add ci yaml

.env.template

@@ -1,10 +0,0 @@
-LLM_API_BASE_URL = "http://localhost:1234/v1"
-LLM_API_KEY = "not-relevant"
-LLM_DEFAULT_TEMPERATURE = 1.0
-
-METADATA_PREFIX = "photools|"
-LOG_LEVEL=INFO
-LLM_REQUEST_TIMEOUT_SECONDS=10
-LLM_MAX_COMPLETION_TOKENS=1024
-LLM_MAX_RETRIES=5
-LLM_MAX_KEYWORDS=15

.gitea/workflows/ci.yml

@@ -1,37 +0,0 @@
----
-name: Tests
-
-on: [push]
-
-jobs:
-  test_and_lint:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - name: Set up Python
-        uses: actions/setup-python@v5
-        with:
-          python-version: "3.14"
-      - name: Install uv
-        run: pip install uv
-      - name: Install dependencies
-        run: uv sync --frozen
-      - name: copy env
-        run: cp .env.template .env
-      - name: Install exiftool (system dependency)
-        run: |
-          if command -v exiftool >/dev/null 2>&1; then
-            echo "exiftool already installed"
-          elif command -v sudo >/dev/null 2>&1; then
-            sudo apt-get update && sudo apt-get install -y --no-install-recommends libimage-exiftool-perl
-          else
-            apt-get update && apt-get install -y --no-install-recommends libimage-exiftool-perl
-          fi
-      - name: Run pytest
-        run: uv run pytest .
-      - name: Lint (ruff)
-        run: uv run ruff check .
-      - name: Typing (mypy)
-        run: uv run mypy .
-      - name: Check formatting (black)
-        run: uv run black --check . --extend-exclude .venv

.gitea/workflows/test.yml

@@ -0,0 +1,40 @@
+name: Tests
+
+on: [push]
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.14"
+      - name: Install uv
+        run: pip install uv
+      - name: Install dependencies
+        run: uv sync --frozen
+      - name: Copy default settings
+        run: cp settings.template.py settings.py
+      - name: Run pytest
+        run: uv run pytest
+
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.14"
+      - name: Install uv
+        run: pip install uv
+      - name: Install dependencies
+        run: uv sync --frozen
+      - name: Copy default settings
+        run: cp settings.template.py settings.py
+      - name: Check formatting (black)
+        run: uv run black --check . --extend-exclude .venv
+      - name: Lint (ruff)
+        run: uv run ruff check .

.gitignore

@@ -10,4 +10,3 @@ wheels/
 .venv
 
 .env
-logs/

AGENTS.md

@@ -1,42 +0,0 @@
-# Agent Instructions: PhotoTools3
-
-## Environment Setup
-
-Requires a `.env` file with the following variables:
-
-- `llm_api_key`: API key for the LLM provider.
-- `llm_api_base_url`: Base URL for the LLM API.
-- `llm_default_temperature`: Float value for temperature.
-
-## System Dependencies
-
-- `exiftool` binary: required by `pyexiftool` at runtime, not installable via pip/uv.
-  - Arch: `perl-image-exiftool`
-  - Debian/Ubuntu: `libimage-exiftool-perl`
-
-## Development Toolchain
-
-- **Dependency Management**: Use `uv`.
-- **Python Version**: Requires Python `>=3.14`.
-- **Testing**: Run `pytest` to execute the test suite in `test/`.
-- **Linting & Formatting**: Use `ruff` and `black`.
-
-## CLI Usage
-
-The primary entry point is the `photools3` command:
-
-- `photools3 keywords <image_path>`: Extracts keywords and captions from a single image.
-
-## Architecture & Logic
-
-- **`src/photools3/photo.py`**: Core image processing logic, including resizing, RGB conversion, and generating base64 JPEG encodings specifically for LLM vision compatibility.
-- **`src/photools3/llm_session.py`**: Manages the workflow of sending processed images to the LLM and parsing results.
-- **Configuration**: The list of keywords to extract is maintained in `config/keywords.txt`.
-
-## Code style
-
-- Avoid overly verbose docstrings or comments. Be precise!
-
-## Comunication
-
-- When done with code modifications, give a one-liner for the currently unstaged changes that can be used as a commit message.

README.md

@@ -1,101 +0,0 @@
-# PhotoTools3
-
-PhotoTools3 uses an OpenAI-compatible vision API to generate photo captions and
-keywords, then writes the results to photo metadata. It can also assess photo
-quality.
-
-## Setup
-
-Install the Python dependencies with `uv` and make sure ExifTool is installed and
-available on `PATH` (it is a system dependency, not a Python package):
-
-```bash
-uv sync
-```
-
-Create a `.env` file in the project root with the LLM connection details and the
-prefix used to identify metadata written by PhotoTools3:
-
-```dotenv
-llm_api_base_url=http://localhost:1234/v1
-llm_api_key=your-api-key
-llm_default_temperature=0.2
-metadata_prefix=photools3:
-LOG_LEVEL=DEBUG
-LLM_REQUEST_TIMEOUT_SECONDS=10
-LLM_MAX_COMPLETION_TOKENS=1024
-LLM_MAX_RETRIES=5
-LLM_MAX_KEYWORDS=15
-```
-
-Keyword suggestions are restricted to the vocabulary in `config/keywords.txt`.
-`LOG_LEVEL=DEBUG` enables detailed per-request and per-photo timings.
-`LLM_MAX_RETRIES` controls retries for malformed or structurally invalid
-responses; `5` means up to 6 requests for a photo. A valid response with
-unsupported keywords gets at most one targeted correction retry, then remaining
-invalid keywords are discarded.
-Each request is limited by `LLM_REQUEST_TIMEOUT_SECONDS`; a timeout reloads the
-LM Studio model before retrying. `LLM_MAX_COMPLETION_TOKENS` caps the generated
-response length. When all retries are exhausted, the model is reloaded before
-the failed photo is reported. `LLM_MAX_KEYWORDS` sets the maximum number of
-distinct keywords requested and stored per photo; excess keywords are discarded.
-
-## Generate metadata
-
-The `metadata-magic` command accepts one or more image paths and/or directories.
-The default mode is `both`, which generates and writes a caption and keywords:
-
-```bash
-uv run photools3 metadata-magic photo.dng
-```
-
-Choose a mode with `--mode`:
-
-```bash
-# Generate only keywords
-uv run photools3 metadata-magic photo.dng --mode keywords
-
-# Generate only a caption
-uv run photools3 metadata-magic photo.dng --mode captions
-
-# Explicitly request both (the default)
-uv run photools3 metadata-magic photo.dng --mode both
-```
-
-Pass multiple files to process them sequentially as a batch:
-
-```bash
-uv run photools3 metadata-magic photo1.dng photo2.jpg --mode both
-```
-
-Directories are scanned at the top level by default. Add `--recursive` to
-include supported images in subdirectories. You can combine directories and
-individual file paths:
-
-```bash
-uv run photools3 metadata-magic ./photos --recursive --mode keywords
-uv run photools3 metadata-magic ./photos photo-to-include.dng --mode captions
-```
-
-The command reports each processed file and exits with a non-zero status if any
-file fails. In a batch, it continues processing the remaining files after an
-individual failure. Generated metadata is written using ExifTool and XMP
-sidecars.
-
-## Assess photo quality
-
-The `quality` command reports an assessment without changing photo metadata:
-
-```bash
-uv run photools3 quality photo.dng
-```
-
-## Help
-
-Show the available commands and options with:
-
-```bash
-uv run photools3 --help
-uv run photools3 metadata-magic --help
-uv run photools3 quality --help
-```

