Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,104 +1,365 @@
+"""Streamlit application for Health Matrix AI Command Center.
+
+This standalone script merges the functionality of three separate files—
+the Streamlit front‑end, custom CSS styling, and UKG API helpers—into a
+single file. It provides a landing page that introduces the AI agent and
+allows users to run scheduling logic with an attractive timeline of actions.
+
+The CSS styling is embedded directly within this script to avoid external
+dependencies. Helper functions for interacting with the UKG (Kronos) API are
+included here as well, avoiding the need for a separate `schedule.py` module.
+
+If your environment does not provide valid UKG authentication or an
+OpenAI API key, the application will still run—failing gracefully and
+displaying warnings instead of crashing.
+
+Usage:
+    streamlit run merged_app.py
+"""
+
 import os
+import json
 import pathlib
-import streamlit as st
-import pandas as pd
+from typing import List, Optional, Iterable, Dict, Any
 from io import StringIO
-import openai
 
-# استيراد دالة جلب الموظفين من ملف schedule ضمن حزمة ukg
-from ukg.schedule import fetch_employees
+import pandas as pd
+import requests
+import streamlit as st
 
-# إعداد متغيرات البيئة لتفادي مشاكل الكتابة في مجلدات محظورة
-os.environ["XDG_CONFIG_HOME"] = "/tmp"
-os.environ["HF_HOME"] = "/tmp"
-os.environ["TRANSFORMERS_CACHE"] = "/tmp"
+# Optional: import OpenAI if available. If not installed, the application
+# will still run but GPT decisions will always skip.
+try:
+    import openai  # type: ignore
+    HAS_OPENAI = True
+except ImportError:
+    openai = None  # type: ignore
+    HAS_OPENAI = False
 
-# إعداد مفتاح GPT
-openai.api_key = os.getenv("OPENAI_API_KEY")
+# -----------------------------------------------------------------------------
+# Embedded CSS from `style (6).css`
+# The original styling file defined the layout, floating circles, button
+# appearance, and timeline card aesthetics. Embedding it here avoids the
+# overhead of reading an external file and ensures all styles load together.
+_EMBEDDED_CSS = """
+/* === General Layout === */
+html, body {
+  margin: 0;
+  padding: 0;
+  height: 100%;
+  /* Use the primary font family suggested by the brief */
+  font-family: 'Segoe UI', 'Open Sans', sans-serif;
+  /* Dark, elegant backdrop inspired by premium tech sites */
+  background: linear-gradient(145deg, #002d5b 0%, #001a33 100%);
+  overflow: hidden;
+  color: #f5faff;
+}
 
-# إعداد صفحة Streamlit
-st.set_page_config(page_title="Health Matrix AI Command Center", layout="wide")
+/* === Floating Animated Circles === */
+.background {
+  position: fixed;
+  inset: 0;
+  overflow: hidden;
+  z-index: 0;
+}
+@keyframes floatAround {
+  0%   { transform: translate(0, 0) scale(1); }
+  25%  { transform: translate(50px, -30px) scale(1.2); }
+  50%  { transform: translate(-40px, 60px) scale(1.1); }
+  75%  { transform: translate(30px, -50px) scale(1.3); }
+  100% { transform: translate(0, 0) scale(1); }
+}
+.circle {
+  position: absolute;
+  width: 320px;
+  height: 320px;
+  background: radial-gradient(circle, #004c97, #36ba01);
+  border-radius: 50%;
+  opacity: 0.12;
+  animation: floatAround 12s ease-in-out infinite;
+  filter: blur(20px);
+}
+.circle:nth-child(1) { top: 10%; left: 20%; animation-delay: 0s; }
+.circle:nth-child(2) { top: 60%; left: 70%; animation-delay: 4s; }
+.circle:nth-child(3) { top: 80%; left: 30%; animation-delay: 8s; }
+.circle:nth-child(4) { top: 40%; left: 50%; animation-delay: 2s; }
+.circle:nth-child(5) { top: 70%; left: 10%; animation-delay: 6s; }
+.circle:nth-child(6) { top: 20%; left: 80%; animation-delay: 1s; }
 
