68 lines
3.0 KiB
Markdown
68 lines
3.0 KiB
Markdown
# PHOTON — local photo intelligence console
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A local web app that walks through a photo folder, has an Ollama vision model
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describe + categorize each photo, and embeds the result as standard metadata
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inside the photo file so everything becomes searchable (Spotlight, Photos,
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Lightroom, etc.). Nothing is ever deleted, moved, or renamed.
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## Run it
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```bash
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cd "/Users/drjones/photo ollama app organizer"
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python3 server.py
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# then open http://localhost:8765
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```
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Requires: Ollama running with a vision model, `exiftool` (installed via brew),
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macOS (`sips` is used for fast downscaling).
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## How it works
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1. **Scan** — recursively finds images (`jpg/jpeg/png/heic/tiff/webp/bmp`).
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Videos are counted but skipped. Hidden files and `._*` AppleDouble sidecars
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are never touched.
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2. **Analyze** — each photo is downscaled with `sips` to a temp copy (original
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is only ever *read*), sent to the chosen Ollama vision model with a JSON
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schema that forces `{description, category}` output.
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3. **Write** — `exiftool` embeds:
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- `EXIF:ImageDescription`, `IPTC:Caption-Abstract`, `XMP-dc:Description` — the description
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- `XMP-dc:Subject` + `IPTC:Keywords` — the category, plus a `photon-tagged` marker
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- Writes use exiftool's temp-file + atomic-rename mode; file dates preserved with `-P`.
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4. **Journal** — every processed photo is appended to `photon_journal.jsonl`
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(path, description, category, model, timing). Restarting the app resumes
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where it left off ("skip already tagged").
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## The 10 categories
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People · Animals · Food & Drink · Nature & Outdoors · City & Buildings ·
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Vehicles · Screenshots & Documents · Events & Parties · Objects & Stuff ·
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Art & Miscellaneous
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## Smart router (recommended)
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With the **smart router** toggle on, a fast scout model (glm-ocr, 1.1B) first
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classifies each image as *screenshot* or *photo*, then hands it to the right
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describer with a specialized prompt. Screenshots also get an **OCR text embed**:
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glm-ocr transcribes the visible words and they're appended to the description
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(`… | text: …`), so you can find a screenshot by searching the exact words in it.
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Tested defaults: scout `glm-ocr` (6/6 routing accuracy) → describer
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`qwen3.5:4b` for both branches (8/8 accuracy, reads product labels correctly).
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## Settings that affect speed
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| Setting | Effect |
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|---|---|
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| Vision model | `glm-ocr` (1.1B) ≈ 7 s/photo; `qwen3.5:9b` slower but smarter |
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| Image feed resolution | 512 px is fastest; originals are untouched either way |
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| Description length | brief/standard/detailed — caps the model's output tokens |
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| Keep-alive | "forever" keeps the model in RAM between photos (fastest) |
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| Dry run | full pipeline but no metadata written |
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| Keep `_original` backups | exiftool keeps a backup copy of every file (doubles disk usage) |
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## Searching afterwards
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Spotlight: just type a word from a description in Finder search.
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Or from terminal: `mdfind -onlyin "/Volumes/sanD/allphotos from phone" "scooter"`
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Or grep the journal: `grep -i scooter photon_journal.jsonl`
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