config/keywords.txt

@@ -1,371 +0,0 @@
-architektur
-architektur|gebäude
-architektur|bauwerke
-architektur|architecture
-architektur|brücke
-architektur|brücke|bridge
-architektur|burg
-architektur|burg|castle
-architektur|dach
-architektur|dach|roof
-architektur|dächer
-architektur|garten
-architektur|garten|garden
-architektur|gleis
-architektur|gleis|rail
-architektur|industrie
-architektur|kirche
-architektur|kirche|church
-architektur|leuchtturm
-architektur|leuchtturm|lighhouse
-architektur|levada
-architektur|modell
-architektur|park
-architektur|ruinen
-architektur|ruinen|ruins
-architektur|siedlung
-architektur|siedlung|dorf ortschaft
-architektur|siedlung|village
-architektur|skyline
-architektur|stadt
-architektur|stadt|city
-architektur|strasse
-architektur|strasse|street
-architektur|fenster
-architektur|fenster|window
-architektur|tür
-architektur|tür|door
-architektur|treppe
-architektur|treppe|stairs
-architektur|turm
-architektur|turm|tower
-architektur|hafen
-architektur|hafen|harbor
-architektur|hafen|port
-architektur|bahnhof
-architektur|bahnhof|train station
-architektur|museum
-essen&trinken
-essen&trinken|Lebensmittel
-essen&trinken|food
-essen&trinken|braten
-essen&trinken|buffett
-essen&trinken|burger
-essen&trinken|kaffee
-essen&trinken|kaffee|coffee
-essen&trinken|shrimps
-essen&trinken|tapas
-essen&trinken|bier
-essen&trinken|bier|beer
-essen&trinken|wein
-essen&trinken|wein|wine
-essen&trinken|obst
-essen&trinken|obst|fruit
-essen&trinken|gemüse
-essen&trinken|gemüse|vegetables
-essen&trinken|kuchen
-essen&trinken|kuchen|cake
-essen&trinken|dessert
-himmel/wetter
-himmel/wetter|weather
-himmel/wetter|sky
-himmel/wetter|abendrot
-himmel/wetter|abendrot|dusk
-himmel/wetter|blauer himmel
-himmel/wetter|blauer himmel|blue sky
-himmel/wetter|diesig
-himmel/wetter|diesig|hazy
-himmel/wetter|diesig|misty
-himmel/wetter|eis
-himmel/wetter|eis|ice
-himmel/wetter|grau
-himmel/wetter|grau|grey
-himmel/wetter|mond
-himmel/wetter|mond|moon
-himmel/wetter|morgengrauen
-himmel/wetter|morgengrauen|dawn
-himmel/wetter|morgenrot
-himmel/wetter|morgenrot|dawn
-himmel/wetter|nebel
-himmel/wetter|nebel|fog
-himmel/wetter|regen
-himmel/wetter|regen|rain
-himmel/wetter|regenbogen
-himmel/wetter|regenbogen|rainbow
-himmel/wetter|schnee
-himmel/wetter|schnee|snow
-himmel/wetter|sonne
-himmel/wetter|sonne|sun
-himmel/wetter|sonne|sonnenaufgang
-himmel/wetter|sonne|sonnenaufgang|sunrise
-himmel/wetter|sonne|sonnenstrahlen
-himmel/wetter|sonne|sonnenstrahlen|sun beams
-himmel/wetter|sonne|sonnenstrahlen|light rays
-himmel/wetter|sonne|sonnenuntergang
-himmel/wetter|sonne|sonnenuntergang|sunset
-himmel/wetter|sterne
-himmel/wetter|sterne|stars
-himmel/wetter|windig
-himmel/wetter|windig|windy
-himmel/wetter|wolken
-himmel/wetter|wolken|clouds
-himmel/wetter|gewitter
-himmel/wetter|gewitter|thunderstorm
-himmel/wetter|blitz
-himmel/wetter|blitz|lightning
-himmel/wetter|frost
-jahreszeit
-jahreszeit|season
-jahreszeit|frühling
-jahreszeit|frühling|spring
-jahreszeit|sommer
-jahreszeit|sommer|summer
-jahreszeit|herbst
-jahreszeit|herbst|autumn
-jahreszeit|herbst|fall
-jahreszeit|winter
-landschaft
-landschaft|landscape
-landschaft|berge
-landschaft|berge|mountains
-landschaft|dünen
-landschaft|dünen|dunes
-landschaft|dünen|sand
-landschaft|feld
-landschaft|feld|field
-landschaft|feld|meadow
-landschaft|felsen
-landschaft|felsen|rock
-landschaft|höhle
-landschaft|höhle|cave
-landschaft|insel
-landschaft|insel|island
-landschaft|klippen
-landschaft|klippen|cliffs
-landschaft|küste
-landschaft|küste|coast
-landschaft|steinbruch
-landschaft|steinbruch|quarry
-landschaft|strand
-landschaft|strand|beach
-landschaft|vulkan
-landschaft|vulkan|vulcano
-landschaft|wald
-landschaft|wald|forest
-landschaft|wald|wood
-landschaft|wasser
-landschaft|wasser|water
-landschaft|wasser|bach
-landschaft|wasser|bach|creek
-landschaft|wasser|brandung
-landschaft|wasser|brandung|breakers
-landschaft|wasser|brandung|surf
-landschaft|wasser|fluss
-landschaft|wasser|fluss|river
-landschaft|wasser|hochwasser
-landschaft|wasser|hochwasser|flood
-landschaft|wasser|hochwasser|high water
-landschaft|wasser|meer
-landschaft|wasser|meer|sea
-landschaft|wasser|meer|ocean
-landschaft|wasser|see
-landschaft|wasser|see|lake
-landschaft|wasser|wasserfall
-landschaft|wasser|wasserfall|waterfall
-landschaft|weinberge
-landschaft|weinberge|vineyard
-landschaft|wüste
-landschaft|wüste|desert
-landschaft|hügel
-landschaft|hügel|hills
-landschaft|tal
-landschaft|tal|valley
-landschaft|schlucht
-landschaft|schlucht|canyon
-landschaft|schlucht|gorge
-landschaft|gipfel
-landschaft|gipfel|summit
-landschaft|gipfel|peak
-nacht
-nacht|night
-nacht|langzeitbelichtung
-nacht|langzeitbelichtung|long exposure
-nacht|lichtspuren
-nacht|lichtspuren|light trails
-nacht|nachtaufnahme
-nacht|nachtaufnahme|night shot
-nacht|stadtlichter
-nacht|stadtlichter|city lights
-personen
-personen|auge
-personen|auge|eye
-personen|geste
-personen|geste|gesture
-personen|gruppenbild
-personen|gruppenbild|group
-personen|menschenmenge
-personen|menschenmenge|crowd
-personen|portrait
-personen|pose
-personen|quatsch
-personen|quatsch|silly
-personen|verkleidung
-personen|kind
-personen|kind|kinder
-personen|kind|child
-personen|kind|children
-personen|paar
-personen|paar|couple
-personen|silhouette
-personen|schatten
-personen|schatten|shadow
-pflanze
-pflanze|plant
-pflanze|bäume
-pflanze|bäume|trees
-pflanze|blumen
-pflanze|blumen|flower
-pflanze|blüten
-pflanze|blüten|flower
-pflanze|blüten|bloomy
-pflanze|grass
-pflanze|grass|gras
-pflanze|kaktus
-pflanze|kaktus|cactus
-pflanze|laub
-pflanze|laub|foliage
-pflanze|moos
-pflanze|moos|moss
-pflanze|palmen
-pflanze|palmen|palms
-pflanze|pilze
-pflanze|pilze|mushrooms
-pflanze|schilf
-pflanze|schilf|reed
-pflanze|sonnenblumen
-pflanze|sonnenblumen|sunflower
-pflanze|sonnentau
-pflanze|sonnentau|sundew
-pflanze|venusfliegenfalle
-pflanze|venusfliegenfalle|venus trap
-pflanze|farn
-pflanze|farn|fern
-pflanze|strauch
-pflanze|strauch|bush
-pflanze|strauch|shrub
-pflanze|efeu
-pflanze|efeu|ivy
-pflanze|hecke
-pflanze|hecke|hedge
-sport
-sport|fussball
-sport|fussball|football
-sport|fussball|soccer
-sport|schwimmen
-sport|schwimmen|swimming
-sport|klettern
-sport|klettern|climbing
-sport|wandern
-sport|wandern|hiking
-sport|radfahren
-sport|radfahren|cycling
-sport|ski
-sport|ski|skiing
-stil
-stil|style
-stil|schwarzweiss
-stil|schwarzweiss|black and white
-stil|schwarzweiss|bw
-stil|vintage
-stil|hdr
-stil|panorama
-technik
-technik|technology
-technik|auto
-technik|auto|car
-technik|eisenbahn
-technik|eisenbahn|train
-technik|fahrrad
-technik|fahrrad|bike
-technik|fahrrad|bicycle
-technik|flugzeug
-technik|flugzeug|plane
-technik|handy
-technik|handy|smartphone
-technik|handy|cell phone
-technik|kamera
-technik|kamera|camera
-technik|musikinstrument
-technik|musikinstrument|musical instrument
-technik|musikinstrument|Gitarre
-technik|musikinstrument|Gitarre|guitar
-technik|musikinstrument|Gitarren Effektgerät
-technik|musikinstrument|Gitarrenverstärker
-technik|musikinstrument|Gitarrenverstärker|guitar amp
-technik|objektiv
-technik|objektiv|lens
-technik|schiff
-technik|schiff|ship
-technik|seilbahn
-technik|seilbahn|teleferico
-technik|seilbahn|cable car
-technik|motorrad
-technik|motorrad|motorcycle
-technik|lastwagen
-technik|lastwagen|truck
-technik|bus
-technik|helikopter
-technik|helikopter|helicopter
-technik|drohne
-technik|drohne|drone
-technik|windrad
-technik|windrad|wind turbine
-tier
-tier|animal
-tier|elefant
-tier|elefant|elephant
-tier|fisch
-tier|fisch|fish
-tier|insekten
-tier|insekten|insects
-tier|insekten|biene
-tier|insekten|biene|bee
-tier|katze
-tier|katze|cat
-tier|nager
-tier|nager|gnawer
-tier|reptil
-tier|reptil|reptile
-tier|rind
-tier|rind|cattle
-tier|Rot$DamWild
-tier|Rot$DamWild|deer
-tier|spinne
-tier|spinne|spider
-tier|tiger
-tier|vogel
-tier|vogel|bird
-tier|waschbär
-tier|waschbär|raccoon
-tier|Wildschwein
-tier|Wildschwein|boar
-tier|ZiegeBockSchaf
-tier|ZiegeBockSchaf|goat
-tier|hund
-tier|hund|dog
-tier|pferd
-tier|pferd|horse
-tier|eichhörnchen
-tier|eichhörnchen|squirrel
-tier|fuchs
-tier|fuchs|fox
-tier|seevogel
-tier|seevogel|seabird
-tier|eidechse
-tier|eidechse|lizard
-tier|frosch
-tier|frosch|frog
-tier|schmetterling
-tier|schmetterling|butterfly
-tier|libelle
-tier|libelle|dragonfly

pyproject.toml

@@ -8,17 +8,10 @@ authors = [
 ]
 requires-python = ">=3.14"
 dependencies = [
-    "black>=26.5.1",
-    "mypy>=2.4.0",
     "openai>=3.22.0",
-    "pillow>=12.3.0",
     "pydantic>=2.13.5",
     "pydantic-settings>=2.15.0",
-    "pyexiftool>=0.5.6",
     "pytest>=9.1.1",
-    "ruff>=0.16.9",
-    "typer>=0.12.0",
-    "utils>=0.1.3",
 ]
 
 [project.scripts]
@@ -27,18 +20,3 @@ photools3 = "photools3:main"
 [build-system]
 requires = ["uv_build>=0.12.19,<0.13.0"]
 build-backend = "uv_build"
-
-[tool.uv.sources]
-utils = { index = "gitea" }
-
-[tool.black]
-target-version = [ "py314" ]
-
-[[tool.mypy.overrides]]
-module = ["exiftool", "exiftool.*", "utils", "utils.*"]
-ignore_missing_imports = true
-
-[[tool.uv.index]]
-name = "gitea"
-url = "https://gitea.schneiderserver.site/api/packages/mathias2784/pypi/simple"
-explicit = true

src/photools3/__init__.py

@@ -1,5 +1,2 @@
-from .cli import app
+def main() -> None:
-
+    print("Hello from photools3!")
-
-def main():
-    app()

src/photools3/__main__.py

@@ -1,4 +0,0 @@
-from .cli import app
-
-if __name__ == "__main__":
-    app()

src/photools3/cli.py

@@ -1,163 +0,0 @@
-import logging
-from pathlib import Path
-from typing import Annotated
-
-import typer
-from PIL import Image
-from utils.logging_config import LoggingSettings
-
-from photools3.llm_session import LlmSessionQuality
-from photools3.photo import Photo
-from photools3.processing import (
-    ProcessingMode,
-    process_batch,
-    process_photo,
-)
-from photools3.settings import settings
-
-app = typer.Typer(help="PhotoTools3: A CLI for image processing.")
-LoggingSettings(LOG_LEVEL=settings.log_level)
-for name in (
-    "uvicorn",
-    "uvicorn.error",
-    "uvicorn.access",
-    "PIL",
-    "openai",
-    "httpx2",
-    "httpcore2",
-):
-    logging.getLogger(name).setLevel(logging.WARNING)
-logger = logging.getLogger(__name__)
-
-
-@app.callback(invoke_without_command=True)
-def main(
-    ctx: typer.Context,
-    version: bool | None = typer.Option(None, "--version", is_eager=True),
-):
-    """Main entry point for PhotoTools3 CLI."""
-    if version:
-        typer.echo("photools3 version 0.1.0")
-        raise typer.Exit()
-
-    if ctx.invoked_subcommand is None:
-        typer.echo(ctx.get_help())
-        raise typer.Exit()
-
-
-@app.command("metadata-magic")
-def metadata_magic(
-    paths: Annotated[
-        list[Path], typer.Argument(..., help="Photo file(s) and/or directory paths")
-    ],
-    mode: Annotated[
-        ProcessingMode,
-        typer.Option(
-            "--mode",
-            case_sensitive=False,
-            help="Metadata to generate and write",
-        ),
-    ] = ProcessingMode.BOTH,
-    recursive: Annotated[
-        bool,
-        typer.Option(
-            "--recursive/--no-recursive",
-            help="Recursively discover photos inside input directories",
-        ),
-    ] = False,
-):
-    """Generate and write keywords, captions, or both for photos."""
-    _run_processing(paths, mode, recursive)
-
-
-@app.command("quality")
-def quality(
-    file_path: Annotated[Path, typer.Argument(..., help="Path to the image file")],
-):
-    """Assess a photo's quality without changing its metadata."""
-    try:
-        with LlmSessionQuality() as session, Photo(file_path) as photo:
-            result = session.call([photo])
-        logger.info("Quality assessment for %s: %s", file_path, result)
-    except FileNotFoundError:
-        logger.error("Photo not found: %s", file_path)
-        raise typer.Exit(code=1) from None
-    except Exception as exc:
-        logger.error("Error assessing photo %s: %s", file_path, exc)
-        raise typer.Exit(code=1) from exc
-
-
-def _run_processing(inputs: list[Path], mode: ProcessingMode, recursive: bool) -> None:
-    photo_paths = _expand_photo_paths(inputs, recursive)
-    if not photo_paths:
-        logger.error("No supported photo files found in the given input paths.")
-        raise typer.Exit(code=1)
-
-    logger.info("Processing %d photo(s) in %s mode", len(photo_paths), mode.value)
-    if len(photo_paths) == 1:
-        path = photo_paths[0]
-        try:
-            process_photo(path, mode)
-        except FileNotFoundError:
-            logger.error("Photo not found: %s", path)
-            raise typer.Exit(code=1) from None
-        except Exception as exc:
-            logger.error("Failed to process %s: %s", path, exc)
-            raise typer.Exit(code=1) from exc
-        logger.info("Processed: %s", path)
-        return
-
-    try:
-        outcomes = process_batch(photo_paths, mode)
-    except Exception as exc:
-        logger.error("Batch processing failed: %s", exc)
-        raise typer.Exit(code=1) from exc
-
-    failures = 0
-    for outcome in outcomes:
-        if outcome.succeeded:
-            logger.info("Processed: %s", outcome.path)
-        else:
-            failures += 1
-            logger.error("Failed to process %s: %s", outcome.path, outcome.error)
-
-    logger.info(
-        "Batch complete: %d succeeded, %d failed", len(outcomes) - failures, failures
-    )
-    if failures:
-        raise typer.Exit(code=1)
-
-
-def _expand_photo_paths(inputs: list[Path], recursive: bool) -> list[Path]:
-    """Expand directories to supported image files, preserving input order."""
-    supported_extensions = {
-        ".dng",
-        *(extension.lower() for extension in Image.registered_extensions()),
-    }
-    discovered: list[Path] = []
-
-    for path in inputs:
-        if not path.is_dir():
-            discovered.append(path)
-            continue
-
-        try:
-            candidates = path.rglob("*") if recursive else path.iterdir()
-            discovered.extend(
-                sorted(
-                    candidate
-                    for candidate in candidates
-                    if candidate.is_file()
-                    and candidate.suffix.lower() in supported_extensions
-                )
-            )
-        except OSError as exc:
-            logger.error("Could not read directory %s: %s", path, exc)
-            raise typer.Exit(code=1) from exc
-
-    # Avoid processing the same exact path more than once while retaining order.
-    return list(dict.fromkeys(discovered))
-
-
-if __name__ == "__main__":
-    app()

