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from dash import Dash, html, dcc
import plotly.express as px
import pandas as pd

**Run the app:**
```bash
python app.py

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from dash import Dash, html, dcc, callback, Output, Input
import plotly.express as px
import pandas as pd

app = Dash(__name__)

html.Label("Select Category:"),
dcc.Dropdown(
    id="category-dropdown",
    options=[{"label": c, "value": c} for c in df["category"].unique()],
    value="A",
    clearable=False
),

dcc.Graph(id="line-chart")

fig = px.line(
    filtered_df,
    x="date",
    y="value",
    title=f"Values for Category {selected_category}"
)

return fig

**Multiple Inputs and Outputs:**
```python
from dash import Dash, html, dcc, callback, Output, Input
import plotly.express as px
import pandas as pd

app = Dash(__name__)

html.Div([
    html.Div([
        html.Label("Category"),
        dcc.Dropdown(
            id="category-filter",
            options=[{"label": c, "value": c} for c in df["category"].unique()],
            value=["A", "B", "C"],
            multi=True
        )
    ], style={"width": "45%", "display": "inline-block"}),

    html.Div([
        html.Label("Region"),
        dcc.Dropdown(
            id="region-filter",
            options=[{"label": r, "value": r} for r in df["region"].unique()],
            value=["North", "South", "East", "West"],
            multi=True
        )
    ], style={"width": "45%", "display": "inline-block", "marginLeft": "5%"})
]),

html.Div([
    html.Div([
        dcc.Graph(id="trend-chart")
    ], style={"width": "60%", "display": "inline-block"}),

    html.Div([
        dcc.Graph(id="pie-chart")
    ], style={"width": "38%", "display": "inline-block", "marginLeft": "2%"})
]),

html.Div(id="summary-stats")

# Trend chart
trend = filtered.groupby("date")["value"].sum().reset_index()
trend_fig = px.line(trend, x="date", y="value", title="Value Trend")

# Pie chart
by_category = filtered.groupby("category")["value"].sum().reset_index()
pie_fig = px.pie(by_category, values="value", names="category", title="By Category")

# Summary stats
stats = html.Div([
    html.H4("Summary Statistics"),
    html.P(f"Total records: {len(filtered):,}"),
    html.P(f"Total value: {filtered['value'].sum():,}"),
    html.P(f"Average value: {filtered['value'].mean():.2f}")
])

return trend_fig, pie_fig, stats

**Chained Callbacks:**
```python
from dash import Dash, html, dcc, callback, Output, Input
import pandas as pd

app = Dash(__name__)

html.Label("Country"),
dcc.Dropdown(id="country-dropdown"),

html.Label("State/Province"),
dcc.Dropdown(id="state-dropdown"),

html.Label("City"),
dcc.Dropdown(id="city-dropdown"),

html.Div(id="selection-output")

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from dash import html

# Lists
html.Ul([
    html.Li("Item 1"),
    html.Li("Item 2"),
    html.Li("Item 3")
]),

# Links
html.A("Click here", href="https://example.com", target="_blank"),

# Images
html.Img(src="/assets/logo.png", style={"width": "200px"}),

# Tables
html.Table([
    html.Thead([
        html.Tr([html.Th("Name"), html.Th("Value")])
    ]),
    html.Tbody([
        html.Tr([html.Td("Item 1"), html.Td("100")]),
        html.Tr([html.Td("Item 2"), html.Td("200")])
    ])
])

**Core Components (dcc):**
```python
from dash import dcc

# Multi-select dropdown
dcc.Dropdown(
    id="multi-dropdown",
    options=[{"label": f"Option {i}", "value": i} for i in range(10)],
    value=[1, 2, 3],
    multi=True
),

# Slider
dcc.Slider(
    id="slider",
    min=0,
    max=100,
    step=5,
    value=50,
    marks={0: "0", 25: "25", 50: "50", 75: "75", 100: "100"}
),

# Range slider
dcc.RangeSlider(
    id="range-slider",
    min=0,
    max=100,
    step=1,
    value=[20, 80],
    marks={i: str(i) for i in range(0, 101, 20)}
),