-# تحميل ملف CSS من نفس مجلد هذا الملف
-current_dir = pathlib.Path(__file__).resolve().parent
-css_file = current_dir / "style.css"
-if css_file.exists():
-    with open(css_file, encoding="utf-8") as f:
-        st.markdown(f"<style>{f.read()}</style>", unsafe_allow_html=True)
+/* === Override default Streamlit button styling === */
+.stButton > button {
+  background: linear-gradient(90deg, #004c97, #36BA01);
+  color: white;
+  border: none;
+  border-radius: 50px;
+  padding: 1rem 2rem;
+  font-size: 1.2rem;
+  font-weight: 600;
+  cursor: pointer;
+  box-shadow: 0 0 25px rgba(54, 186, 1, 0.4);
+  transition: transform 0.3s ease-in-out;
+  animation: pulseAI 2.5s infinite;
+}
+.stButton > button:hover { transform: scale(1.05); }
+@keyframes pulseAI {
+  0%   { transform: scale(1); }
+  50%  { transform: scale(1.03); }
+  100% { transform: scale(1); }
+}
+
+/* === Timeline styles === */
+.timeline {
+  margin-top: 2rem;
+  z-index: 2;
+}
+.timeline-card {
+  display: flex;
+  align-items: flex-start;
+  background: rgba(255, 255, 255, 0.08);
+  border-left: 6px solid #36ba01;
+  border-radius: 12px;
+  padding: 1.2rem;
+  margin-bottom: 1rem;
+  backdrop-filter: blur(6px);
+  box-shadow: 0 6px 12px rgba(0, 0, 0, 0.15);
+}
+.timeline-card .icon {
+  font-size: 1.6rem;
+  line-height: 1;
+  margin-right: 1rem;
+  color: #36ba01;
+}
+.timeline-card h4 {
+  margin: 0;
+  font-size: 1.1rem;
+  color: #e5f0fb;
+}
+.timeline-card p {
+  margin: 0.3rem 0 0;
+  font-size: 0.9rem;
+  color: #a9bcd4;
+}
+"""
 
-# إضافة الخلفية المتحركة
-st.markdown(
+# -----------------------------------------------------------------------------
+# UKG API helper functions (merged from schedule (3).py)
+
+def _get_auth_header() -> Dict[str, str]:
+    """Construct the HTTP headers required for UKG API requests.
+
+    The UKG API uses both an app key and a bearer token for authorization.
+    These values are read from environment variables ``UKG_APP_KEY`` and
+    ``UKG_AUTH_TOKEN``. If either variable is missing, a Streamlit warning is
+    displayed. The function returns a dictionary of headers for use with
+    ``requests``.
     """
-<div class="background">
-  <div class="circle"></div>
-  <div class="circle"></div>
-  <div class="circle"></div>
-  <div class="circle"></div>
-  <div class="circle"></div>
-  <div class="circle"></div>
-</div>
-""",
-    unsafe_allow_html=True,
-)
+    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 and/or 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 "",
+    }
 
-# عرض شعار Health Matrix في الزاوية العلوية اليسرى باستخدام الصورة المحلية
-logo_path = current_dir / "logo.jpg"
-if logo_path.exists():
-    logo_url = str(logo_path.as_posix())
-    st.markdown(
-        f"""
-<div style="position:absolute; top:1rem; left:1rem; z-index:2;">
-  <img src="file://{logo_url}" width="140" alt="Health Matrix Logo"/>
-</div>
-""",
-        unsafe_allow_html=True,
+
+def fetch_open_shifts(
+    start_date: str = "2000-01-01",
+    end_date: str = "3000-01-01",
+    location_ids: Optional[Iterable[str]] = None,
+) -> pd.DataFrame:
+    """Retrieve open shift instances from the UKG demo API.
+
+    This helper sends a POST request with the given date range and optional
+    location identifiers. If the API returns data, the function constructs
+    a DataFrame with selected fields. On failure, an empty DataFrame is
+    returned and an error is logged in the Streamlit interface.
+    """
+    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:
+        response = requests.post(url, headers=headers, json=payload)
+        response.raise_for_status()
+        data = response.json()
+        open_shifts = data.get("openShifts", [])
+        rows: List[Dict[str, Any]] = []
+        for shift in open_shifts:
+            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()
+
 
-# --------------------
-# محتوى الصفحة الرئيسية (Landing Page)
+def fetch_location_data(date: str = "2025-07-13", query: str = "Medsurg") -> pd.DataFrame:
+    """Retrieve location information from the UKG demo API.
 