src/photools3/llm_session.py

@@ -1,108 +1,25 @@
-import json
-import logging
-import re
-import time
-
-import openai
 from openai import OpenAI
-from pydantic import BaseModel, ValidationError
+from openai.types.chat import ChatCompletion
 
-from photools3.lm_studio import LMStudioModelManager
+from photools3.models import LlmRequest
-from photools3.models import (
+from photools3.settings import Settings
-    LlmMessageContent,
-    LlmMessageRole,
-    LlmMessageType,
-    LlmRequest,
-    LlmRequestMessage,
-    LlmResponseCaption,
-    LlmResponseCaptionsAndKeywords,
-    LlmResponseGeneral,
-    LlmResponseKeywords,
-    LlmResponseQuality,
-    load_valid_keywords,
-)
-from photools3.photo import Photo
-from photools3.settings import settings
 
-logger = logging.getLogger(__name__)
+settings = Settings()
-
-
-def _clean_llm_response(txt: str):
-    # remove ```json ... ``` wrap
-    txt = re.sub(r"^```json(.*)```$", r"\1", txt, flags=re.DOTALL).strip()
-    return txt
-
-
-def _correction_prompt(instruction: str) -> LlmRequestMessage:
-    return LlmRequestMessage(
-        role=LlmMessageRole.user,
-        content=instruction,
-    )
-
-
-def _request_with_correction(base_request: LlmRequest, instruction: str) -> LlmRequest:
-    return base_request.model_copy(
-        update={"messages": [*base_request.messages, _correction_prompt(instruction)]}
-    )
-
-
-def _validation_summary(exception: ValidationError) -> str:
-    return "; ".join(
-        f"{'.'.join(map(str, error['loc']))}: {error['msg']}"
-        for error in exception.errors(include_input=False)
-    )
 
 
 class LlmSession:
     base_url: str = str(settings.llm_api_base_url)
     api_key: str = settings.llm_api_key
-    response_model: type[BaseModel] = LlmResponseGeneral
-    prompt: str = ""
 
-    def __init__(
+    def __init__(self, system_prompt: str):
-        self, system_prompt: str, temperature: float = settings.llm_default_temperature
-    ):
         self._models: list = []
-        self._model_id: str | None = None
 
         self.system_prompt = system_prompt
-        self.temperature = temperature
 
         self.client = OpenAI(
             base_url=self.base_url,
             api_key=self.api_key,
-            max_retries=0,
         )
-        self._model_manager = LMStudioModelManager(self.base_url, self.api_key)
-
-    def close(self):
-        self.client.close()
-
-    def __enter__(self):
-        return self
-
-    def __exit__(self, exc_type, exc_value, traceback):
-        self.close()
-
-    def flush_model(self):
-        """Unload and reload this session's model through LM Studio."""
-        if self._model_id is None:
-            self._model_id = self.models[0].id
-        self._model_manager.reload_model(self._model_id)
-
-    def client_chat_completion_create(self, request: LlmRequest, choice_no: int = 0):
-        response = self.client.chat.completions.create(
-            **request.model_dump(),
-            timeout=settings.llm_request_timeout_seconds,
-        )
-        return response.choices[choice_no].message.content
-
-    def is_api_reachable(self) -> bool:
-        try:
-            _ = self.client.models.list().data
-            return True
-        except openai.APIConnectionError:
-            return False
 
     @property
     def models(self):
@@ -110,247 +27,5 @@ class LlmSession:
             self._models = self.client.models.list().data
         return self._models
 
-    def call(
+    def call_raw(self, request: LlmRequest) -> ChatCompletion:
-        self,
+        return self.client.chat.completions.create(**request.model_dump())
-        photos: list[Photo],
-        model_id=None,
-        max_retries: int | None = None,
-        prompt: str | None = None,
-    ):
-        retry_limit = settings.llm_max_retries if max_retries is None else max_retries
-        if retry_limit < 0:
-            raise ValueError("max_retries must be greater than or equal to zero")
-
-        if model_id is None:
-            if self._model_id is None:
-                self._model_id = self.models[0].id
-            model_id = self._model_id
-        else:
-            self._model_id = model_id
-
-        base_request = self._build_request(prompt or self.prompt, photos, model_id)
-        request = base_request
-        tag_correction_used = False
-        valid_keywords = load_valid_keywords()
-        max_keywords = settings.llm_max_keywords
-
-        for attempt in range(retry_limit + 1):
-            request_started = time.perf_counter()
-            try:
-                result_raw = self.client_chat_completion_create(request)
-            except openai.APITimeoutError as exc:
-                logger.warning(
-                    "LLM request timed out after %.2f seconds; reloading model",
-                    time.perf_counter() - request_started,
-                )
-                self.flush_model()
-                if attempt < retry_limit:
-                    continue
-                raise ValueError(
-                    f"LLM request timed out after {retry_limit + 1} attempts"
-                ) from exc
-            except Exception:
-                logger.debug(
-                    "LLM request attempt %d/%d failed after %.2f seconds",
-                    attempt + 1,
-                    retry_limit + 1,
-                    time.perf_counter() - request_started,
-                )
-                raise
-
-            logger.debug(
-                "LLM request attempt %d/%d completed in %.2f seconds",
-                attempt + 1,
-                retry_limit + 1,
-                time.perf_counter() - request_started,
-            )
-            try:
-                response_data = json.loads(_clean_llm_response(result_raw))
-            except (json.JSONDecodeError, TypeError) as exc:
-                if attempt < retry_limit:
-                    logger.info("Chatbot response was not valid JSON; retrying")
-                    request = _request_with_correction(
-                        base_request,
-                        "Return one complete JSON object matching the schema. "
-                        f"The previous response was invalid: {exc}",
-                    )
-                    continue
-
-                logger.warning(
-                    "LLM retry limit reached; reloading model before failing"
-                )
-                self.flush_model()
-                raise ValueError(
-                    f"Failed to parse LLM response after {retry_limit} retries: {exc}"
-                ) from exc
-
-            if (
-                isinstance(response_data, dict)
-                and "tags" in self.response_model.model_fields
-                and isinstance(response_data.get("tags"), list)
-            ):
-                tags = response_data["tags"]
-                considered_tags = tags[:max_keywords]
-                invalid_tags = [
-                    tag
-                    for tag in considered_tags
-                    if not isinstance(tag, str) or tag not in valid_keywords
-                ]
-
-                if invalid_tags and not tag_correction_used and attempt < retry_limit:
-                    tag_correction_used = True
-                    logger.info(
-                        "LLM returned unsupported keywords; requesting one correction"
-                    )
-                    request = _request_with_correction(
-                        base_request,
-                        f"These keywords are not allowed: {invalid_tags!r}. "
-                        "Replace them with exact allowed keywords when sensible; "
-                        f"otherwise omit them. Return at most {max_keywords} "
-                        "distinct keywords and do not list the vocabulary.",
-                    )
-                    continue
-
-                if len(tags) > max_keywords:
-                    logger.info(
-                        "Discarding keywords beyond configured limit of %d",
-                        max_keywords,
-                    )
-                if invalid_tags:
-                    logger.warning("Discarding unsupported keywords: %s", invalid_tags)
-
-                response_data = dict(response_data)
-                valid_tags = [
-                    tag
-                    for tag in tags
-                    if isinstance(tag, str) and tag in valid_keywords
-                ]
-                response_data["tags"] = list(dict.fromkeys(valid_tags))[:max_keywords]
-
-            try:
-                return self.response_model.model_validate(response_data)
-            except ValidationError as exc:
-                if attempt < retry_limit:
-                    logger.info("LLM response did not match the schema; retrying")
-                    request = _request_with_correction(
-                        base_request,
-                        "Return one complete JSON object matching the schema. "
-                        f"Validation issues: {_validation_summary(exc)}",
-                    )
-                    continue
-
-                logger.warning(
-                    "LLM retry limit reached; reloading model before failing"
-                )
-                self.flush_model()
-                raise ValueError(
-                    f"Failed to validate LLM response after {retry_limit} retries: "
-                    f"{_validation_summary(exc)}"
-                ) from exc
-
-    def _build_request(
-        self,
-        prompt: str,
-        photos: list[Photo],
-        model_id: str,
-    ) -> LlmRequest:
-        """Build a valid request that complies with openai API"""
-        return LlmRequest(
-            model=model_id,
-            temperature=self.temperature,
-            max_tokens=settings.llm_max_completion_tokens,
-            messages=[
-                # System prompt
-                LlmRequestMessage(
-                    role=LlmMessageRole.system,
-                    content=self.system_prompt,
-                ),
-                # User prompt
-                LlmRequestMessage(
-                    role=LlmMessageRole.user,
-                    content=[
-                        # Text prompt
-                        LlmMessageContent(
-                            type=LlmMessageType.text,
-                            text=prompt,
-                        ),
-                        # Image contents
-                        *[
-                            LlmMessageContent(
-                                type=LlmMessageType.image_url,
-                                image_url=photo.to_llm_image_url(),
-                            )
-                            for photo in photos
-                        ],
-                    ],
-                ),
-            ],
-        )
-
-
-class LlmSessionCaptionsAndKeywords(LlmSession):
-    response_model = LlmResponseCaptionsAndKeywords
-    prompt = "Give me a caption and keywords for the following photo"
-
-    def __init__(self, temperature: float = settings.llm_default_temperature):
-        super().__init__(
-            system_prompt=(
-                "You are a helpful photographer's assistant that helps to write **captions** and **keywords** to photos:\n"
-                "Answer in German.\n"
-                f"Choose at most {settings.llm_max_keywords} distinct, relevant keywords. "
-                "Do not repeat or enumerate the allowed-keyword list.\n"
-                "Output must be a JSON of the following schema:\n"
-                f"{self.response_model.model_json_schema()}"
-            ),
-            temperature=temperature,
-        )
-
-
-class LlmSessionCaption(LlmSession):
-    response_model = LlmResponseCaption
-    prompt = "Write a caption for the following photo"
-
-    def __init__(self, temperature: float = settings.llm_default_temperature):
-        super().__init__(
-            system_prompt=(
-                "You are a helpful photographer's assistant that writes captions "
-                "for photos. Answer in German.\n"
-                "Output must be a JSON of the following schema:\n"
-                f"{self.response_model.model_json_schema()}"
-            ),
-            temperature=temperature,
-        )
-
-
-class LlmSessionKeywords(LlmSession):
-    response_model = LlmResponseKeywords
-    prompt = "Choose appropriate keywords for the following photo"
-
-    def __init__(self, temperature: float = settings.llm_default_temperature):
-        super().__init__(
-            system_prompt=(
-                "You are a helpful photographer's assistant that chooses keywords "
-                "for photos. Answer in German.\n"
-                f"Choose at most {settings.llm_max_keywords} distinct, relevant keywords. "
-                "Do not repeat or enumerate the allowed-keyword list.\n"
-                "Output must be a JSON of the following schema:\n"
-                f"{self.response_model.model_json_schema()}"
-            ),
-            temperature=temperature,
-        )
-
-
-class LlmSessionQuality(LlmSession):
-    response_model = LlmResponseQuality
-    prompt = "Assess the quality of the following photos to help me decide which ones to sort out"
-
-    def __init__(self, temperature: float = settings.llm_default_temperature):
-        super().__init__(
-            system_prompt=(
-                "You are a helpful photographer's assistant that helps to identify "
-                "photos that can be sorted out due to qulity issues:\n"
-                "Output must be a JSON of the following schema:\n"
-                f"{self.response_model.model_json_schema()}"
-            ),
-            temperature=temperature,
-        )

