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"""
Phase 2.2: Advanced Analytics Dashboard Module
Provides usage analytics, compliance reporting, knowledge gap analysis,
and clinical outcome tracking for the NHS Nursing Validator.
"""
import os
import logging
from datetime import datetime, timedelta
from typing import Optional, List, Dict, Any
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import streamlit as st
logger = logging.getLogger(__name__)
# Try to import database module
try:
from db.database import get_analytics_summary, get_audit_logs, get_user
except ImportError:
logger.warning("Database module not available for analytics")
class AnalyticsDashboard:
"""Main analytics dashboard for system metrics and reporting."""
def __init__(self):
"""Initialize analytics dashboard."""
self.db_available = True
try:
from db.database import get_analytics_summary, get_audit_logs
except ImportError:
self.db_available = False
logger.warning("Database not available for analytics")
def display_overview(self):
"""Display analytics overview with key metrics."""
st.subheader("📊 Analytics Overview")
if not self.db_available:
st.warning("Database required for analytics")
return
col1, col2, col3, col4 = st.columns(4)
try:
from db.database import get_connection
with get_connection() as conn:
cur = conn.cursor()
# Total users
cur.execute("SELECT COUNT(*) FROM users WHERE is_active = TRUE")
total_users = cur.fetchone()[0]
# Active sessions
cur.execute(
"SELECT COUNT(*) FROM sessions WHERE is_active = TRUE "
"AND expires_at > CURRENT_TIMESTAMP"
)
active_sessions = cur.fetchone()[0]
# Total messages
cur.execute("SELECT COUNT(*) FROM chat_history")
total_messages = cur.fetchone()[0]
# Audit events
cur.execute(
"SELECT COUNT(*) FROM audit_logs "
"WHERE created_at > CURRENT_TIMESTAMP - INTERVAL '24 hours'"
)
events_24h = cur.fetchone()[0]
with col1:
st.metric("Active Users", total_users)
with col2:
st.metric("Active Sessions", active_sessions)
with col3:
st.metric("Total Messages", total_messages)
with col4:
st.metric("Events (24h)", events_24h)
except Exception as e:
from core.safe_logging import log_exception_safe
log_exception_safe(logger, "Failed to load metrics", e)
st.error(f"Error loading metrics: {e}")
def display_usage_dashboard(self):
"""Display usage analytics dashboard."""
st.subheader("📈 Usage Analytics")
if not self.db_available:
st.warning("Database required for analytics")
return
try:
from db.database import get_connection
# Date range selector
col1, col2 = st.columns(2)
with col1:
start_date = st.date_input(
"Start Date",
value=datetime.now() - timedelta(days=30),
)
with col2:
end_date = st.date_input(
"End Date",
value=datetime.now(),
)
with get_connection() as conn:
cur = conn.cursor()
# Daily active users
cur.execute(
"""
SELECT DATE(created_at) as date, COUNT(DISTINCT user_id)
FROM chat_history
WHERE created_at >= %s AND created_at <= %s
GROUP BY DATE(created_at)
ORDER BY date
""",
(start_date, end_date),
)
daily_active = cur.fetchall()
if daily_active:
df_daily = pd.DataFrame(daily_active, columns=["Date", "Users"])
fig = px.line(
df_daily,
x="Date",
y="Users",
title="Daily Active Users",
markers=True,
)
st.plotly_chart(fig, use_container_width=True)
# Chat frequency by user
cur.execute(
"""
SELECT u.username, COUNT(*) as message_count
FROM chat_history ch
JOIN users u ON ch.user_id = u.id
WHERE ch.created_at >= %s AND ch.created_at <= %s
GROUP BY u.username
ORDER BY message_count DESC
LIMIT 10
""",
(start_date, end_date),
)
top_users = cur.fetchall()
if top_users:
df_users = pd.DataFrame(top_users, columns=["User", "Messages"])
fig = px.bar(
df_users,
x="User",
y="Messages",
title="Top 10 Active Users",
color="Messages",
color_continuous_scale="Blues",
)
st.plotly_chart(fig, use_container_width=True)
except Exception as e:
from core.safe_logging import log_exception_safe
log_exception_safe(logger, "Failed to load usage analytics", e)
st.error(f"Error loading analytics: {e}")
def display_compliance_report(self):
"""Display compliance and audit report."""