# Input
dcc.Input(
    id="text-input",
    type="text",
    placeholder="Enter text...",
    debounce=True  # Wait for typing to stop
),

# Textarea
dcc.Textarea(
    id="textarea",
    placeholder="Enter longer text...",
    style={"width": "100%", "height": "100px"}
),

# Checklist
dcc.Checklist(
    id="checklist",
    options=[
        {"label": "Option 1", "value": "1"},
        {"label": "Option 2", "value": "2"},
        {"label": "Option 3", "value": "3"}
    ],
    value=["1"],
    inline=True
),

# Radio items
dcc.RadioItems(
    id="radio",
    options=[
        {"label": "Small", "value": "s"},
        {"label": "Medium", "value": "m"},
        {"label": "Large", "value": "l"}
    ],
    value="m",
    inline=True
),

# Date picker
dcc.DatePickerSingle(
    id="date-picker",
    date="2025-01-01",
    display_format="YYYY-MM-DD"
),

# Date range picker
dcc.DatePickerRange(
    id="date-range",
    start_date="2025-01-01",
    end_date="2025-12-31",
    display_format="YYYY-MM-DD"
),

# Upload
dcc.Upload(
    id="upload",
    children=html.Div(["Drag and Drop or ", html.A("Select Files")]),
    style={
        "width": "100%",
        "height": "60px",
        "lineHeight": "60px",
        "borderWidth": "1px",
        "borderStyle": "dashed",
        "borderRadius": "5px",
        "textAlign": "center"
    }
),

# Tabs
dcc.Tabs(id="tabs", value="tab-1", children=[
    dcc.Tab(label="Tab 1", value="tab-1"),
    dcc.Tab(label="Tab 2", value="tab-2")
]),

# Loading indicator
dcc.Loading(
    id="loading",
    type="default",  # default, graph, cube, circle, dot
    children=html.Div(id="loading-output")
),

# Interval (for periodic updates)
dcc.Interval(
    id="interval-component",
    interval=1000,  # milliseconds
    n_intervals=0
),

# Store (client-side data storage)
dcc.Store(id="data-store", storage_type="session"),  # memory, session, local

# Graph
dcc.Graph(
    id="graph",
    config={
        "displayModeBar": True,
        "displaylogo": False,
        "modeBarButtonsToRemove": ["lasso2d", "select2d"]
    }
)

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from dash import Dash, html, dcc, callback, Output, Input
import dash_bootstrap_components as dbc
import plotly.express as px
import pandas as pd

# Metrics row
dbc.Row([
    dbc.Col([
        dbc.Card([
            dbc.CardBody([
                html.H4("Total Sales", className="card-title"),
                html.H2(f"${df['sales'].sum():,}", className="text-success")
            ])
        ])
    ], md=4),
    dbc.Col([
        dbc.Card([
            dbc.CardBody([
                html.H4("Total Orders", className="card-title"),
                html.H2(f"{df['orders'].sum():,}", className="text-info")
            ])
        ])
    ], md=4),
    dbc.Col([
        dbc.Card([
            dbc.CardBody([
                html.H4("Avg Order Value", className="card-title"),
                html.H2(f"${df['sales'].sum() / df['orders'].sum():.2f}", className="text-warning")
            ])
        ])
    ], md=4)
], className="mb-4"),

# Filters
dbc.Row([
    dbc.Col([
        dbc.Card([
            dbc.CardHeader("Filters"),
            dbc.CardBody([
                dbc.Label("Date Range"),
                dcc.DatePickerRange(
                    id="date-range",
                    start_date=df["date"].min(),
                    end_date=df["date"].max(),
                    className="mb-3"
                ),
                dbc.Label("Metric"),
                dcc.Dropdown(
                    id="metric-dropdown",
                    options=[
                        {"label": "Sales", "value": "sales"},
                        {"label": "Orders", "value": "orders"}
                    ],
                    value="sales"
                )
            ])
        ])
    ], md=3),
    dbc.Col([
        dcc.Graph(id="main-chart")
    ], md=9)
]),

