---
title: Segmentation Eval Dashboard
emoji: 📊
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860
pinned: false
---

# Segmentation Evaluation Metrics Dashboard

Streamlit dashboard for tracking segmentation evaluation metrics stored in Hugging Face **dataset cards** under a Markdown section titled exactly:

`### Performance Metrics`

The app discovers dataset repos (typically with `eval` in the repo id), parses the metrics table, and builds a historical view from commit revisions when possible.

## Features

- Dataset discovery from Hugging Face Hub (`huggingface_hub` API)
- Allowlist/prefix-based repo filtering via environment variables
- Robust Markdown section/table parsing for `Performance Metrics`
- Historical trend extraction using dataset commit history
- Filters by repo, class, and metric
- Trend chart with optional standard-deviation bands
- Latest snapshot table
- Repo-to-repo comparison chart on latest runs
- Optional per-class small multiples
- CSV export for filtered data
- Health/status panel (parsed vs failed repos)
- Auto-refresh and cached Hub calls

## Project Structure

- `app.py`: Streamlit entrypoint and UI
- `config.py`: env-based configuration
- `hf_client.py`: Hugging Face Hub API access and README retrieval
- `parsing.py`: Markdown section/table extraction
- `transforms.py`: data normalization, history shaping, latest snapshots
- `charts.py`: Altair chart builders
- `requirements.txt`: Python dependencies

## Required Environment Variables

- `HF_TOKEN` (optional, recommended for private repos or higher rate limits)
- `HF_OWNER` (optional namespace/organization filter, e.g. `my-org`)

## Optional Environment Variables

- `EVAL_REPO_ALLOWLIST` (comma-separated dataset ids)
- `EVAL_REPO_PREFIXES` (comma-separated prefixes, e.g. `my-org/`)
- `EVAL_REPO_ID_CONTAINS` (default: `eval`)
- `EVAL_DISCOVERY_ENABLED` (`true`/`false`, default: `true`)
- `EVAL_MAX_REPOS` (default: `200`)
- `EVAL_INCLUDE_HISTORY` (`true`/`false`, default: `true`)
- `EVAL_MAX_COMMITS_PER_REPO` (default: `20`)
- `EVAL_METRICS_SECTION_HEADING` (default: `Performance Metrics`)
- `EVAL_CACHE_TTL_SECONDS` (default: `600`)
- `EVAL_AUTO_REFRESH_SECONDS` (default: `300`)
- `EVAL_MAX_LOAD_SECONDS` (default: `90`)

## Local Run

```bash
pip install -r requirements.txt
streamlit run app.py
```

## Deploy to Hugging Face Space

1. Create a new **Space** with SDK = **Streamlit**.
2. Copy all files in this folder to the Space repository root.
3. In Space settings, set secrets/variables as needed:
   - `HF_TOKEN` (as a secret)
   - any optional `EVAL_*` vars for discovery behavior
4. Push to the Space.
5. The app will start from `app.py`.

## Notes / Limitations

- History quality depends on README/table availability across commits.
- If commit history is unavailable or inaccessible, the app still supports latest snapshot parsing.
- Table parsing is tolerant to minor Markdown inconsistencies, but severely malformed tables are skipped.
- Missing numeric values (`nan`, `NA`, empty) are treated as `NaN` and handled safely in charts/tables.