-# عنوان وتعريف قصير
-st.markdown(
+    This helper composes a POST request containing a search query and
+    returns location attributes in a DataFrame. If the call fails,
+    an empty DataFrame is returned and an error is displayed.
     """
-<div style="margin-top:5rem; text-align:center;">
-  <h1 style="font-size:3rem; font-weight:600; margin-bottom:0.5rem; color:#36ba01;">Welcome to the AI‑Powered Command Center</h1>
-  <h2 style="font-size:1.8rem; font-weight:500; margin-top:0; color:#004c97;">by Health Matrix</h2>
-  <p style="max-width:700px; margin:0 auto; font-size:1.1rem; line-height:1.5; color:#a9bcd4;">
-    This smart assistant leverages AI to automate decisions, schedule actions, and provide real‑time updates – all while you relax.
-  </p>
-</div>
-""",
-    unsafe_allow_html=True,
-)
+    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:
+        response = requests.post(url, headers=headers, json=payload)
+        response.raise_for_status()
+        data = response.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()
 
-# يمكن لاحقاً عرض الخط الزمني بعد اكتمال تنفيذ الوكيل
 
-# تخصيص الأسماء الافتراضية للموظفين والشفتات
-employee_ids_default = [850]
-shift_data = """
-ShiftID,Department,RequiredSkill,RequiredCert,ShiftTime
-S101,ICU,ICU,ACLS,2025-06-04 07:00
-"""
-df_shifts_default = pd.read_csv(StringIO(shift_data))
+def fetch_employees(employee_ids: Iterable[int]) -> pd.DataFrame:
+    """Fetch employee details from the UKG demo API.
+
+    For each identifier provided, this function calls the persons endpoint
+    and extracts person number, full name, phone number and organizational
+    path. It derives a ``JobRole`` from the final segment of the path.
+    If no records are found, a DataFrame with expected columns is returned
+    so downstream logic does not encounter missing keys.
+
+    Parameters
+    ----------
+    employee_ids : Iterable[int]
+        Identifiers of employees to retrieve.
+
+    Returns
+    -------
+    pandas.DataFrame
+        A DataFrame of employee details. Empty if no employees could be
+        retrieved.
+    """
+    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)
+            if resp.status_code == 200:
+                data = resp.json()
+                person_info = data.get("personInformation", {}).get("person", {})
+                person_number = person_info.get("personNumber")
+                full_name = person_info.get("fullName", "")
+                # Extract organization path from primary labor accounts
+                org_path = ""
+                primary_accounts = (
+                    data.get("jobAssignment", {}).get("primaryLaborAccounts", [])
+                )
+                if primary_accounts:
+                    org_path = primary_accounts[0].get("organizationPath", "")
+                # Extract first phone number if present
+                phone = ""
+                phones = data.get("personInformation", {}).get("telephoneNumbers", [])
+                if phones:
+                    phone = phones[0].get("phoneNumber", "")
+                return {
+                    "personNumber": person_number,
+                    "organizationPath": org_path,
+                    "phoneNumber": phone,
+                    "fullName": full_name,
+                }
+            else:
+                st.warning(f"⚠️ Could not fetch employee {emp_id}: {resp.status_code}")
+        except Exception as e:
+            st.error(f"❌ Error fetching employee {emp_id}: {e}")
+        return None
+
+    records: List[Dict[str, Any]] = []
+    for emp_id in employee_ids:
+        data = fetch_employee_data(emp_id)
+        if data:
+            records.append(data)
+    # Build DataFrame with guaranteed columns
+    df_employees = pd.DataFrame(records)
+    for col in ["personNumber", "organizationPath", "phoneNumber", "fullName"]:
+        if col not in df_employees.columns:
+            df_employees[col] = []
+    def derive_role(path: Any) -> str:
+        if isinstance(path, str) and path:
+            return path.split("/")[-1]
+        return ""
+    df_employees["JobRole"] = df_employees["organizationPath"].apply(derive_role)
+    return df_employees
 
