
    </div>
    """, unsafe_allow_html=True)

if __name__ == "__main__":
    main()