README.md

metadata

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

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.