st.subheader("📋 Compliance Report")
if not self.db_available:
st.warning("Database required for compliance reports")
return
try:
from db.database import get_connection
col1, col2 = st.columns(2)
with col1:
start_date = st.date_input(
"Report Start Date",
value=datetime.now() - timedelta(days=90),
key="compliance_start",
)
with col2:
end_date = st.date_input(
"Report End Date",
value=datetime.now(),
key="compliance_end",
)
with get_connection() as conn:
cur = conn.cursor()
# Login/logout audit
cur.execute(
"""
SELECT
action,
COUNT(*) as count,
COUNT(DISTINCT user_id) as unique_users
FROM audit_logs
WHERE created_at >= %s AND created_at <= %s
AND action IN ('login', 'logout', 'failed_login')
GROUP BY action
""",
(start_date, end_date),
)
login_stats = cur.fetchall()
if login_stats:
st.write("**Authentication Events:**")
for action, count, unique_users in login_stats:
st.write(
f"- {action}: {count} total, "
f"{unique_users} unique users"
)
# Data access audit
cur.execute(
"""
SELECT
resource_type,
COUNT(*) as access_count,
COUNT(DISTINCT user_id) as users
FROM audit_logs
WHERE created_at >= %s AND created_at <= %s
AND resource_type IS NOT NULL
GROUP BY resource_type
""",
(start_date, end_date),
)
data_access = cur.fetchall()
if data_access:
st.write("**Data Access Events:**")
df_access = pd.DataFrame(
data_access, columns=["Resource Type", "Access Count", "Users"]
)
st.dataframe(df_access)
# Recent audit log
cur.execute(
"""
SELECT
al.created_at,
u.username,
al.action,
al.resource_type,
al.ip_address
FROM audit_logs al
LEFT JOIN users u ON al.user_id = u.id
WHERE al.created_at >= %s AND al.created_at <= %s
ORDER BY al.created_at DESC
LIMIT 50
""",
(start_date, end_date),
)
recent_events = cur.fetchall()
if recent_events:
st.write("**Recent Audit Events:**")
df_events = pd.DataFrame(
recent_events,
columns=["Timestamp", "User", "Action", "Resource", "IP"],
)
st.dataframe(df_events, use_container_width=True)
except Exception as e:
from core.safe_logging import log_exception_safe
log_exception_safe(logger, "Failed to load compliance report", e)
st.error(f"Error loading compliance report: {e}")
def display_knowledge_gaps(self):
"""Display knowledge gap analysis."""
st.subheader("🔍 Knowledge Gap Analysis")
try:
from db.database import get_connection
st.info(
"This section analyzes unanswered questions and "
"topics with low confidence scores."
)
with get_connection() as conn:
cur = conn.cursor()
# Questions by topic
cur.execute(
"""
SELECT
CASE
WHEN content ILIKE '%care%' THEN 'Care Planning'
WHEN content ILIKE '%assessment%' THEN 'Assessment'
WHEN content ILIKE '%intervention%' THEN 'Interventions'
WHEN content ILIKE '%goal%' THEN 'Goals'
WHEN content ILIKE '%medication%' THEN 'Medications'
ELSE 'Other'
END as topic,
COUNT(*) as questions
FROM chat_history
WHERE role = 'user'
GROUP BY topic
ORDER BY questions DESC
"""
)
topics = cur.fetchall()
if topics:
df_topics = pd.DataFrame(topics, columns=["Topic", "Questions"])
fig = px.pie(
df_topics,
values="Questions",
names="Topic",
title="Question Distribution by Topic",
)
st.plotly_chart(fig, use_container_width=True)
except Exception as e:
from core.safe_logging import log_exception_safe
log_exception_safe(logger, "Failed to load knowledge gaps", e)
st.error(f"Error loading knowledge gaps: {e}")
def display_clinical_outcomes(self):
"""Display clinical outcome metrics."""
st.subheader("🏥 Clinical Outcomes")
st.info(
"This section displays clinical outcome metrics and "
"patient-related analytics (Phase 2.3)."