# Tabs
dbc.Row([
    dbc.Col([
        dbc.Tabs([
            dbc.Tab(label="Daily Data", tab_id="daily"),
            dbc.Tab(label="Summary", tab_id="summary")
        ], id="tabs", active_tab="daily"),
        html.Div(id="tab-content", className="mt-3")
    ])
], className="mt-4")

fig = px.line(
    filtered,
    x="date",
    y=metric,
    title=f"{metric.title()} Over Time"
)
fig.update_layout(template="plotly_white")

return fig

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my_dash_app/
├── app.py              # Main entry point
├── pages/
│   ├── __init__.py
│   ├── home.py
│   ├── analytics.py
│   └── settings.py
├── components/
│   ├── __init__.py
│   ├── navbar.py
│   └── footer.py
├── utils/
│   ├── __init__.py
│   └── data.py
└── assets/
    ├── style.css
    └── logo.png

from dash import Dash, html, dcc, page_container
import dash_bootstrap_components as dbc

app = Dash(
    __name__,
    use_pages=True,
    external_stylesheets=[dbc.themes.BOOTSTRAP]
)

**Home Page (pages/home.py):**
```python
from dash import html, register_page
import dash_bootstrap_components as dbc

register_page(__name__, path="/", name="Home")

layout = dbc.Container([
    dbc.Row([
        dbc.Col([
            html.H1("Welcome to the Dashboard"),
            html.P("Select a page from the navigation bar to get started."),
            dbc.Card([
                dbc.CardBody([
                    html.H4("Quick Links"),
                    dbc.ListGroup([
                        dbc.ListGroupItem("Analytics", href="/analytics"),
                        dbc.ListGroupItem("Settings", href="/settings")
                    ])
                ])
            ])
        ])
    ])
])

from dash import html, dcc, callback, Output, Input, register_page
import dash_bootstrap_components as dbc
import plotly.express as px
import pandas as pd

register_page(__name__, path="/analytics", name="Analytics")

dbc.Row([
    dbc.Col([
        dbc.Label("Chart Type"),
        dcc.Dropdown(
            id="chart-type",
            options=[
                {"label": "Line", "value": "line"},
                {"label": "Bar", "value": "bar"},
                {"label": "Area", "value": "area"}
            ],
            value="line"
        )
    ], md=4)
], className="mb-4"),

dcc.Graph(id="analytics-chart")

return fig

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from dash import Dash, html, dcc
import dash_auth

app = Dash(__name__)

**Custom Login (with session):**
```python
from dash import Dash, html, dcc, callback, Output, Input, State
import dash_bootstrap_components as dbc
from flask import session

app = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])
app.server.secret_key = "your-secret-key-here"

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from dash import Dash, html, dcc, callback, Output, Input
import dash_bootstrap_components as dbc
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import pandas as pd
import numpy as np
from datetime import datetime, timedelta

# Date filter
dbc.Row([
    dbc.Col([
        dbc.Card([
            dbc.CardBody([
                dbc.Row([
                    dbc.Col([
                        dbc.Label("Date Range"),
                        dcc.DatePickerRange(
                            id="date-filter",
                            start_date="2025-01-01",
                            end_date="2025-12-31",
                            display_format="YYYY-MM-DD"
                        )
                    ], md=4),
                    dbc.Col([
                        dbc.Label("Region"),
                        dcc.Dropdown(
                            id="region-filter",
                            options=[{"label": r, "value": r} for r in df["region"].unique()],
                            value=df["region"].unique().tolist(),
                            multi=True
                        )
                    ], md=4),
                    dbc.Col([
                        dbc.Label("Category"),
                        dcc.Dropdown(
                            id="category-filter",
                            options=[{"label": c, "value": c} for c in df["category"].unique()],
                            value=df["category"].unique().tolist(),
                            multi=True
                        )
                    ], md=4)
                ])
            ])
        ])
    ])
], className="mb-4"),

