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"""
Dashboard Overview Layout.
This module provides the overview layout for the analytics dashboard,
showing high-level system metrics and status.
"""
from typing import Dict, Any
from dash import html, dcc
import plotly.graph_objs as go
import plotly.express as px
def create_overview_layout(dashboard_data: Dict[str, Any]) -> html.Div:
"""
Create overview dashboard layout.
Args:
dashboard_data: Real-time dashboard data
Returns:
Overview layout component
"""
overview = dashboard_data.get("overview", {})
performance = dashboard_data.get("performance", {})
quality = dashboard_data.get("quality", {})
time_series = dashboard_data.get("time_series", {})
# Key metrics cards
metrics_cards = [
_create_metric_card(
"Total Queries",
overview.get("total_queries", 0),
"📊",
"total-queries-card"
),
_create_metric_card(
"Queries/Min",
f"{overview.get('queries_per_minute', 0):.1f}",
"⚡",
"qpm-card"
),
_create_metric_card(
"Avg Latency",
f"{overview.get('avg_latency_ms', 0):.1f}ms",
"⏱️",
"latency-card"
),
_create_metric_card(
"Success Rate",
f"{overview.get('success_rate', 0):.1f}%",
"✅",
"success-card"
)
]
# System status
status_indicators = _create_status_indicators(performance, quality)
# Time series charts
charts = _create_overview_charts(time_series, performance)
return html.Div([
# Key metrics row
html.Div([
html.H2("System Overview", className="section-title"),
html.Div(metrics_cards, className="metrics-cards")
], className="metrics-section"),
# Status indicators
html.Div([
html.H3("System Status", className="subsection-title"),
status_indicators
], className="status-section"),
# Charts
html.Div([
html.H3("Performance Trends", className="subsection-title"),
charts
], className="charts-section")
], className="overview-layout")
def _create_metric_card(title: str, value: str, icon: str, card_id: str) -> html.Div:
"""Create a metric card component."""
return html.Div([
html.Div([
html.Span(icon, className="metric-icon"),
html.Div([
html.H3(str(value), className="metric-value"),
html.P(title, className="metric-title")
], className="metric-text")
], className="metric-content")
], className="metric-card", id=card_id)
def _create_status_indicators(performance: Dict[str, Any], quality: Dict[str, Any]) -> html.Div:
"""Create system status indicators."""
# Determine status colors based on metrics
latency_p95 = performance.get("latency_percentiles", {}).get("p95", 0)
avg_confidence = quality.get("avg_confidence_score", 0)
# Status determination
latency_status = "healthy" if latency_p95 < 500 else "warning" if latency_p95 < 1000 else "critical"
quality_status = "healthy" if avg_confidence > 0.8 else "warning" if avg_confidence > 0.6 else "critical"
status_colors = {
"healthy": "#4CAF50",
"warning": "#FF9800",
"critical": "#F44336"
}
indicators = [
html.Div([
html.Span("●", style={"color": status_colors[latency_status], "fontSize": "20px"}),
html.Span(f"Latency P95: {latency_p95:.1f}ms", className="status-text")
], className="status-indicator"),
html.Div([
html.Span("●", style={"color": status_colors[quality_status], "fontSize": "20px"}),
html.Span(f"Quality Score: {avg_confidence:.2f}", className="status-text")
], className="status-indicator"),
html.Div([
html.Span("●", style={"color": "#4CAF50", "fontSize": "20px"}),
html.Span("Neural Reranking: Active", className="status-text")
], className="status-indicator"),
html.Div([
html.Span("●", style={"color": "#4CAF50", "fontSize": "20px"}),
html.Span("Multi-Backend: Operational", className="status-text")
], className="status-indicator")
]
return html.Div(indicators, className="status-indicators")
def _create_overview_charts(time_series: Dict[str, Any], performance: Dict[str, Any]) -> html.Div:
"""Create overview charts."""
# Queries per second over time
qps_chart = _create_qps_chart(time_series)
# Latency distribution
latency_chart = _create_latency_distribution_chart(performance)
# Component latency breakdown
component_chart = _create_component_latency_chart(performance)
return html.Div([
# Top row - QPS and Latency Distribution
html.Div([
html.Div([
dcc.Graph(figure=qps_chart, config={'displayModeBar': False})
], className="chart-container"),
html.Div([
dcc.Graph(figure=latency_chart, config={'displayModeBar': False})
], className="chart-container")
], className="charts-row"),
# Bottom row - Component breakdown
html.Div([
html.Div([
dcc.Graph(figure=component_chart, config={'displayModeBar': False})
], className="chart-container-full")
], className="charts-row")
])
def _create_qps_chart(time_series: Dict[str, Any]) -> go.Figure:
"""Create queries per second chart."""
timestamps = time_series.get("timestamps", [])
qps_values = time_series.get("qps", [])
fig = go.Figure()
if timestamps and qps_values:
fig.add_trace(go.Scatter(
x=timestamps,
y=qps_values,
mode='lines+markers',
name='QPS',
line=dict(color='#2E86AB', width=3),
marker=dict(size=6)
))
fig.update_layout(
title="Queries Per Second",
xaxis_title="Time",
yaxis_title="QPS",
height=300,
margin=dict(l=50, r=50, t=50, b=50),
plot_bgcolor='rgba(0,0,0,0)',
paper_bgcolor='rgba(0,0,0,0)'
)
return fig
def _create_latency_distribution_chart(performance: Dict[str, Any]) -> go.Figure:
"""Create latency distribution chart."""
latency_percentiles = performance.get("latency_percentiles", {})
percentiles = ['P50', 'P95', 'P99']
values = [
latency_percentiles.get("p50", 0),
latency_percentiles.get("p95", 0),
latency_percentiles.get("p99", 0)
]
colors = ['#4CAF50', '#FF9800', '#F44336']
fig = go.Figure(data=[
go.Bar(
x=percentiles,
y=values,
marker=dict(color=colors),
text=[f'{v:.1f}ms' for v in values],
textposition='auto'
)
])
fig.update_layout(
title="Latency Percentiles",
xaxis_title="Percentile",
yaxis_title="Latency (ms)",
height=300,
margin=dict(l=50, r=50, t=50, b=50),
plot_bgcolor='rgba(0,0,0,0)',
paper_bgcolor='rgba(0,0,0,0)'
)
return fig
def _create_component_latency_chart(performance: Dict[str, Any]) -> go.Figure:
"""Create component latency breakdown chart."""
component_latencies = performance.get("component_latencies", {})
components = list(component_latencies.keys())
latencies = list(component_latencies.values())
# Color mapping for components
color_map = {
"dense_retrieval": "#2E86AB",
"sparse_retrieval": "#A23B72",
"graph_retrieval": "#F18F01",
"neural_reranking": "#C73E1D"
}
colors = [color_map.get(comp, "#666666") for comp in components]
fig = go.Figure(data=[
go.Bar(
x=components,
y=latencies,
marker=dict(color=colors),
text=[f'{lat:.1f}ms' for lat in latencies],
textposition='auto'
)
])
fig.update_layout(
title="Component Latency Breakdown",
xaxis_title="Component",
yaxis_title="Average Latency (ms)",
height=350,
margin=dict(l=50, r=50, t=50, b=50),
plot_bgcolor='rgba(0,0,0,0)',
paper_bgcolor='rgba(0,0,0,0)'
)
return fig |