-def is_eligible(row, shift):
-    """Check if employee's job role matches shift requirement."""
+# -----------------------------------------------------------------------------
+# GPT decision helper and timeline rendering
+
+def is_eligible(row: pd.Series, shift: pd.Series) -> bool:
+    """Determine if a given employee record matches the required skill."""
     return row.get("JobRole", "").strip().lower() == shift["RequiredSkill"].strip().lower()
 
-def gpt_decide(shift, eligible_df):
-    """Call GPT model to decide assignment or notification.
 
-    The function returns a dict with keys action and employee, defaulting to skip if any error occurs
-    or the API key isn't configured.
+def gpt_decide(shift: pd.Series, eligible_df: pd.DataFrame) -> Dict[str, str]:
+    """Invoke OpenAI GPT model to decide assignment or notification.
+
+    If an API key is not configured or the model invocation fails, the
+    function defaults to returning ``{"action": "skip"}``.
     """
-    # If API key is not set, skip decision automatically
-    if not openai.api_key:
+    if not HAS_OPENAI or openai is None or not os.getenv("OPENAI_API_KEY"):
         return {"action": "skip"}
-    # Build prompt listing eligible employees
     emp_list = (
         eligible_df[["fullName", "phoneNumber", "organizationPath"]].to_dict(orient="records")
-        if not eligible_df.empty
-        else []
+        if not eligible_df.empty else []
     )
     prompt = f"""
 أنت مساعد ذكي مسؤول عن إدارة المناوبات الطبية.
@@ -109,7 +370,7 @@ def gpt_decide(shift, eligible_df):
 - وقت الشفت: {shift['ShiftTime']}
 
 الموظفون المؤهلون بناءً على الوظيفة:
-{emp_list if emp_list else "لا يوجد موظفون مؤهلون"}
+{emp_list if emp_list else 'لا يوجد موظفون مؤهلون'}
 
 اختر أحدهم باستخدام التنسيق التالي:
 {{"action": "assign", "employee": "الاسم"}}
@@ -134,8 +395,9 @@ def gpt_decide(shift, eligible_df):
         st.error(f"❌ GPT Error: {e}")
         return {"action": "skip"}
 
-def render_timeline(events):
-    """Return HTML string rendering a list of timeline events."""
+
+def render_timeline(events: List[Dict[str, str]]) -> str:
+    """Convert a list of events into an HTML timeline."""
     html = '<div class="timeline">'
     for event in events:
         icon = event.get("icon", "")
@@ -153,15 +415,22 @@ def render_timeline(events):
     html += "</div>"
     return html
 
-def run_agent(employee_ids, df_shifts):
-    """Execute the AI agent workflow and return events, summary, reasoning."""
-    events = []
-    shift_assignment_results = []
-    reasoning_rows = []
+
+def run_agent(employee_ids: Iterable[int], df_shifts: pd.DataFrame):
+    """Execute the AI agent workflow and produce a timeline.
+
+    Steps:
+      1. Fetch employees from UKG API.
+      2. Evaluate each shift and determine assignments via GPT or simple logic.
+      3. Generate reasoning rows for display.
+    Returns a tuple (events, shift_assignment_results, reasoning_rows).
+    """
+    events: List[Dict[str, str]] = []
+    shift_assignment_results: List[tuple] = []
+    reasoning_rows: List[Dict[str, Any]] = []
 
     # Step 1: Fetch employees
     events.append({"icon": "🔍", "title": "Fetching employee data", "desc": "Loading employee information from UKG API..."})
-    # Attempt to fetch employee data
     df_employees = fetch_employees(employee_ids)
     if df_employees.empty:
         events.append({
@@ -169,20 +438,16 @@ def run_agent(employee_ids, df_shifts):
             "title": "No Employee Data",
             "desc": "No employees were returned or credentials are invalid."
         })
-        # even if no employees, proceed to next steps with empty DataFrame
     else:
         events.append({
             "icon": "✅",
             "title": "Employee Data Loaded",
             "desc": f"{len(df_employees)} employee(s) loaded successfully."
         })
-    # timeline update deferred until end
 