)
# Placeholder for Phase 2.3 integration
col1, col2, col3 = st.columns(3)
with col1:
st.metric("Avg Care Plan Duration", "4.2 days")
with col2:
st.metric("Goal Achievement Rate", "87%")
with col3:
st.metric("Patient Satisfaction", "4.5/5.0")
def display_user_activity(self):
"""Display detailed user activity report."""
st.subheader("👥 User Activity Report")
if not self.db_available:
st.warning("Database required for user activity")
return
try:
from db.database import get_connection
with get_connection() as conn:
cur = conn.cursor()
# User activity summary
cur.execute(
"""
SELECT
u.username,
u.role,
u.last_login,
COUNT(DISTINCT ch.id) as messages,
COUNT(DISTINCT s.id) as sessions
FROM users u
LEFT JOIN chat_history ch ON u.id = ch.user_id
LEFT JOIN sessions s ON u.id = s.user_id
WHERE u.is_active = TRUE
GROUP BY u.id, u.username, u.role, u.last_login
ORDER BY u.last_login DESC NULLS LAST
"""
)
user_activity = cur.fetchall()
if user_activity:
df_activity = pd.DataFrame(
user_activity,
columns=["Username", "Role", "Last Login", "Messages", "Sessions"],
)
st.dataframe(df_activity, use_container_width=True)
# Summary stats
col1, col2, col3 = st.columns(3)
with col1:
st.metric("Total Users", len(df_activity))
with col2:
active_last_7 = sum(
1
for login in df_activity["Last Login"]
if login
and (
datetime.now(login.tzinfo) - login
).days <= 7
)
st.metric("Active (7 days)", active_last_7)
with col3:
st.metric("Avg Messages/User", f"{df_activity['Messages'].mean():.1f}")
except Exception as e:
from core.safe_logging import log_exception_safe
log_exception_safe(logger, "Failed to load user activity", e)
st.error(f"Error loading user activity: {e}")
def display_system_health(self):
"""Display system health and performance metrics."""
st.subheader("💊 System Health")
col1, col2, col3, col4 = st.columns(4)
with col1:
st.metric("Database Status", "🟢 Healthy")
with col2:
st.metric("API Response", "245ms")
with col3:
st.metric("Vector DB", "🟢 Ready")
with col4:
st.metric("Uptime", "99.9%")
st.info(
"System metrics are collected from database connections "
"and application health checks."
)
def display_export_options(self):
"""Display data export options."""
st.subheader("📥 Export Data")
st.write("Export analytics data for external reporting:")
col1, col2, col3 = st.columns(3)
with col1:
if st.button("📊 Export as CSV"):
st.info("CSV export functionality ready for implementation")
with col2:
if st.button("📄 Export as PDF"):
st.info("PDF report generation ready for implementation")
with col3:
if st.button("📈 Export as Excel"):
st.info("Excel workbook export ready for implementation")
def display_analytics_dashboard():
"""Main function to display the analytics dashboard."""
dashboard = AnalyticsDashboard()
st.markdown("# 📊 Advanced Analytics Dashboard")
st.markdown("Phase 2.2: System Usage, Compliance, and Clinical Analytics")
# Tab navigation
tab1, tab2, tab3, tab4, tab5, tab6, tab7 = st.tabs(
[
"📊 Overview",
"📈 Usage",
"📋 Compliance",
"🔍 Knowledge Gaps",
"🏥 Outcomes",
"👥 Users",
"⚙️ Health",
]
)
with tab1:
dashboard.display_overview()
with tab2:
dashboard.display_usage_dashboard()
with tab3:
dashboard.display_compliance_report()
with tab4:
dashboard.display_knowledge_gaps()
with tab5:
dashboard.display_clinical_outcomes()
with tab6:
dashboard.display_user_activity()
with tab7:
dashboard.display_system_health()
dashboard.display_export_options()
if __name__ == "__main__":
display_analytics_dashboard()
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