src/photools3/lm_studio.py

@@ -1,135 +0,0 @@
-import json
-import logging
-import time
-from urllib.error import HTTPError, URLError
-from urllib.parse import urlsplit, urlunsplit
-from urllib.request import Request, urlopen
-
-logger = logging.getLogger(__name__)
-
-_LOAD_CONFIG_KEYS = (
-    "context_length",
-    "eval_batch_size",
-    "flash_attention",
-    "num_experts",
-    "offload_kv_cache_to_gpu",
-)
-
-
-class LMStudioError(RuntimeError):
-    """Raised when LM Studio model management fails."""
-
-
-class LMStudioModelManager:
-    """Unload and reload a model through LM Studio's native REST API."""
-
-    def __init__(self, openai_base_url: str, api_key: str, timeout: float = 600):
-        parsed_url = urlsplit(openai_base_url)
-        path = parsed_url.path.rstrip("/")
-        path = path.removesuffix("/v1")
-        root_url = urlunsplit(
-            (parsed_url.scheme, parsed_url.netloc, path, "", "")
-        ).rstrip("/")
-        self.api_base_url = f"{root_url}/api/v1"
-        self.api_key = api_key
-        self.timeout = timeout
-
-    def reload_model(self, model_id: str) -> None:
-        """Unload the matching loaded instance and reload it with its config."""
-        started = time.perf_counter()
-        models_response = self._request("GET", "/models")
-        model, loaded_instance = self._find_model(models_response, model_id)
-        model_key = model["key"]
-
-        if loaded_instance is not None:
-            self._request(
-                "POST",
-                "/models/unload",
-                {"instance_id": loaded_instance["id"]},
-            )
-            load_config = loaded_instance.get("config", {})
-        else:
-            load_config = {}
-
-        load_body = {"model": model_key}
-        load_body.update(
-            {
-                key: load_config[key]
-                for key in _LOAD_CONFIG_KEYS
-                if key in load_config and load_config[key] is not None
-            }
-        )
-        self._request("POST", "/models/load", load_body)
-        logger.info(
-            "Reloaded LM Studio model %s in %.1f seconds",
-            model_key,
-            time.perf_counter() - started,
-        )
-
-    @staticmethod
-    def _find_model(models_response: dict, model_id: str) -> tuple[dict, dict | None]:
-        models = models_response.get("models")
-        if not isinstance(models, list):
-            raise LMStudioError("LM Studio model list response has no models array")
-
-        for model in models:
-            loaded_instances = model.get("loaded_instances", [])
-            for instance in loaded_instances:
-                if instance.get("id") == model_id:
-                    return model, instance
-
-        matching_models = [model for model in models if model.get("key") == model_id]
-        if len(matching_models) != 1:
-            raise LMStudioError(
-                f"Could not uniquely identify LM Studio model {model_id!r}"
-            )
-
-        model = matching_models[0]
-        loaded_instances = model.get("loaded_instances", [])
-        if len(loaded_instances) > 1:
-            raise LMStudioError(
-                f"Multiple loaded instances found for LM Studio model {model_id!r}"
-            )
-        loaded_instance = loaded_instances[0] if loaded_instances else None
-        return model, loaded_instance
-
-    def _request(self, method: str, path: str, body: dict | None = None) -> dict:
-        data = None if body is None else json.dumps(body).encode("utf-8")
-        headers = {"Accept": "application/json"}
-        if data is not None:
-            headers["Content-Type"] = "application/json"
-        if self.api_key:
-            headers["Authorization"] = f"Bearer {self.api_key}"
-
-        request = Request(
-            f"{self.api_base_url}{path}",
-            data=data,
-            headers=headers,
-            method=method,
-        )
-        try:
-            with urlopen(request, timeout=self.timeout) as response:
-                response_body = response.read()
-        except HTTPError as exc:
-            detail = exc.read().decode("utf-8", errors="replace")
-            raise LMStudioError(
-                f"LM Studio {method} {path} returned HTTP {exc.code}: {detail}"
-            ) from exc
-        except (URLError, TimeoutError) as exc:
-            raise LMStudioError(
-                f"Could not reach LM Studio at {self.api_base_url}: {exc}"
-            ) from exc
-
-        if not response_body:
-            return {}
-        try:
-            result = json.loads(response_body)
-        except json.JSONDecodeError as exc:
-            raise LMStudioError(
-                f"LM Studio {method} {path} returned invalid JSON"
-            ) from exc
-        if not isinstance(result, dict):
-            raise LMStudioError(
-                f"LM Studio {method} {path} returned an unexpected response"
-            )
-        return result

src/photools3/metadata_writer.py

@@ -1,180 +0,0 @@
-import logging
-import shutil
-from pathlib import Path
-from typing import Annotated, Any
-
-import exiftool
-from pydantic import StringConstraints
-
-from photools3.settings import settings
-
-log = logging.getLogger(__name__)
-
-_EXIFTOOL_MISSING_MSG = (
-    "exiftool binary not found in PATH. "
-    "Install the system package: 'perl-image-exiftool' (Arch) or 'libimage-exiftool-perl' (Debian/Ubuntu)."
-)
-if shutil.which("exiftool") is None:
-    raise FileNotFoundError(_EXIFTOOL_MISSING_MSG)
-
-XmpKey = Annotated[str, StringConstraints(pattern=r"^\w+\:\w+")]
-
-type Caption = str
-type Keywords = list[str]
-type Color = int
-type XmpMetadata = dict[str, Any]
-
-XMP_KEYS_CAPTION: list[XmpKey] = [
-    "XMP:Description",
-    "EXIF:ImageDescription",
-    "IPTC:Caption-Abstract",
-]
-
-XMP_KEYS_KEYWORDS: list[XmpKey] = [
-    "XMP:HierarchicalSubject",
-    "XMP:Subject",
-    "IPTC:Keywords",
-]
-
-
-def _prefixed(txt: str) -> str:
-    return f"{settings.metadata_prefix}{txt}"
-
-
-def _is_prefixed(txt: str) -> bool:
-    return txt.startswith(settings.metadata_prefix)
-
-
-def _separate_prefixed_caption(txt: str) -> tuple[str, str | None]:
-    separated = [x.strip() for x in txt.split(settings.metadata_prefix)]
-    return (separated[0], separated[1] if len(separated) > 1 else None)
-
-
-class MetadataWriter:
-    """
-    Write metadata (captions and keywords) to image files.
-    For DNG files, creates/updates XMP sidecar files.
-    """
-
-    def __init__(
-        self,
-        path: Path,
-        force_sidecar: bool = False,
-        append: bool = True,
-    ):
-        self.path: Path = path
-        self.force_sidecar: bool = force_sidecar
-        self.append: bool = append
-
-        self._metadata: XmpMetadata | None = None
-
-    @property
-    def sidecar_path(self) -> Path:
-        return Path(str(self.path) + ".xmp")
-
-    @property
-    def metadata(self) -> Any:
-        path = self.sidecar_path if self.sidecar_path.exists() else self.path
-        if not path.exists():
-            log.warning(f"File not found: {path}")
-            return None
-        try:
-            with exiftool.ExifToolHelper() as exif:
-                metadata = exif.get_metadata([path])[0]
-        except Exception as exc:  # noqa: BLE001
-            log.error(f"Could not read metadata from {path}: {exc}")
-            return None
-        return metadata
-
-    def get_caption(self) -> str:
-        result = (self.metadata or {}).get(XMP_KEYS_CAPTION[0], "")
-        assert isinstance(result, str)
-        return result
-
-    def get_keywords(self) -> list[str]:
-        result = (self.metadata or {}).get(XMP_KEYS_KEYWORDS[0], []) or []
-        if isinstance(result, str):
-            result = [result]
-        assert isinstance(result, list)
-        return result
-
-    def set_metadata(self, metadata_tags: dict[str, Any]):
-        try:
-            with exiftool.ExifToolHelper() as exif:
-                if not self.sidecar_path.exists():
-                    exif.execute("-o", str(self.sidecar_path), str(self.path))
-                exif.set_tags(
-                    [str(self.sidecar_path)],
-                    tags=metadata_tags,
-                    params=["-overwrite_original"],
-                )
-        except Exception as exc:
-            log.error(f"Could not write metadata to file {self.sidecar_path}: {exc}")
-            raise RuntimeError(
-                f"Could not write metadata to file {self.sidecar_path}"
-            ) from exc
-
-    def write_caption(
-        self, caption: Caption, include_prefix: bool = True, override: bool = True
-    ):
-        existing_no_prefix, existing_prefix = _separate_prefixed_caption(
-            self.get_caption()
-        )
-        if existing_no_prefix:
-            existing_no_prefix = (
-                f"{existing_no_prefix}."
-                if not existing_no_prefix.endswith(".")
-                else existing_no_prefix
-            )
-            existing_no_prefix = f"{existing_no_prefix} "
-
-        if include_prefix and existing_prefix and not override:
-            return
-        if not include_prefix and existing_no_prefix and not override:
-            return
-        if include_prefix:
-            caption = f"{existing_no_prefix}{_prefixed(caption)}"
-        metadata_tags = {key: caption for key in XMP_KEYS_CAPTION}
-        self.set_metadata(metadata_tags)
-
-    def write_keywords(self, keywords: Keywords, include_prefix: bool = True):
-        if include_prefix:
-            keywords = list(map(_prefixed, keywords))
-        existing = self.get_keywords()
-        keywords = list(set(existing + keywords))
-        metadata_tags = {tag: keywords for tag in XMP_KEYS_KEYWORDS}
-        self.set_metadata(metadata_tags)
-
-    def write_color(self, color: Color):
-        self.set_metadata({"XMP-darktable:Colorlabels": color})
-
-    def write(
-        self,
-        caption: Caption | None = None,
-        keywords: Keywords | None = None,
-        color: Color | None = None,
-    ):
-        metadata_tags: XmpMetadata = {}
-        if caption is not None:
-            existing_no_prefix, _ = _separate_prefixed_caption(self.get_caption())
-            if existing_no_prefix:
-                existing_no_prefix = (
-                    f"{existing_no_prefix}."
-                    if not existing_no_prefix.endswith(".")
-                    else existing_no_prefix
-                )
-                existing_no_prefix = f"{existing_no_prefix} "
-            caption = f"{existing_no_prefix}{_prefixed(caption)}"
-            metadata_tags.update({key: caption for key in XMP_KEYS_CAPTION})
-
-        if keywords is not None:
-            keywords = list(map(_prefixed, keywords))
-            existing = self.get_keywords()
-            keywords = list(set(existing + keywords))
-            metadata_tags.update({tag: keywords for tag in XMP_KEYS_KEYWORDS})
-
-        if color is not None:
-            metadata_tags["XMP-darktable:Colorlabels"] = color
-
-        if metadata_tags:
-            self.set_metadata(metadata_tags)

src/photools3/models.py

@@ -1,79 +1,23 @@
-import json
+from typing import Literal
-from enum import StrEnum
-from functools import lru_cache
-from typing import Annotated, Any, Literal
 
