podiumscan

Extract structured performance data from music program booklets using an LLM.

Reads competition schedules, concert programs, recital booklets, and event posters (PDF, DOC, DOCX, ODT, PNG, JPG, WEBP), finds performances by configured performers, and outputs structured JSON.

Setup

pip install podiumscan

Or for development:

git clone <repo-url>
cd podiumscan
make install-dev

On first run, the tool copies the example config to ~/.config/podiumscan/config.yaml automatically.

Edit ~/.config/podiumscan/config.yaml:

• Set model to a vision-capable LLM (default: anthropic/claude-opus-4-6)
• Set api_key to your provider's API key
• Add your performers under performers

Usage

podiumscan document.pdf
podiumscan concert-poster.jpg
podiumscan -c "Look at page 3" program.pdf
podiumscan -v document.docx

Options

Exit codes

Output

JSON array to stdout. Example:

[
  {
    "event_name": "Spring Chamber Music Festival",
    "performance_date": "2025-11-15",
    "performer": "Emily Parker",
    "instrument": "violin",
    "pieces": [
      { "composer": "J. S. Bach", "title": "Partita No. 2 in D minor, BWV 1004 / Sarabande" },
      { "composer": "Dvorak", "title": "Slavonic Dance No. 2 in E minor" }
    ],
    "teacher": "Sarah Mitchell",
    "accompanist": "James Crawford",
    "co_performers": [
      { "name": "Tom Wilson", "instrument": "piano" }
    ]
  }
]

Config format

See config.example.yaml for the full format. Key sections:

• model / api_key: LLM provider configuration
• performers: list of people to search for, with instruments (including aliases), teachers, and accompanists with date ranges

How it works

1. Detects file type and prepares the document
2. Tries sending it to the LLM in progressively degraded formats: raw document, then PDF, then page images
3. At each level, if the LLM returns invalid output, retries once with guidance
4. Fills in missing teacher/accompanist data from config based on performance date
5. Outputs JSON to stdout

Model updater

podiumscan-update-models

Queries an LLM for currently available PDF/vision-capable models, cross-references with LiteLLM's registry, and updates the commented model list in your config file.

Supported file types

PDF, DOC, DOCX, ODT, PNG, JPG, JPEG, WEBP

DOC/DOCX/ODT conversion requires libreoffice installed on the system.

Dependencies

• Python 3
• LiteLLM
• PyYAML
• PyMuPDF
• libreoffice (only for DOC/DOCX/ODT)