# KPI Cards
dbc.Row([
    dbc.Col([
        dbc.Card([
            dbc.CardBody([
                html.H6("Total Revenue", className="text-muted"),
                html.H3(id="kpi-revenue", className="text-success"),
                html.Small(id="kpi-revenue-change", className="text-muted")
            ])
        ], color="light")
    ], md=3),
    dbc.Col([
        dbc.Card([
            dbc.CardBody([
                html.H6("Total Orders", className="text-muted"),
                html.H3(id="kpi-orders", className="text-info"),
                html.Small(id="kpi-orders-change", className="text-muted")
            ])
        ], color="light")
    ], md=3),
    dbc.Col([
        dbc.Card([
            dbc.CardBody([
                html.H6("Unique Customers", className="text-muted"),
                html.H3(id="kpi-customers", className="text-warning"),
                html.Small(id="kpi-customers-change", className="text-muted")
            ])
        ], color="light")
    ], md=3),
    dbc.Col([
        dbc.Card([
            dbc.CardBody([
                html.H6("Avg Order Value", className="text-muted"),
                html.H3(id="kpi-aov", className="text-primary"),
                html.Small(id="kpi-aov-change", className="text-muted")
            ])
        ], color="light")
    ], md=3)
], className="mb-4"),

# Charts Row 1
dbc.Row([
    dbc.Col([
        dbc.Card([
            dbc.CardHeader("Revenue Trend"),
            dbc.CardBody([
                dcc.Graph(id="revenue-trend")
            ])
        ])
    ], md=8),
    dbc.Col([
        dbc.Card([
            dbc.CardHeader("Revenue by Category"),
            dbc.CardBody([
                dcc.Graph(id="category-pie")
            ])
        ])
    ], md=4)
], className="mb-4"),

# Charts Row 2
dbc.Row([
    dbc.Col([
        dbc.Card([
            dbc.CardHeader("Regional Performance"),
            dbc.CardBody([
                dcc.Graph(id="regional-bar")
            ])
        ])
    ], md=6),
    dbc.Col([
        dbc.Card([
            dbc.CardHeader("Orders vs Customers"),
            dbc.CardBody([
                dcc.Graph(id="scatter-chart")
            ])
        ])
    ], md=6)
], className="mb-4"),

# Data Table
dbc.Row([
    dbc.Col([
        dbc.Card([
            dbc.CardHeader("Detailed Data"),
            dbc.CardBody([
                html.Div(id="data-table")
            ])
        ])
    ])
])

# KPIs
revenue = f"${filtered['revenue'].sum():,.0f}"
orders = f"{filtered['orders'].sum():,}"
customers = f"{filtered['customers'].sum():,}"
aov = f"${filtered['revenue'].sum() / filtered['orders'].sum():.2f}" if filtered['orders'].sum() > 0 else "$0"

# Revenue trend
daily_revenue = filtered.groupby("date")["revenue"].sum().reset_index()
trend_fig = px.line(
    daily_revenue,
    x="date",
    y="revenue",
    title=None
)
trend_fig.update_layout(
    margin=dict(l=0, r=0, t=0, b=0),
    hovermode="x unified"
)

# Category pie
by_category = filtered.groupby("category")["revenue"].sum().reset_index()
pie_fig = px.pie(
    by_category,
    values="revenue",
    names="category",
    title=None
)
pie_fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))

# Regional bar
by_region = filtered.groupby("region").agg({
    "revenue": "sum",
    "orders": "sum"
}).reset_index()
bar_fig = px.bar(
    by_region,
    x="region",
    y="revenue",
    color="region",
    title=None
)
bar_fig.update_layout(
    margin=dict(l=0, r=0, t=0, b=0),
    showlegend=False
)

# Scatter
scatter_fig = px.scatter(
    filtered.groupby("date").agg({"orders": "sum", "customers": "sum"}).reset_index(),
    x="orders",
    y="customers",
    title=None,
    trendline="ols"
)
scatter_fig.update_layout(margin=dict(l=0, r=0, t=0, b=0))

# Table
table = dbc.Table.from_dataframe(
    filtered.groupby(["region", "category"]).agg({
        "revenue": "sum",
        "orders": "sum",
        "customers": "sum"
    }).reset_index().round(2),
    striped=True,
    bordered=True,
    hover=True,
    responsive=True
)

return revenue, orders, customers, aov, trend_fig, pie_fig, bar_fig, scatter_fig, table

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from dash import Dash, html, dcc, callback, Output, Input
import dash_bootstrap_components as dbc
import plotly.graph_objects as go
from collections import deque
import random
from datetime import datetime

app = Dash(__name__, external_stylesheets=[dbc.themes.CYBORG])