     # Step 2: Evaluate each shift
     events.append({"icon": "📋", "title": "Evaluating Shifts", "desc": "Matching employees to shift requirements..."})
-    # Loop through shifts
     for _, shift in df_shifts.iterrows():
-        # Determine eligible employees based on JobRole
         eligible = df_employees[
             df_employees.apply(lambda r: is_eligible(r, shift), axis=1)
         ] if not df_employees.empty else pd.DataFrame()
@@ -212,60 +477,130 @@ def run_agent(employee_ids, df_shifts):
                 "desc": "No eligible employees available or decision skipped."
             })
             shift_assignment_results.append(("❌ No eligible", shift["ShiftID"], "⚠️ Skipped"))
-        # timeline update deferred until end
-        # Add reasoning for each employee
+        # Collect reasoning rows for each employee
         for _, emp_row in df_employees.iterrows():
             role_match = shift["RequiredSkill"].strip().lower() == emp_row.get("JobRole", "").strip().lower()
             cert_ok = True
             avail_ok = True
             ot_ok = True
             status = "✅ Eligible" if all([role_match, cert_ok, avail_ok, ot_ok]) else "❌ Not Eligible"
-            reasoning_rows.append(
-                {
-                    "Employee": emp_row.get("fullName", ""),
-                    "ShiftID": shift["ShiftID"],
-                    "Eligible": status,
-                    "Reasoning": " | ".join(
-                        [
-                            "✅ Role Match" if role_match else "❌ Role",
-                            "✅ Cert" if cert_ok else "❌ Cert",
-                            "✅ Avail" if avail_ok else "❌ Avail",
-                            "✅ OT" if ot_ok else "❌ OT",
-                        ]
-                    ),
-                }
-            )
+            reasoning_rows.append({
+                "Employee": emp_row.get("fullName", ""),
+                "ShiftID": shift["ShiftID"],
+                "Eligible": status,
+                "Reasoning": " | ".join([
+                    "✅ Role Match" if role_match else "❌ Role",
+                    "✅ Cert" if cert_ok else "❌ Cert",
+                    "✅ Avail" if avail_ok else "❌ Avail",
+                    "✅ OT" if ot_ok else "❌ OT",
+                ]),
+            })
     events.append({"icon": "📊", "title": "Summary Ready", "desc": "The AI has finished processing the shifts."})
     return events, shift_assignment_results, reasoning_rows
 
-# زر رئيسي لتشغيل الوكيل الذكي موضوع في منتصف الصفحة
-col_left, col_center, col_right = st.columns([2, 3, 2])
-with col_center:
-    start_clicked = st.button("▶️ Start AI Agent", key="start_agent", help="Let the AI handle the work")
-if start_clicked:
-    # تنفيذ الوكيل وعرض النتائج على شكل خط زمني
-    events, shift_assignment_results, reasoning_rows = run_agent(employee_ids_default, df_shifts_default)
-    # عرض الملخص والشرح بعد الانتهاء
-    # عرض الخط الزمني بعد اكتمال تشغيل الوكيل
-    st.subheader("🕒 Timeline of Actions")
-    st.markdown(render_timeline(events), unsafe_allow_html=True)
-
-    st.subheader("📊 Shift Fulfillment Summary")
-    if shift_assignment_results:
-        st.dataframe(pd.DataFrame(shift_assignment_results, columns=["Employee", "ShiftID", "Status"]))
-    else:
-        st.write("No assignments to display.")
-    st.subheader("📋 Reasoning Behind Selections")
-    if reasoning_rows:
-        st.dataframe(pd.DataFrame(reasoning_rows))
-    else:
-        st.write("No reasoning data available.")
+# -----------------------------------------------------------------------------
+# Streamlit UI logic
 
