marimo

```python
# marimo notebook for Sales Performance Analysis
# Generated by AI assistant

import marimo
__generated_with = "0.8.15"
app = marimo.App(width="medium")

@app.cell
def _():
    import pandas as pd
    import marimo as mo
    import altair as alt
    
    # Load sample sales data
    sales_data = pd.DataFrame({
        'date': pd.date_range(start='2024-01-01', periods=100),
        'product': ['A', 'B', 'C'] * 33 + ['A'],
        'region': ['North', 'South', 'East', 'West'] * 25,
        'revenue': [1000 + i*10 for i in range(100)],
        'units': [50 + i for i in range(100)]
    })
    return sales_data, mo.md("# Sales Performance Dashboard")

@app.cell
def _(sales_data):
    region_filter = mo.ui.multiselect(
        options=sales_data['region'].unique().tolist(),
        value=['North', 'South'],
        label="Select Regions"
    )
    return region_filter

@app.cell
def _(sales_data, region_filter):
    filtered_data = sales_data[sales_data['region'].isin(region_filter.value)]
    return filtered_data

@app.cell
def _(filtered_data):
    chart = alt.Chart(filtered_data).mark_line().encode(
        x='date:T',
        y='sum(revenue):Q',
        color='region:N'
    ).properties(width=600, height=300)
    return chart

@app.cell
def _(filtered_data):
    summary = filtered_data.groupby('product').agg({
        'revenue': 'sum',
        'units': 'sum'
    }).reset_index()
    return summary

if __name__ == "__main__":
    app.run()
```

### Interactive Features:
- **Region Selector**: Choose which regions to display in the dashboard
- **Real-time Updates**: All visualizations update automatically when filters change
- **Summary Statistics**: Product performance metrics automatically calculated

**Note**: This notebook can be shared as a standalone HTML file that maintains all interactive functionality.