Initial commit of PHOTON photo intelligence app

Ollama-based photo tagging with exiftool metadata writes.

.gitignore

@@ -0,0 +1,3 @@
+__pycache__/
+*.pyc
+photon_journal.jsonl

README.md

@@ -0,0 +1,67 @@
+# PHOTON — local photo intelligence console
+
+A local web app that walks through a photo folder, has an Ollama vision model
+describe + categorize each photo, and embeds the result as standard metadata
+inside the photo file so everything becomes searchable (Spotlight, Photos,
+Lightroom, etc.). Nothing is ever deleted, moved, or renamed.
+
+## Run it
+
+```bash
+cd "/Users/drjones/photo ollama app organizer"
+python3 server.py
+# then open http://localhost:8765
+```
+
+Requires: Ollama running with a vision model, `exiftool` (installed via brew),
+macOS (`sips` is used for fast downscaling).
+
+## How it works
+
+1. **Scan** — recursively finds images (`jpg/jpeg/png/heic/tiff/webp/bmp`).
+   Videos are counted but skipped. Hidden files and `._*` AppleDouble sidecars
+   are never touched.
+2. **Analyze** — each photo is downscaled with `sips` to a temp copy (original
+   is only ever *read*), sent to the chosen Ollama vision model with a JSON
+   schema that forces `{description, category}` output.
+3. **Write** — `exiftool` embeds:
+   - `EXIF:ImageDescription`, `IPTC:Caption-Abstract`, `XMP-dc:Description` — the description
+   - `XMP-dc:Subject` + `IPTC:Keywords` — the category, plus a `photon-tagged` marker
+   - Writes use exiftool's temp-file + atomic-rename mode; file dates preserved with `-P`.
+4. **Journal** — every processed photo is appended to `photon_journal.jsonl`
+   (path, description, category, model, timing). Restarting the app resumes
+   where it left off ("skip already tagged").
+
+## The 10 categories
+
+People · Animals · Food & Drink · Nature & Outdoors · City & Buildings ·
+Vehicles · Screenshots & Documents · Events & Parties · Objects & Stuff ·
+Art & Miscellaneous
+
+## Smart router (recommended)
+
+With the **smart router** toggle on, a fast scout model (glm-ocr, 1.1B) first
+classifies each image as *screenshot* or *photo*, then hands it to the right
+describer with a specialized prompt. Screenshots also get an **OCR text embed**:
+glm-ocr transcribes the visible words and they're appended to the description
+(`… | text: …`), so you can find a screenshot by searching the exact words in it.
+
+Tested defaults: scout `glm-ocr` (6/6 routing accuracy) → describer
+`qwen3.5:4b` for both branches (8/8 accuracy, reads product labels correctly).
+
+## Settings that affect speed
+
+| Setting | Effect |
+|---|---|
+| Vision model | `glm-ocr` (1.1B) ≈ 7 s/photo; `qwen3.5:9b` slower but smarter |
+| Image feed resolution | 512 px is fastest; originals are untouched either way |
+| Description length | brief/standard/detailed — caps the model's output tokens |
+| Keep-alive | "forever" keeps the model in RAM between photos (fastest) |
+| Dry run | full pipeline but no metadata written |
+| Keep `_original` backups | exiftool keeps a backup copy of every file (doubles disk usage) |
+
+## Searching afterwards
+
+Spotlight: just type a word from a description in Finder search.
+Or from terminal: `mdfind -onlyin "/Volumes/sanD/allphotos from phone" "scooter"`
+Or grep the journal: `grep -i scooter photon_journal.jsonl`