-# تذييل الصفحة
-st.markdown(
-    """
+def main() -> None:
+    """Entry point for the Streamlit application."""
+    # Configure page
+    st.set_page_config(page_title="Health Matrix AI Command Center", layout="wide")
+
+    # Set environment variables to avoid config warnings
+    os.environ.setdefault("XDG_CONFIG_HOME", "/tmp")
+    os.environ.setdefault("HF_HOME", "/tmp")
+    os.environ.setdefault("TRANSFORMERS_CACHE", "/tmp")
+
+    # Apply CSS styling
+    st.markdown(f"<style>{_EMBEDDED_CSS}</style>", unsafe_allow_html=True)
+
+    # Draw animated background circles
+    st.markdown(
+        """
+<div class="background">
+  <div class="circle"></div>
+  <div class="circle"></div>
+  <div class="circle"></div>
+  <div class="circle"></div>
+  <div class="circle"></div>
+  <div class="circle"></div>
+</div>
+""",
+        unsafe_allow_html=True,
+    )
+
+    # Display the logo if available
+    current_dir = pathlib.Path(__file__).resolve().parent if 'pathlib' in globals() else None
+    # Fallback: attempt to load logo from the working directory
+    logo_candidates = [
+        os.path.join(os.getcwd(), "logo.jpg"),
+        os.path.join(os.path.dirname(__file__) if '__file__' in globals() else '.', "logo.jpg"),
+    ]
+    logo_url = None
+    for candidate in logo_candidates:
+        if os.path.exists(candidate):
+            logo_url = candidate
+            break
+    if logo_url:
+        st.markdown(
+            f"""
+<div style="position:absolute; top:1rem; left:1rem; z-index:2;">
+  <img src="file://{logo_url}" width="140" alt="Health Matrix Logo"/>
+</div>
+""",
+            unsafe_allow_html=True,
+        )
+
+    # Intro header and subtitle
+    st.markdown(
+        """
+<div style="margin-top:5rem; text-align:center;">
+  <h1 style="font-size:3rem; font-weight:600; margin-bottom:0.5rem; color:#36ba01;">Welcome to the AI‑Powered Command Center</h1>
+  <h2 style="font-size:1.8rem; font-weight:500; margin-top:0; color:#004c97;">by Health Matrix</h2>
+  <p style="max-width:700px; margin:0 auto; font-size:1.1rem; line-height:1.5; color:#a9bcd4;">
+    This smart assistant leverages AI to automate decisions, schedule actions, and provide real‑time updates – all while you relax.
+  </p>
+</div>
+""",
+        unsafe_allow_html=True,
+    )
+
+    # Default data for demonstration
+    employee_ids_default = [850]
+    shift_data = """ShiftID,Department,RequiredSkill,RequiredCert,ShiftTime\nS101,ICU,ICU,ACLS,2025-06-04 07:00"""
+    df_shifts_default = pd.read_csv(StringIO(shift_data))
+
+    # Center the AI button
+    col_left, col_center, col_right = st.columns([2, 3, 2])
+    with col_center:
+        if st.button("▶️ Start AI Agent", key="start_agent", help="Let the AI handle the work"):
+            events, shift_assignment_results, reasoning_rows = run_agent(employee_ids_default, df_shifts_default)
+            # Display timeline after running the agent
+            st.subheader("🕒 Timeline of Actions")
+            st.markdown(render_timeline(events), unsafe_allow_html=True)
+            # Show summary table
+            st.subheader("📊 Shift Fulfillment Summary")
+            if shift_assignment_results:
+                st.dataframe(pd.DataFrame(shift_assignment_results, columns=["Employee", "ShiftID", "Status"]))
+            else:
+                st.write("No assignments to display.")
+            # Show reasoning table
+            st.subheader("📋 Reasoning Behind Selections")
+            if reasoning_rows:
+                st.dataframe(pd.DataFrame(reasoning_rows))
+            else:
+                st.write("No reasoning data available.")
+
+    # Footer
+    st.markdown(
+        """
 ---
 © 2025 Health Matrix Corp – Empowering Digital Health Transformation  
 Contact: [info@healthmatrixcorp.com](mailto:info@healthmatrixcorp.com)
-"""
-)
+""",
+        unsafe_allow_html=True,
+    )
+
+# Execute when run as a script
+if __name__ == "__main__":
+    main()