# Colab 03: Excel Visualization

This OpenRath v2.0.0 example turns a fixed sales CSV into an editable Excel
workbook with formulas, KPIs, charts, previews, and automated quality checks.
It demonstrates how a router can block bad input before any artifact is built.

[Open in Google Colab](https://colab.research.google.com/github/Rath-Team/example/blob/main/examples/03_excel_visualization/notebook.ipynb)
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[View the workflow source](https://github.com/Rath-Team/example/blob/8db4d409e716458cea3040b905769a1222cb26e5/examples/03_excel_visualization/src/workflow.py)
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[View the reference workbook](https://github.com/Rath-Team/example/blob/8db4d409e716458cea3040b905769a1222cb26e5/examples/03_excel_visualization/output/sales-dashboard.xlsx)

## Input and Output

| Input | Output |
| --- | --- |
| `sales.csv` with 24 typed sales rows across six months and four regions. | `output/sales-dashboard.xlsx` |

The generated workbook contains four worksheets:

| Worksheet | Contents |
| --- | --- |
| `Data` | The typed source rows in an Excel table. |
| `Calculations` | Visible regional, monthly, gross-profit, and return-rate formulas. |
| `Dashboard` | Four KPIs plus regional and monthly charts. |
| `Notes` | Purpose, traceability, generation, and security notes. |

## Workflow

| Node | What happens |
| --- | --- |
| `profile_data` | Counts missing cells and duplicate business keys, then computes the source profile. |
| `quality_route` | Sends clean data to planning or routes invalid data to `stop_for_quality`. |
| `plan_dashboard` | Requests validated presentation text while preserving program-computed values. |
| `build_workbook` | Builds the four-sheet XLSX and four visual previews. |
| `qa_and_register` | Rejects missing previews or Excel formula errors before publishing the artifact. |

The LLM supplies only bounded presentation wording. Revenue, orders, return
rate, region totals, month totals, KPI formulas, and chart data all come from
the CSV and deterministic workbook builder.

## Quality Routing

```python
@router(successors=("plan_dashboard", "stop_for_quality"))
def quality_route(self, state):
    return (
        "plan_dashboard"
        if state["profile"]["quality"] == "pass"
        else "stop_for_quality"
    )
```

For this example, data quality passes only when rows exist and both missing
cells and duplicate business keys equal zero. A blocked dataset never reaches
the LLM planning or workbook-build steps.

## Run and Verify

1. Open the Notebook in Google Colab.
2. Choose **Runtime → Run all**.
3. Enter the DeepSeek API key in the masked prompt.
4. Review the dashboard preview and download the workbook.

The Notebook verifies:

- the durable run finishes as `SUCCEEDED`;
- the profile contains 24 rows and a `pass` decision;
- all four worksheets have visual previews;
- the formula audit finds no `#REF!`, `#DIV/0!`, `#VALUE!`, `#NAME?`, or `#N/A`;
- five checkpoints are stored;
- the workbook is registered under an `artifact://local/` URI.

## Adapt It

Replace `sales.csv` while preserving the published column contract, or update
the profiler and portable builder together for a new schema. Add new quality
rules before `quality_route` so invalid data remains outside artifact-building
steps.