-from pydantic import (
+from pydantic import BaseModel, model_validator
-    BaseModel,
-    BeforeValidator,
-    Field,
-    field_validator,
-    model_validator,
-)
-
-from .settings import KEYWORDS_LIST_PATH, settings
-
-
-@lru_cache
-def load_valid_keywords() -> set[str]:
-    """Loads all valid keywords from the configuration file."""
-    if not KEYWORDS_LIST_PATH.exists():
-        return set()
-
-    keywords = set()
-    with open(KEYWORDS_LIST_PATH, "r", encoding="utf-8") as f:
-        for line in f:
-            line = line.strip()
-            if not line:
-                continue
-            # Each line can contain hierarchical tags separated by '|'
-            # e.g., 'architektur|gebäude' means both 'architektur' and 'gebäude' are valid.
-            parts = line.split("|")
-            current_path = ""
-            for part in parts:
-                if current_path:
-                    current_path += "|" + part
-                else:
-                    current_path = part
-                keywords.add(current_path)
-    return keywords
 
 
 class LlmRequest(BaseModel):
-    model: str = "gpt-4o"
+    model_id: int
     messages: list[LlmRequestMessage]
     temperature: float | None = None
-    max_tokens: int
-
-
-class LlmMessageRole(StrEnum):
-    system = "system"
-    user = "user"
 
 
 class LlmRequestMessage(BaseModel):
-    role: LlmMessageRole
+    role: Literal["system", "user"]
-    content: list[LlmMessageContent] | str
+    content: list[LlmMessageContent]
-
-
-def transform_to_image_dict(v: Any) -> Any:
-    if isinstance(v, str):
-        return {"url": f"data:image/png;base64,{v}"}
-    return v
-
-
-Base64ImageUrl = Annotated[dict[str, str], BeforeValidator(transform_to_image_dict)]
-
-
-class LlmMessageType(StrEnum):
-    text = "text"
-    image_url = "image_url"
 
 
 class LlmMessageContent(BaseModel):
-    type: LlmMessageType
+    type: Literal["text", "image_url"]
     text: str | None = None
-    image_url: Base64ImageUrl | None = None
+    image_url: str | None = None
 
     @model_validator(mode="after")
     def _type_matches_content(self):
@@ -82,100 +26,3 @@ class LlmMessageContent(BaseModel):
         if self.type == "image_url" and self.image_url is None:
             raise ValueError("image_url must be set when type is 'image_url'")
         return self
-
-
-class LlmResponseGeneral(BaseModel):
-    text: str
-
-
-class LlmResponseCaption(BaseModel):
-    caption: Annotated[
-        str,
-        Field(
-            description=(
-                "A brief but precise description of what the picture shows. "
-                "No interpretation; just what is visible"
-            )
-        ),
-    ]
-
-
-def _keyword_tags_field():
-    allowed_keywords = json.dumps(
-        sorted(load_valid_keywords()), ensure_ascii=False, separators=(",", ":")
-    )
-    return Field(
-        max_length=settings.llm_max_keywords,
-        description=(
-            f"Choose at most {settings.llm_max_keywords} distinct, relevant keywords. "
-            "Do not enumerate the vocabulary. Allowed keywords: "
-            f"{allowed_keywords}"
-        ),
-    )
-
-
-class _LlmResponseWithTags(BaseModel):
-    tags: list[str] = _keyword_tags_field()
-
-    @field_validator("tags")
-    @classmethod
-    def validate_tags(cls, v: list[str]) -> list[str]:
-        valid_keywords = load_valid_keywords()
-        for tag in v:
-            if tag not in valid_keywords:
-                raise ValueError(
-                    f"'{tag}' is not a valid keyword. Must be one from the configuration."
-                )
-        return v
-
-
-class LlmResponseKeywords(_LlmResponseWithTags):
-    pass
-
-
-class LlmResponseCaptionsAndKeywords(_LlmResponseWithTags):
-    caption: Annotated[
-        str,
-        Field(
-            description=(
-                "A brief but precise description of what the picture shows. "
-                "No interpretation; just what is visible"
-            )
-        ),
-    ]
-
-
-class LlmResponseQuality(BaseModel):
-    rating: Annotated[
-        Literal["ok", "review", "not_ok"],
-        Field(
-            description=(
-                "General quality assessment of the photo. "
-                'Helping to decide whether to keep ("ok"), '
-                'to consider sorting out ("review"), '
-                'or to almost certainly sort out ("not_ok")'
-            )
-        ),
-    ]
-    reason: (
-        None
-        | Annotated[
-            Literal["blur", "exposure", "style"],
-            Field(
-                description=(
-                    'Possible reasons for a lower-than-"ok" assessment. '
-                    '"blur": Obviously mis-focussed or motion-blurred. '
-                    '"exposure": severly under- or over-exposed. '
-                    '"style": photo has severe style issues (beyond personal taste).'
-                )
-            ),
-        ]
-    )
-
-    @model_validator(mode="after")
-    def _not_ok_needs_reason(self):
-        if self.rating != "ok" and self.reason is None:
-            raise ValueError("lower-than-ok rating needs a reason")
-        elif self.rating == "ok" and self.reason is not None:
-            raise ValueError("ok rating needs no reason")
-        return self

src/photools3/photo.py

@@ -1,49 +1,18 @@
-import base64
+
-from io import BytesIO
 from pathlib import Path
-from typing import Self
-
-from PIL import Image
-
-from photools3.models import transform_to_image_dict
-
-
-def _exist_guard(path: Path):
-    pass
 
 
 class Photo:
 
-    def __init__(self, path: Path):
+    def __init__(self):
-        self.path = path
+        ...
-        self.image = Image.open(path)
 
-    def close(self):
+    @classmethod
-        self.image.close()
+    def from_file(cls, path: Path) -> Photo:
+        # TODO
+        return cls()
 
-    def __enter__(self) -> Self:
+    def resize(self) -> Photo:
+        # TODO
         return self
 
-    def __exit__(self, exc_type, exc_value, traceback):
-        self.close()
-
-    def resize(self, image_max_size=1024) -> Photo:
-        """Limiting max dimensions while maintaining aspect ratio"""
-        self.image.thumbnail((image_max_size, image_max_size), Image.Resampling.LANCZOS)
-        return self
-
-    def to_rgb(self):
-        if self.image.mode != "RGB":
-            original = self.image
-            self.image = original.convert("RGB")
-            original.close()
-        return self
-
-    def get_encoded(self):
-        buffer = BytesIO()
-        self.image.save(buffer, format="JPEG", quality=85)
-        image_data = base64.b64encode(buffer.getvalue()).decode("utf-8")
-        return image_data
-
-    def to_llm_image_url(self):
-        return transform_to_image_dict(self.resize().to_rgb().get_encoded())

src/photools3/processing.py

@@ -1,138 +0,0 @@
-import logging
-import time
-from collections.abc import Iterable
-from dataclasses import dataclass
-from enum import StrEnum
-from pathlib import Path
-
-from photools3.llm_session import (
-    LlmSession,
-    LlmSessionCaption,
-    LlmSessionCaptionsAndKeywords,
-    LlmSessionKeywords,
-)
-from photools3.metadata_writer import MetadataWriter
-from photools3.models import (
-    LlmResponseCaption,
-    LlmResponseCaptionsAndKeywords,
-    LlmResponseKeywords,
-)
-from photools3.photo import Photo
-
-logger = logging.getLogger(__name__)
-
-type ProcessingResponse = (
-    LlmResponseCaption | LlmResponseKeywords | LlmResponseCaptionsAndKeywords
-)
-
-
-class ProcessingMode(StrEnum):
-    KEYWORDS = "keywords"
-    CAPTIONS = "captions"
-    BOTH = "both"
-
-
-@dataclass(frozen=True)
-class ProcessingOutcome:
-    path: Path
-    result: ProcessingResponse | None = None
-    error: str | None = None
-
-    @property
-    def succeeded(self) -> bool:
-        return self.error is None and self.result is not None
-
-
-def _create_session(mode: ProcessingMode) -> LlmSession:
-    if mode == ProcessingMode.KEYWORDS:
-        return LlmSessionKeywords()
-    if mode == ProcessingMode.CAPTIONS:
-        return LlmSessionCaption()
-    return LlmSessionCaptionsAndKeywords()
-
-
-def process_photo(
-    path: Path | str,
-    mode: ProcessingMode | str = ProcessingMode.BOTH,
-    *,
-    session: LlmSession | None = None,
-) -> ProcessingResponse:
-    """Process one photo and write the requested LLM result to its metadata.
-
-    If a session is supplied, its lifecycle remains the caller's responsibility.
-    """
-    path = Path(path)
-    mode = ProcessingMode(mode)
-    owns_session = session is None
-    active_session = session if session is not None else _create_session(mode)
-
-    logger.info(f"Processing photo: {path!s}")
-
-    try:
-        with Photo(path) as photo:
-            result = active_session.call([photo])
-
-        caption: str | None = None
-        keywords: list[str] | None = None
-        if mode == ProcessingMode.CAPTIONS:
-            if not isinstance(result, LlmResponseCaption):
-                raise TypeError("Caption mode returned an unexpected response type")
-            caption = result.caption
-        elif mode == ProcessingMode.KEYWORDS:
-            if not isinstance(result, LlmResponseKeywords):
-                raise TypeError("Keywords mode returned an unexpected response type")
-            keywords = result.tags
-        else:
-            if not isinstance(result, LlmResponseCaptionsAndKeywords):
-                raise TypeError("Both mode returned an unexpected response type")
-            caption = result.caption
-            keywords = result.tags
-
-        MetadataWriter(path).write(caption=caption, keywords=keywords)
-        return result
-    finally:
-        if owns_session:
-            active_session.close()
-
-
-def process_batch(
-    paths: Iterable[Path | str],
-    mode: ProcessingMode | str = ProcessingMode.BOTH,
-) -> list[ProcessingOutcome]:
-    """Process photos sequentially, reusing one LLM client for the whole batch.
-
-    A failed photo is recorded in its outcome and does not stop later photos.
-    Only one decoded image is held at a time.
-    """
-    mode = ProcessingMode(mode)
-    session = _create_session(mode)
-    outcomes: list[ProcessingOutcome] = []
-
-    try:
-        for item in paths:
-            path = Path(item)
-            photo_started = time.perf_counter()
-            try:
-                result = process_photo(path, mode, session=session)
-            except Exception as exc:
-                logger.exception("Failed to process %s", path)
-                # Keep only a string, not the exception/traceback, which can
-                # retain references to the just-processed image and stack.
-                outcomes.append(
-                    ProcessingOutcome(
-                        path=path,
-                        error=f"{type(exc).__name__}: {exc}",
-                    )
-                )
-            else:
-                outcomes.append(ProcessingOutcome(path=path, result=result))
-            finally:
-                logger.debug(
-                    "Photo processing for %s took %.2f seconds",
-                    path,
-                    time.perf_counter() - photo_started,
-                )
-    finally:
-        session.close()
-
-    return outcomes

src/photools3/settings.py

@@ -1,31 +1,9 @@
-from pathlib import Path
-
-from pydantic import Field
 from pydantic_core import Url
 from pydantic_settings import BaseSettings, SettingsConfigDict
 
