# Dashboard Guide

This project includes a Streamlit dashboard for presenting the current
framework, experiment artifacts, and comparison outputs in a more visual and
demo-friendly way.

## Purpose

The dashboard is a presentation layer for the saved experiment artifacts.

It is designed to help the team:

- inspect individual runs
- review baseline-vs-defended comparisons
- present category, surface, and source breakdowns
- inspect case-level outcomes
- show a polished proof-of-concept during demos and reporting

## Entry Point

- `dashboard/streamlit_app.py`

## What The Dashboard Shows

### Hero and project snapshot

- active corpus size and sources
- selected run and selected comparison
- a spotlight on the latest real comparison artifact

### Run Overview

- key metrics from `summary.json`
- mitigation pressure and manual-review counts
- attack-category, attack-surface, and source breakdowns

### Comparison Lab

- saved comparison artifacts from `results/comparisons/*.json`
- overall metric deltas
- category, surface, and source deltas
- optional report-ready Markdown summaries when available

### Case Explorer

- case-level results from `case_results.jsonl`
- request prompt
- execution context
- response text
- mitigation action and evaluation outcome

### Manifest View

- model snapshot
- mitigation snapshot
- dataset snapshot
- run provenance

## Installation

Install the dashboard extras:

```bash
pip install -e .[dashboard]
```

## Run

From the repository root:

```bash
streamlit run dashboard/streamlit_app.py
```

## Design Notes

The dashboard remains intentionally lightweight:

- no extra backend service
- no database
- no API layer
- reads repository artifacts directly

That keeps it aligned with capstone scope while still giving the project a
strong visual interface.

## Recommended Demo Flow

1. Open the dashboard and show the hero/project snapshot.
2. Select a real defended run in Run Overview.
3. Show the category and source breakdowns.
4. Open Comparison Lab and inspect a saved real baseline-vs-defended comparison.
5. Use Case Explorer to show one concrete example.
6. Use Manifest to explain reproducibility and experiment setup.