# Status indicators
dbc.Row([
    dbc.Col([
        dbc.Card([
            dbc.CardBody([
                html.H6("CPU Usage"),
                html.H2(id="cpu-value", className="text-info"),
                dbc.Progress(id="cpu-progress", value=0, max=100)
            ])
        ])
    ], md=4),
    dbc.Col([
        dbc.Card([
            dbc.CardBody([
                html.H6("Memory Usage"),
                html.H2(id="memory-value", className="text-warning"),
                dbc.Progress(id="memory-progress", value=0, max=100)
            ])
        ])
    ], md=4),
    dbc.Col([
        dbc.Card([
            dbc.CardBody([
                html.H6("Network I/O"),
                html.H2(id="network-value", className="text-success"),
                dbc.Progress(id="network-progress", value=0, max=100)
            ])
        ])
    ], md=4)
], className="mb-4"),

# Charts
dbc.Row([
    dbc.Col([
        dbc.Card([
            dbc.CardHeader("System Metrics (Last 50 Updates)"),
            dbc.CardBody([
                dcc.Graph(id="live-graph", animate=True)
            ])
        ])
    ])
]),

# Interval component for updates
dcc.Interval(
    id="interval-component",
    interval=1000,  # 1 second
    n_intervals=0
)

# Update data stores
time_data.append(datetime.now())
cpu_data.append(cpu)
memory_data.append(memory)
network_data.append(network)

# Create figure
fig = go.Figure()

fig.add_trace(go.Scatter(
    x=list(time_data),
    y=list(cpu_data),
    name="CPU",
    mode="lines",
    line=dict(color="#17a2b8")
))

fig.add_trace(go.Scatter(
    x=list(time_data),
    y=list(memory_data),
    name="Memory",
    mode="lines",
    line=dict(color="#ffc107")
))

fig.add_trace(go.Scatter(
    x=list(time_data),
    y=list(network_data),
    name="Network",
    mode="lines",
    line=dict(color="#28a745")
))

fig.update_layout(
    template="plotly_dark",
    paper_bgcolor="rgba(0,0,0,0)",
    plot_bgcolor="rgba(0,0,0,0)",
    yaxis=dict(range=[0, 100], title="Usage %"),
    xaxis=dict(title="Time"),
    legend=dict(orientation="h", yanchor="bottom", y=1.02),
    margin=dict(l=50, r=20, t=30, b=50),
    uirevision="constant"  # Preserve zoom/pan on update
)

return (
    f"{cpu:.1f}%",
    f"{memory:.1f}%",
    f"{network:.1f}%",
    cpu,
    memory,
    network,
    fig
)

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from dash import Dash, html, callback, Output, Input
import dash_ag_grid as dag
import dash_bootstrap_components as dbc
import pandas as pd
import numpy as np

app = Dash(__name__, external_stylesheets=[dbc.themes.BOOTSTRAP])

dbc.Row([
    dbc.Col([
        dbc.Input(
            id="search-input",
            placeholder="Quick search...",
            className="mb-3"
        )
    ], md=4),
    dbc.Col([
        dbc.Button("Export CSV", id="export-btn", color="primary")
    ], md=2)
]),

dag.AgGrid(
    id="inventory-grid",
    columnDefs=column_defs,
    rowData=df.to_dict("records"),
    defaultColDef={
        "sortable": True,
        "filter": True,
        "resizable": True,
        "floatingFilter": True
    },
    dashGridOptions={
        "pagination": True,
        "paginationPageSize": 20,
        "rowSelection": "multiple",
        "animateRows": True
    },
    style={"height": "600px"}
),

html.Div(id="selection-output", className="mt-3")

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```yaml

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from dash import callback, Output, Input
from dash.exceptions import PreventUpdate

@callback(
    Output("output", "children"),
    Input("input", "value")
)
def safe_callback(value):
    if value is None:
        raise PreventUpdate

    try:
        result = process(value)
        return result
    except Exception as e:
        return html.Div(f"Error: {str(e)}", className="text-danger")

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**Issue: Slow initial load**
```python

**Issue: Memory leaks**
```python

**Issue: Multiple callback outputs**
```python

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