-PHOTOOLS3_DIR = Path(__file__).parent.resolve()
-SRC_DIR = PHOTOOLS3_DIR.parent
-ROOT_DIR = SRC_DIR.parent
-EXAMPLES_DIR = ROOT_DIR / "examples"
-DNG_EXAMPLES_DIR = EXAMPLES_DIR / "dng"
-CONFIG_DIR = ROOT_DIR / "config"
-KEYWORDS_LIST_PATH = CONFIG_DIR / "keywords.txt"
-
 
 class Settings(BaseSettings):
-    model_config = SettingsConfigDict(env_file=".env", extra="ignore")
+    model_config = SettingsConfigDict(env_file=".env")
 
     llm_api_base_url: Url
     llm_api_key: str
-    llm_default_temperature: float
-    llm_request_timeout_seconds: float = Field(default=10, gt=0)
-    llm_max_completion_tokens: int = Field(default=1024, gt=0)
-    llm_max_retries: int = Field(default=5, ge=0)
-    llm_max_keywords: int = Field(default=15, ge=1)
-    log_level: str = "INFO"
-
-    metadata_prefix: str
-
-
-settings = Settings()  # type: ignore[call-arg]

test/conftest.py

@@ -1,22 +0,0 @@
-import pytest
-
-from photools3.llm_session import LlmSession
-from photools3.photo import Photo
-from test.test_photo import PATHS_DNG_EXAMPLES
-
-
-@pytest.fixture
-def test_photo():
-    return Photo(PATHS_DNG_EXAMPLES[0])
-
-
-@pytest.fixture
-def llm_session():
-    return LlmSession(system_prompt="You are a helpful assistant.")
-
-
-@pytest.fixture
-def llm_session_captions_and_keywords():
-    from photools3.llm_session import LlmSessionCaptionsAndKeywords
-
-    return LlmSessionCaptionsAndKeywords()

test/test_cli.py

@@ -1,198 +0,0 @@
-from pathlib import Path
-from types import SimpleNamespace
-from unittest.mock import patch
-
-from typer.testing import CliRunner
-
-from photools3.cli import app
-from photools3.processing import ProcessingMode
-
-runner = CliRunner()
-
-
-def _success_outcome(path: Path):
-    return SimpleNamespace(path=path, succeeded=True, error=None)
-
-
-def test_metadata_magic_command_defaults_to_both_for_single_photo(tmp_path):
-    image_path = tmp_path / "photo.jpg"
-    image_path.write_bytes(b"test image")
-
-    with (
-        patch("photools3.cli.process_photo") as process_photo,
-        patch("photools3.cli.process_batch") as process_batch,
-    ):
-        result = runner.invoke(app, ["metadata-magic", str(image_path)])
-
-    assert result.exit_code == 0
-    process_photo.assert_called_once_with(image_path, ProcessingMode.BOTH)
-    process_batch.assert_not_called()
-
-
-def test_metadata_magic_command_selects_caption_mode(tmp_path):
-    image_path = tmp_path / "photo.jpg"
-    image_path.write_bytes(b"test image")
-
-    with patch("photools3.cli.process_photo") as process_photo:
-        result = runner.invoke(
-            app, ["metadata-magic", str(image_path), "--mode", "captions"]
-        )
-
-    assert result.exit_code == 0
-    process_photo.assert_called_once_with(image_path, ProcessingMode.CAPTIONS)
-
-
-def test_metadata_magic_command_batches_multiple_files(tmp_path):
-    paths = [tmp_path / "one.jpg", tmp_path / "two.jpg"]
-    for path in paths:
-        path.write_bytes(b"test image")
-
-    with (
-        patch("photools3.cli.process_photo") as process_photo,
-        patch("photools3.cli.process_batch") as process_batch,
-    ):
-        process_batch.return_value = [_success_outcome(path) for path in paths]
-        result = runner.invoke(
-            app,
-            ["metadata-magic", *(str(path) for path in paths), "--mode", "keywords"],
-        )
-
-    assert result.exit_code == 0
-    process_photo.assert_not_called()
-    process_batch.assert_called_once_with(paths, ProcessingMode.KEYWORDS)
-
-
-def test_metadata_magic_command_expands_directories_recursively(tmp_path):
-    root = tmp_path / "photos"
-    nested = root / "nested"
-    nested.mkdir(parents=True)
-    top_level_photo = root / "one.jpg"
-    nested_photo = nested / "two.png"
-    sidecar = root / "one.jpg.xmp"
-    for path in (top_level_photo, nested_photo, sidecar):
-        path.write_bytes(b"placeholder")
-
-    with (
-        patch("photools3.cli.process_photo") as process_photo,
-        patch("photools3.cli.process_batch") as process_batch,
-    ):
-        process_batch.return_value = [
-            _success_outcome(nested_photo),
-            _success_outcome(top_level_photo),
-        ]
-        result = runner.invoke(
-            app, ["metadata-magic", str(root), "--recursive", "--mode", "both"]
-        )
-
-    assert result.exit_code == 0
-    process_photo.assert_not_called()
-    process_batch.assert_called_once_with(
-        [nested_photo, top_level_photo], ProcessingMode.BOTH
-    )
-
-
-def test_metadata_magic_command_does_not_recurse_by_default(tmp_path):
-    root = tmp_path / "photos"
-    nested = root / "nested"
-    nested.mkdir(parents=True)
-    top_level_photo = root / "one.jpg"
-    nested_photo = nested / "two.jpg"
-    top_level_photo.write_bytes(b"placeholder")
-    nested_photo.write_bytes(b"placeholder")
-
-    with (
-        patch("photools3.cli.process_photo") as process_photo,
-        patch("photools3.cli.process_batch") as process_batch,
-    ):
-        result = runner.invoke(app, ["metadata-magic", str(root)])
-
-    assert result.exit_code == 0
-    process_photo.assert_called_once_with(top_level_photo, ProcessingMode.BOTH)
-    process_batch.assert_not_called()
-
-
-def test_metadata_magic_command_returns_failure_if_any_batch_item_fails(tmp_path):
-    paths = [tmp_path / "one.jpg", tmp_path / "two.jpg"]
-    for path in paths:
-        path.write_bytes(b"test image")
-
-    with patch("photools3.cli.process_batch") as process_batch:
-        process_batch.return_value = [
-            _success_outcome(paths[0]),
-            SimpleNamespace(
-                path=paths[1], succeeded=False, error="RuntimeError: write failed"
-            ),
-        ]
-        result = runner.invoke(app, ["metadata-magic", *(str(path) for path in paths)])
-
-    assert result.exit_code == 1
-
-
-def test_metadata_magic_command_returns_failure_for_single_photo_error(tmp_path):
-    image_path = tmp_path / "photo.jpg"
-    image_path.write_bytes(b"test image")
-
-    with patch("photools3.cli.process_photo", side_effect=RuntimeError("LLM error")):
-        result = runner.invoke(app, ["metadata-magic", str(image_path)])
-
-    assert result.exit_code == 1
-
-
-def test_keywords_alias_is_not_registered():
-    result = runner.invoke(app, ["keywords", "photo.jpg"])
-
-    assert result.exit_code == 2
-    assert "No such command 'keywords'" in result.output
-
-
-def test_old_process_command_name_is_not_registered():
-    result = runner.invoke(app, ["process", "photo.jpg"])
-
-    assert result.exit_code == 2
-    assert "No such command 'process'" in result.output
-
-
-def test_metadata_magic_command_rejects_empty_directory(tmp_path):
-    with (
-        patch("photools3.cli.process_photo") as process_photo,
-        patch("photools3.cli.process_batch") as process_batch,
-    ):
-        result = runner.invoke(app, ["metadata-magic", str(tmp_path)])
-
-    assert result.exit_code == 1
-    process_photo.assert_not_called()
-    process_batch.assert_not_called()
-
-
-def test_quality_command_closes_photo_and_session(tmp_path):
-    image_path = tmp_path / "photo.jpg"
-    image_path.write_bytes(b"test image")
-
-    with (
-        patch("photools3.cli.LlmSessionQuality") as session_class,
-        patch("photools3.cli.Photo") as photo_class,
-    ):
-        result = runner.invoke(app, ["quality", str(image_path)])
-
-    assert result.exit_code == 0
-    session_class.return_value.__enter__.return_value.call.assert_called_once_with(
-        [photo_class.return_value.__enter__.return_value]
-    )
-    session_class.return_value.__exit__.assert_called_once()
-    photo_class.return_value.__exit__.assert_called_once()
-
-
-def test_quality_command_reports_error_and_exits(tmp_path):
-    image_path = tmp_path / "photo.jpg"
-
-    with patch("photools3.cli.Photo", side_effect=FileNotFoundError(image_path)):
-        result = runner.invoke(app, ["quality", str(image_path)])
-
-    assert result.exit_code == 1
-
-
-def test_version_command():
-    result = runner.invoke(app, ["--version"])
-
-    assert result.exit_code == 0
-    assert "photools3 version 0.1.0" in result.stdout

test/test_llm_session_integration.py

@@ -1,27 +0,0 @@
-import pytest
-
-from photools3.llm_session import LlmSession
-
-
-@pytest.mark.skipif(
-    not LlmSession(system_prompt="").is_api_reachable(), reason="API not available"
-)
-def test_llm_session(llm_session, test_photo):
-    llm_session.system_prompt = "Return only JSON format: {'text': '...'}"
-    result = llm_session.call(
-        prompt="keywords for the attached picture", photos=[test_photo]
-    )
-    assert result is not None
-
-
-@pytest.mark.skipif(
-    not LlmSession(system_prompt="").is_api_reachable(), reason="API not available"
-)
-def test_llm_session_captions_and_keywords(
-    llm_session_captions_and_keywords, test_photo
-):
-    result = llm_session_captions_and_keywords.call(photos=[test_photo])
-    assert result.caption
-    assert result.tags
-    assert isinstance(result.caption, str)
-    assert isinstance(result.tags, list)

