Rename project from gastronomy to fuelish

Renamed all image tags, container/volume names, and doc references.

AGENT.md

@@ -0,0 +1,87 @@
+# fuelish
+
+Extracts structured data from photos of German fuel/petrol receipts:
+petrol station, transaction number, date, time, price per litre, litres,
+discount, and total price.
+
+The extraction pipeline is self-hosted (no cloud AI APIs) and is built to
+eventually run on a Raspberry Pi 4 cluster under k3s. Everything runs in
+containers; there is no host Python environment to set up.
+
+## Pipeline
+
+Three verifiable, independently-inspectable steps, each a thin host
+script that runs the actual work inside the `fuelish/tools` container:
+
+1. `1_preprocess_image.sh -in <photo> -out <dir>` - grayscale conversion,
+   a mild bilateral filter, and a percentile contrast stretch. Writes
+   every intermediate stage (`01_gray.png`, `02_bilateral.png`,
+   `03_final.png`) so each step can be checked on its own.
+2. `2_ocr_image.sh -in <dir>/03_final.png -out <dir>` - Tesseract OCR
+   (German), writes `ocr.txt`.
+3. `3_extract_data.sh -in <dir>/ocr.txt -out <dir>` - sends the raw OCR
+   text to a small local LLM (`qwen2.5:3b`, served by Ollama) with a JSON
+   schema, writes `extracted.json`.
+
+Preprocessing choice is evidence-based, not assumed: heavier processing
+(strong denoising, CLAHE, unsharp masking) was tried and measured worse -
+it amplifies the receipt's dotted security-background pattern into OCR
+noise. The lighter bilateral+autocontrast approach in
+`tooling-container/src/preprocess.py` is what survived testing.
+
+## Supporting scripts
+
+- `0_run_ollama.sh [-model <name>]` - starts Ollama as its own long-lived
+  Docker container (`fuelish-ollama`, model weights in the named
+  volume `fuelish-ollama-data`), pulling the model if not already
+  present. Deliberately separate from the tooling container: Ollama needs
+  to stay resident in memory with the model loaded, while the pipeline
+  steps are short-lived and stateless (one `docker run` per receipt).
+  `3_extract_data.sh` reaches it via `--network host` at
+  `http://127.0.0.1:11434` by default (override with `OLLAMA_URL`/
+  `OLLAMA_MODEL`).
+- `build-image.sh` / `upload-image.sh` - build/push the
+  `tooling-container/` image, tagged from `tooling-container/VERSION`.
+  Image name defaults to `fuelish/tools`, overridable via
+  `registry.env` (copy from `registry.env.example`; gitignored).
+- `test_batch.sh -in <test-data-dir>` - runs the full pipeline over every
+  `*.jpg` in a directory and compares results against that directory's
+  ground-truth spreadsheet (`*.ods`). For an input dir `<path>/<name>` it
+  creates sibling `<path>/<name>-scratch/` (intermediate step output per
+  receipt) and `<path>/<name>-result/` (final `extracted.json` per
+  receipt, plus `summary.txt`). Both are gitignored.
+
+## Current state
+
+Last full run against the 17-receipt ground-truth test set
+(`testdata/2026_new/`, not checked in - see `.gitignore`):
+
+- 15/17 receipts date-matched to ground truth (up from 11/17 with the
+  original plain-grayscale preprocessing)
+- price_per_litre: 11/15 correct, litres: 9/15, discount: 11/15,
+  total_price: 12/15
+
+Known remaining failure modes, not expected to be fixable by further
+preprocessing tuning:
+
+- OCR garbling on the compact "litres EUR/l price" line on some receipts
+  (the raw text sometimes lacks recoverable digits)
+- Occasional year-digit misreads in the date causing a false date-match
+  failure
+- Low-resolution source photos (well below the ~4000x3000 the good
+  results come from) OCR poorly regardless of preprocessing
+
+The structuring prompt in `tooling-container/src/extract.py` uses
+deliberately non-colliding example values. An earlier version used real
+receipt values as worked examples, which caused the small model to
+copy/anchor on those exact numbers instead of reading the actual OCR
+text - a subtle failure mode worth remembering if the prompt is revised.
+
+## Not yet built
+
+Per the original idea (see git history / project notes): an Android app
+to capture and confirm receipts, a backend server to store raw + confirmed
+submissions, a simple authenticated web UI to query/filter/export
+(CSV/JSON), and cross-checking total_price against
+price_per_litre * litres - discount. None of that exists yet - this repo
+currently covers only the extraction pipeline.