hermes
24cdd452c5
The old UI was two pages of raw pandas tables with no navigation, no styling and no way to see a run's comments. This replaces it with a small, testable application and a beer-themed design. What changed ------------ Restructure - The domain logic now lives in `brewing.py` as pure functions over plain data, with no Flask or Mongo dependency. The gravity maths, the ABV formula and the run-aggregation rules are unit-testable on their own. - `app_factory.py` replaces the module-level app, and the routes move into blueprints under `views/`. A request-scoped Mongo repository is created per request and closed at teardown. - `mongo/repository.py` replaces `build_db.py` / `query_db.py` / `get_conn.py`. Reading a document no longer requires a second query to fetch the fields. Interface - A dark, beer-themed design system: warm amber `#be7f39` on near-black, a condensed display face (Anton) for headings and Inter for body text. Both fonts and the chart library are vendored into `static/`, so the container needs no outbound network at runtime — the old templates pulled Bootstrap from a CDN. - A real navigation bar, a login page, a brew-process page with a proper form, and a run-detail page listing every reading with its comment. - The run summary tables now show the beer name and the latest comment, which the old pandas tables did not. - Native form controls (checkbox, number spinners, date icon, scrollbars) are restyled for the dark theme. Correctness - The alcohol calculation now uses the standard homebrew formula, `(OG - FG) x 131.25`. The old code used `(max - min) / 7.36 + 0.5`, which is not a recognised brewing formula and overstated every result. - Gravity points and decimal readings now convert correctly in both directions (`1039` <-> `1.039`). - A run's display name resolves to the most-used label across its readings, because `beer_type` is stored per-reading and drifts across a run. - Re-submitting a reading for the same run and day updates it in place rather than creating a duplicate. Security / production - The container ran the Werkzeug debugger with `FLASK_DEBUG=1`, exposing an interactive console on any unhandled error. It now runs waitress with debugging off. - `SECRET_KEY` comes from the environment. - The app secret is no longer a hard-coded string in source. Housekeeping - Removes the bokeh/duckdb/pandas dependency chain and the static file it generated at import time; removes the dead `add_user.py` and `test.py`. - Splits `requirements.txt` (runtime) from `requirements-dev.txt` (tests). - Adds a pytest suite (64 tests) and a `test` job to the CI workflow so the image is only built when the tests pass. - Adds a README describing the data model and a RUNBOOK for the cluster side. Data compatibility ------------------ The existing `beer_db.beer_data` documents are read as they are and are not migrated or rewritten. No collection is dropped, and no document is modified by this change other than through the explicit "record a reading" form, which keeps the original `(date, beer_run_id)` uniqueness rule.
105 lines
3.4 KiB
Markdown
105 lines
3.4 KiB
Markdown
# Brew Log
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Stores specific-gravity data for home-brewed beer runs in Mongo and turns it into
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useful figures — alcohol by volume, days in the fermenter, gravity trends.
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Flask app, Mongo backend, no build step for the front end.
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## Running it locally
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```bash
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python -m venv .venv
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. .venv/bin/activate
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pip install -r requirements-dev.txt
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export MONGO_HOST=192.168.178.229
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export MONGO_USER=root
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export MONGO_PASS=...
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export SECRET_KEY=anything-for-local-use
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cd src/flask
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python -m beer_data # dev server on :5000
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# or, production-style:
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python -m waitress --listen=0.0.0.0:5000 --call beer_data:create_app
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```
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Then open <http://localhost:5000/> and sign in.
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## Tests
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```bash
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. .venv/bin/activate
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python -m pytest tests/ -q
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```
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The tests use `mongomock`, so they need no database and no network.
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## Layout
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```
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src/flask/
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beer_data.py entry point; exposes `app` and `create_app`
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app_factory.py application factory, request-scoped Mongo repository
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brewing.py pure domain logic — gravity parsing, ABV, run aggregation
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views/ blueprints: auth, brews, (later) analysis
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mongo/repository.py Mongo access for the beer_data and beer_runs collections
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templates/ Jinja templates
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static/ CSS, JS, fonts, logo (all served locally — no CDN)
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tests/ pytest suite
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```
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`brewing.py` holds every piece of arithmetic and has no Flask or Mongo
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dependency, so the maths is unit-testable on its own.
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## Data model
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### `beer_data` — one document per gravity reading
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| Field | Type | Notes |
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|---|---|---|
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| `_id` | string | 24 random characters |
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| `beer_run_id` | string | The run this reading belongs to, e.g. `"20"` |
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| `beer_type` | string | Beer/cider name. **Free text, per reading** — it drifts across a run |
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| `sg` | string | Specific gravity in *points*: `1039` means 1.039 |
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| `date` | string | `YYYY-MM-DD` |
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| `final_reading` | string | `"True"` / `"False"` — marks the last reading of a run |
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| `comment` | string | Free text |
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The unique key is `(date, beer_run_id)`: re-submitting a reading for the same
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run and day updates it in place rather than creating a duplicate.
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A run is **active** while it has no reading with `final_reading == "True"`.
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### `beer_runs` — one document per run (metadata)
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Added alongside the readings to hold what cannot be derived from them: the
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canonical beer name, the tap number, the recorded original gravity, and brew
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notes. This collection is only ever inserted into, never written over by the
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backfill.
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## Alcohol by volume
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Standard homebrew formula:
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```
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ABV% = (OG - FG) × 131.25
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```
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With gravity stored in points, the drop is already in points, so
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`ABV% = points_drop × 0.13125`.
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> The previous version of this app used `(max - min) / 7.36 + 0.5`, which is not
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> a recognised brewing formula and overstated every result. The old figure is
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> still computed by `brewing.legacy_abv_percent()` so the history page can show
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> the difference between the two.
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For a run still in the fermenter the figure is an **estimate**: it uses the
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run's first reading as the original gravity. A run with a single reading and no
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recorded OG has no reliable estimate, and the UI shows a dash rather than a
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misleading number.
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## Deployment
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The image is built and pushed by `.gitea/workflows/build_push.yml` on push to
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`master`. See [RUNBOOK.md](RUNBOOK.md) for the cluster side.
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