test/test_llm_session_mocked.py

@@ -1,303 +0,0 @@
-from unittest.mock import MagicMock, patch
-
-import openai
-import pytest
-
-from photools3.llm_session import LlmSession, LlmSessionCaptionsAndKeywords
-from photools3.models import LlmResponseCaptionsAndKeywords
-
-
-def test_llm_session_mocked(test_photo):
-    # We want to mock OpenAI client's chat.completions.create
-    with patch("photools3.llm_session.OpenAI") as mocked_openai:
-        # Setup the mock response structure
-        mock_client = MagicMock()
-        mocked_openai.return_value = mock_client
-
-        # Mock the models list to return a proper object with an ID string
-        # This prevents Pydantic ValidationError when LlmSession tries to use model_id
-        mock_model = MagicMock()
-        mock_model.id = "gpt-4o"
-        mock_client.models.list.return_value.data = [mock_model]
-
-        # Create a response object: response.choices[0].message.content
-        mock_response = MagicMock()
-        mock_choice = MagicMock()
-        mock_message = MagicMock()
-
-        mock_message.content = '{"text": "some text"}'
-        mock_choice.message = mock_message
-        mock_response.choices = [mock_choice]
-
-        # Configure the client to return our mock response
-        mock_client.chat.completions.create.return_value = mock_response
-
-        # Create a new LlmSession instance that will use the mocked OpenAI client
-        mocked_session = LlmSession(system_prompt="You are a helper.")
-
-        # Run the test
-        result = mocked_session.call(
-            prompt="keywords for the attached picture", photos=[test_photo]
-        )
-
-        # Verify the results
-        assert result.text == "some text"
-
-        # Verify that the mock was enough
-        assert mock_client.chat.completions.create.called
-
-
-def test_llm_session_captions_and_keywords_mocked(test_photo):
-    # We want to mock OpenAI client's chat.completions.create
-    with patch("photools3.llm_session.OpenAI") as mocked_openai:
-        # Setup the mock response structure
-        mock_client = MagicMock()
-        mocked_openai.return_value = mock_client
-
-        # Mock the models list to return a proper object with an ID string
-        # This prevents Pydantic ValidationError when LlmSession tries to use model_id
-        mock_model = MagicMock()
-        mock_model.id = "gpt-4o"
-        mock_client.models.list.return_value.data = [mock_model]
-
-        # Create a response object: response.choices[0].message.content
-        mock_response = MagicMock()
-        mock_choice = MagicMock()
-        mock_message = MagicMock()
-
-        mock_message.content = (
-            '{"caption": "A mocked caption", "tags": ["architektur"]}'
-        )
-        mock_choice.message = mock_message
-        mock_response.choices = [mock_choice]
-
-        # Configure the client to return our mock response
-        mock_client.chat.completions.create.return_value = mock_response
-
-        # Create a new LlmSessionCaptionsAndKeywords instance that will use the mocked OpenAI client
-        from photools3.llm_session import LlmSessionCaptionsAndKeywords
-
-        mocked_session = LlmSessionCaptionsAndKeywords()
-
-        # Run the test
-        result = mocked_session.call(photos=[test_photo])
-
-        # Verify the results
-        assert result.caption == "A mocked caption"
-        assert "architektur" in result.tags
-
-        # Verify that the mock was called
-        assert mock_client.chat.completions.create.called
-
-
-def test_llm_session_retries_from_original_request_without_accumulating_messages():
-    with patch("photools3.llm_session.OpenAI") as mocked_openai:
-        mock_client = MagicMock()
-        mocked_openai.return_value = mock_client
-        mock_model = MagicMock()
-        mock_model.id = "gpt-4o"
-        mock_client.models.list.return_value.data = [mock_model]
-
-        def response(content):
-            message = MagicMock()
-            message.content = content
-            choice = MagicMock()
-            choice.message = message
-            result = MagicMock()
-            result.choices = [choice]
-            return result
-
-        mock_client.chat.completions.create.side_effect = [
-            response('{"wrong": "first"}'),
-            response('{"wrong": "second"}'),
-            response('{"text": "correct"}'),
-        ]
-
-        session = LlmSession(system_prompt="You are a helper.")
-        result = session.call(photos=[], prompt="Return JSON")
-
-    assert result.text == "correct"
-    requests = mock_client.chat.completions.create.call_args_list
-    messages_per_attempt = [call.kwargs["messages"] for call in requests]
-    assert [len(messages) for messages in messages_per_attempt] == [2, 3, 3]
-    final_correction = messages_per_attempt[2][-1]["content"]
-    assert "Field required" in final_correction
-    assert '"wrong"' not in final_correction
-
-
-def test_llm_session_uses_configured_retry_count(monkeypatch):
-    monkeypatch.setattr("photools3.llm_session.settings.llm_max_retries", 1)
-
-    with patch("photools3.llm_session.OpenAI") as mocked_openai:
-        mock_client = MagicMock()
-        mocked_openai.return_value = mock_client
-        mock_model = MagicMock()
-        mock_model.id = "gpt-4o"
-        mock_client.models.list.return_value.data = [mock_model]
-
-        def response(content):
-            message = MagicMock()
-            message.content = content
-            choice = MagicMock()
-            choice.message = message
-            result = MagicMock()
-            result.choices = [choice]
-            return result
-
-        mock_client.chat.completions.create.side_effect = [
-            response('{"wrong": "first"}'),
-            response('{"wrong": "second"}'),
-            response('{"text": "would be a third call"}'),
-        ]
-        session = LlmSession(system_prompt="You are a helper.")
-
-        with (
-            patch.object(session, "flush_model") as flush_model,
-            pytest.raises(ValueError, match="after 1 retries"),
-        ):
-            session.call(photos=[])
-
-    assert mock_client.chat.completions.create.call_count == 2
-    flush_model.assert_called_once()
-
-
-def test_llm_session_reloads_after_timeout_and_retries(monkeypatch):
-    monkeypatch.setattr(
-        "photools3.llm_session.settings.llm_request_timeout_seconds", 10
-    )
-    monkeypatch.setattr("photools3.llm_session.settings.llm_max_completion_tokens", 768)
-    monkeypatch.setattr("photools3.llm_session.settings.llm_max_retries", 1)
-
-    with patch("photools3.llm_session.OpenAI") as mocked_openai:
-        mock_client = MagicMock()
-        mocked_openai.return_value = mock_client
-        mock_model = MagicMock()
-        mock_model.id = "gpt-4o"
-        mock_client.models.list.return_value.data = [mock_model]
-
-        response_message = MagicMock(content='{"text": "recovered"}')
-        response_choice = MagicMock(message=response_message)
-        success_response = MagicMock(choices=[response_choice])
-        mock_client.chat.completions.create.side_effect = [
-            openai.APITimeoutError(request=MagicMock()),
-            success_response,
-        ]
-
-        session = LlmSession(system_prompt="You are a helper.")
-        with patch.object(session, "flush_model") as flush_model:
-            result = session.call(photos=[])
-
-    assert result.text == "recovered"
-    flush_model.assert_called_once()
-    calls = mock_client.chat.completions.create.call_args_list
-    assert len(calls) == 2
-    assert all(call.kwargs["timeout"] == 10 for call in calls)
-    assert all(call.kwargs["max_tokens"] == 768 for call in calls)
-    mocked_openai.assert_called_once_with(
-        base_url=session.base_url,
-        api_key=session.api_key,
-        max_retries=0,
-    )
-
-
-@pytest.mark.parametrize("failure_kind", ["invalid_response", "timeout"])
-def test_llm_session_reloads_when_retry_limit_is_exhausted(monkeypatch, failure_kind):
-    monkeypatch.setattr("photools3.llm_session.settings.llm_max_retries", 0)
-
-    with patch("photools3.llm_session.OpenAI") as mocked_openai:
-        mock_client = MagicMock()
-        mocked_openai.return_value = mock_client
-        mock_model = MagicMock()
-        mock_model.id = "gpt-4o"
-        mock_client.models.list.return_value.data = [mock_model]
-
-        if failure_kind == "timeout":
-            mock_client.chat.completions.create.side_effect = openai.APITimeoutError(
-                request=MagicMock()
-            )
-        else:
-            response_message = MagicMock(content='{"wrong": "shape"}')
-            response_choice = MagicMock(message=response_message)
-            mock_client.chat.completions.create.return_value = MagicMock(
-                choices=[response_choice]
-            )
-
-        session = LlmSession(system_prompt="You are a helper.")
-        with (
-            patch.object(session, "flush_model") as flush_model,
-            pytest.raises(ValueError),
-        ):
-            session.call(photos=[])
-
-    flush_model.assert_called_once()
-    assert mock_client.chat.completions.create.call_count == 1
-
-
-def test_invalid_keywords_get_one_correction_then_invalid_tags_are_dropped(monkeypatch):
-    monkeypatch.setattr("photools3.llm_session.settings.llm_max_keywords", 3)
-    monkeypatch.setattr("photools3.llm_session.settings.llm_max_retries", 5)
-
-    with patch("photools3.llm_session.OpenAI") as mocked_openai:
-        mock_client = MagicMock()
-        mocked_openai.return_value = mock_client
-        mock_model = MagicMock()
-        mock_model.id = "gpt-4o"
-        mock_client.models.list.return_value.data = [mock_model]
-
-        def response(content):
-            message = MagicMock(content=content)
-            choice = MagicMock(message=message)
-            return MagicMock(choices=[choice])
-
-        mock_client.chat.completions.create.side_effect = [
-            response(
-                '{"caption": "A caption", "tags": ["not_allowed", "architektur"]}'
-            ),
-            response(
-                '{"caption": "A caption", '
-                '"tags": ["still_invalid", "architektur", "personen"]}'
-            ),
-        ]
-        session = LlmSessionCaptionsAndKeywords()
-        with patch.object(session, "flush_model") as flush_model:
-            result = session.call(photos=[])
-
-    assert result.tags == ["architektur", "personen"]
-    assert mock_client.chat.completions.create.call_count == 2
-    flush_model.assert_not_called()
-    correction = mock_client.chat.completions.create.call_args_list[1].kwargs[
-        "messages"
-    ][-1]["content"]
-    assert "not_allowed" in correction
-    assert '"caption"' not in correction
-
-
-def test_excess_keywords_are_truncated_to_configured_limit(monkeypatch):
-    monkeypatch.setattr("photools3.llm_session.settings.llm_max_keywords", 2)
-
-    with patch("photools3.llm_session.OpenAI") as mocked_openai:
-        mock_client = MagicMock()
-        mocked_openai.return_value = mock_client
-        mock_model = MagicMock()
-        mock_model.id = "gpt-4o"
-        mock_client.models.list.return_value.data = [mock_model]
-        message = MagicMock(
-            content=(
-                '{"caption": "A caption", '
-                '"tags": ["architektur", "personen", "landschaft"]}'
-            )
-        )
-        mock_client.chat.completions.create.return_value = MagicMock(
-            choices=[MagicMock(message=message)]
-        )
-
-        result = LlmSessionCaptionsAndKeywords().call(photos=[])
-
-    assert result.tags == ["architektur", "personen"]
-    assert mock_client.chat.completions.create.call_count == 1
-
-
-def test_keyword_response_schema_includes_configured_max_items():
-    schema = LlmResponseCaptionsAndKeywords.model_json_schema()
-
-    assert schema["properties"]["tags"]["maxItems"] == 15

test/test_lm_studio.py

@@ -1,102 +0,0 @@
-import json
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from photools3.lm_studio import LMStudioError, LMStudioModelManager
-
-
-def _http_response(payload: dict):
-    response = MagicMock()
-    response.__enter__.return_value = response
-    response.read.return_value = json.dumps(payload).encode("utf-8")
-    return response
-
-
-def test_reload_model_unloads_and_reloads_preserving_context_config():
-    model_response = {
-        "models": [
-            {
-                "key": "qwen/qwen3-vl-4b",
-                "loaded_instances": [
-                    {
-                        "id": "qwen/qwen3-vl-4b-instance",
-                        "config": {
-                            "context_length": 16384,
-                            "eval_batch_size": 256,
-                            "flash_attention": True,
-                            "offload_kv_cache_to_gpu": False,
-                            "parallel": 2,
-                        },
-                    }
-                ],
-            }
-        ]
-    }
-    with patch(
-        "photools3.lm_studio.urlopen",
-        side_effect=[
-            _http_response(model_response),
-            _http_response({"instance_id": "qwen/qwen3-vl-4b-instance"}),
-            _http_response({"instance_id": "qwen/qwen3-vl-4b-instance"}),
-        ],
-    ) as mocked_urlopen:
-        manager = LMStudioModelManager(
-            "http://localhost:1234/v1", "test-token", timeout=12
-        )
-        manager.reload_model("qwen/qwen3-vl-4b")
-
-    requests = [call.args[0] for call in mocked_urlopen.call_args_list]
-    assert requests[0].full_url == "http://localhost:1234/api/v1/models"
-    assert requests[0].get_method() == "GET"
-    assert requests[0].get_header("Authorization") == "Bearer test-token"
-
-    assert requests[1].full_url.endswith("/models/unload")
-    assert json.loads(requests[1].data) == {"instance_id": "qwen/qwen3-vl-4b-instance"}
-
-    assert requests[2].full_url.endswith("/models/load")
-    assert json.loads(requests[2].data) == {
-        "model": "qwen/qwen3-vl-4b",
-        "context_length": 16384,
-        "eval_batch_size": 256,
-        "flash_attention": True,
-        "offload_kv_cache_to_gpu": False,
-    }
-
-
-def test_reload_model_loads_by_key_if_no_instance_is_loaded():
-    model_response = {"models": [{"key": "qwen/model", "loaded_instances": []}]}
-    with patch(
-        "photools3.lm_studio.urlopen",
-        side_effect=[
-            _http_response(model_response),
-            _http_response({"status": "loaded"}),
-        ],
-    ) as mocked_urlopen:
-        manager = LMStudioModelManager("http://localhost:1234/v1", "token")
-        manager.reload_model("qwen/model")
-
-    assert mocked_urlopen.call_count == 2
-    load_request = mocked_urlopen.call_args.args[0]
-    assert load_request.full_url.endswith("/models/load")
-    assert json.loads(load_request.data) == {"model": "qwen/model"}
-
-
-def test_reload_model_rejects_ambiguous_model_selection():
-    model_response = {
-        "models": [
-            {
-                "key": "qwen/model",
-                "loaded_instances": [
-                    {"id": "instance-1", "config": {}},
-                    {"id": "instance-2", "config": {}},
-                ],
-            }
-        ]
-    }
-    with patch(
-        "photools3.lm_studio.urlopen", return_value=_http_response(model_response)
-    ):
-        manager = LMStudioModelManager("http://localhost:1234/v1", "token")
-        with pytest.raises(LMStudioError, match="Multiple loaded instances"):
-            manager.reload_model("qwen/model")

test/test_metadata_writer.py

@@ -1,243 +0,0 @@
-import shutil
-from pathlib import Path
-from unittest.mock import patch
-
-import pytest
-
-from photools3.metadata_writer import (
-    MetadataWriter,
-    _is_prefixed,
-    _prefixed,
-)
-from photools3.settings import settings
-from test.test_photo import PATHS_DNG_EXAMPLES
-
-PREFIX = settings.metadata_prefix
-
-
-@pytest.fixture
-def make_dng_file(tmp_path):
-    def _make(src: Path) -> Path:
-        dst = tmp_path / f"copy_{src.name}"
-        shutil.copy(src, dst)
-        return dst
-
-    return _make
-
-
-@pytest.fixture
-def metadata_writer_on_existing(make_dng_file):
-    metadata_writer = MetadataWriter(make_dng_file(PATHS_DNG_EXAMPLES[0]))
-    metadata_writer.write_caption(
-        "Let's pretend this is the caption of the original image",
-        include_prefix=False,
-    )
-    return metadata_writer
-
-
-@pytest.mark.parametrize("pic", PATHS_DNG_EXAMPLES)
-def test_read_metadata(make_dng_file, pic):
-    """primitive test for `MetadataWriter`"""
-    file_path = make_dng_file(pic)
-    result = MetadataWriter(file_path)
-    assert result
-    assert result.metadata
-    assert isinstance(result.metadata, dict)
-
-
-@pytest.mark.parametrize(
-    "pic, expected_keywords",
-    [
-        (PATHS_DNG_EXAMPLES[0], ["architektur", "zeit|abend"]),
-        (PATHS_DNG_EXAMPLES[1], ["architektur"]),
-        (PATHS_DNG_EXAMPLES[2], ["morgen", "tiere|vögel"]),
-        (
-            PATHS_DNG_EXAMPLES[3],
-            [
-                "abend",
-                "architektur",
-                "bewegungsunschärfe",
-                "lichter",
-                "mainz",
-                "spiegelung",
-                "wasser",
-            ],
-        ),
-        (
-            PATHS_DNG_EXAMPLES[4],
-            ["grau", "landschaft", "landschaft|weinberge", "personen", "tiefe", "weg"],
-        ),
-    ],
-)
-def test_get_keywords(make_dng_file, pic, expected_keywords):
-    path = make_dng_file(pic)
-    result = MetadataWriter(path).get_keywords()
-    assert sorted(result) == sorted(expected_keywords)
-
-
-def test_prefixed():
-    txt = "bla_bla"
-    txt_prefixed = _prefixed(txt)
-    assert txt_prefixed == f"{PREFIX}bla_bla"
-
-
-@pytest.mark.parametrize(
-    "txt, expected_result",
-    [
-        (f"{PREFIX}some text", True),
-        ("prefix is missing here", False),
-        ("some_perfix|but not the right one", False),
-    ],
-)
-def test_is_prefixed(txt, expected_result):
-    assert _is_prefixed(txt) == expected_result
-
-
-@pytest.mark.parametrize("pic", PATHS_DNG_EXAMPLES)
-def test_read_metadata_sidecar_path(make_dng_file, pic):
-    file_path = make_dng_file(pic)
-    metadata_writer = MetadataWriter(file_path)
-    assert isinstance(metadata_writer.sidecar_path, Path)
-    assert str(metadata_writer.sidecar_path).lower().endswith(".dng.xmp")
-
-    # file does not exits yet, only the name
-    assert not metadata_writer.sidecar_path.exists()
-
-
-@pytest.mark.parametrize("pic", PATHS_DNG_EXAMPLES)
-def test_set_caption(make_dng_file, pic):
-    metadata_writer = MetadataWriter(make_dng_file(pic))
-    assert not metadata_writer.sidecar_path.exists()  # sidecar does not exist yet ...
-    metadata_writer.write_caption("This is a test")
-    assert metadata_writer.sidecar_path.exists()  # ... but now!
-    result = metadata_writer.get_caption()
-    assert result
-    assert result == f"{PREFIX}This is a test"
-
-
-def test_set_caption_on_existing(metadata_writer_on_existing):
-    assert (
-        metadata_writer_on_existing.get_caption()
-        == "Let's pretend this is the caption of the original image"
-    )
-
-    metadata_writer_on_existing.write_caption("Another caption")
-    assert (
-        metadata_writer_on_existing.get_caption()
-        == f"Let's pretend this is the caption of the original image. {PREFIX}Another caption"
-    )
-
-    metadata_writer_on_existing.write_caption("Updated caption")
-    assert (
-        metadata_writer_on_existing.get_caption()
-        == f"Let's pretend this is the caption of the original image. {PREFIX}Updated caption"
-    )
-
-    metadata_writer_on_existing.write_caption("Don't do this!", override=False)
-    assert (
-        metadata_writer_on_existing.get_caption()
-        == f"Let's pretend this is the caption of the original image. {PREFIX}Updated caption"
-    )
-
-
-@pytest.mark.parametrize(
-    "pic, expected_keywords",
-    [
-        (
-            PATHS_DNG_EXAMPLES[0],
-            [
-                "architektur",
-                "zeit|abend",
-                f"{PREFIX}hund",
-                f"{PREFIX}katze",
-                f"{PREFIX}maus",
-            ],
-        ),
-        (
-            PATHS_DNG_EXAMPLES[1],
-            ["architektur", f"{PREFIX}hund", f"{PREFIX}katze", f"{PREFIX}maus"],
-        ),
-        (
-            PATHS_DNG_EXAMPLES[2],
-            [
-                "morgen",
-                "tiere|vögel",
-                f"{PREFIX}hund",
-                f"{PREFIX}katze",
-                f"{PREFIX}maus",
-            ],
-        ),
-        (
-            PATHS_DNG_EXAMPLES[3],
-            [
-                "abend",
-                "architektur",
-                "bewegungsunschärfe",
-                "lichter",
-                "mainz",
-                "spiegelung",
-                "wasser",
-                f"{PREFIX}hund",
-                f"{PREFIX}katze",
-                f"{PREFIX}maus",
-            ],
-        ),
-        (
-            PATHS_DNG_EXAMPLES[4],
-            [
-                "grau",
-                "landschaft",
-                "landschaft|weinberge",
-                "personen",
-                "tiefe",
-                "weg",
-                f"{PREFIX}hund",
-                f"{PREFIX}katze",
-                f"{PREFIX}maus",
-            ],
-        ),
-    ],
-)
-def test_set_keywords(make_dng_file, pic, expected_keywords):
-    metadata_writer = MetadataWriter(make_dng_file(pic))
-    metadata_writer.write_keywords(["hund", "katze", "maus"])
-    result = metadata_writer.get_keywords()
-    assert result
-    assert isinstance(result, list)
-    assert f"{PREFIX}hund" in result
-    assert f"{PREFIX}katze" in result
-    assert f"{PREFIX}maus" in result
-
-
-def test_set_keywords_on_existing():
-    # TODO
-    ...
-
-
-def test_write_caption_and_keywords_in_one_metadata_update(tmp_path):
-    writer = MetadataWriter(tmp_path / "photo.jpg")
-    with (
-        patch.object(writer, "get_caption", return_value="Original caption"),
-        patch.object(writer, "get_keywords", return_value=["existing"]),
-        patch.object(writer, "set_metadata") as set_metadata,
-    ):
-        writer.write(caption="Generated caption", keywords=["architektur"])
-
-    set_metadata.assert_called_once()
-    metadata_tags = set_metadata.call_args.args[0]
-    assert metadata_tags["XMP:Description"] == (
-        f"Original caption. {PREFIX}Generated caption"
-    )
-    assert set(metadata_tags["XMP:Subject"]) == {
-        "existing",
-        f"{PREFIX}architektur",
-    }
-
-
-def test_set_metadata_surfaces_write_failures(tmp_path):
-    writer = MetadataWriter(tmp_path / "photo.jpg")
-    with patch("photools3.metadata_writer.exiftool.ExifToolHelper") as exif:
-        exif_instance = exif.return_value.__enter__.return_value
-        exif_instance.execute.side_effect = OSError("ExifTool failed")
-        with pytest.raises(RuntimeError, match="Could not write metadata"):
-            writer.set_metadata({"XMP:Description": "caption"})

test/test_photo.py

@@ -1,285 +0,0 @@
-import base64
-from io import BytesIO
-
-import pytest
-from PIL import Image
-
-from photools3.photo import Photo
-from photools3.settings import DNG_EXAMPLES_DIR
-
-PATHS_DNG_EXAMPLES = [
-    DNG_EXAMPLES_DIR / "example_dng_01.dng",
-    DNG_EXAMPLES_DIR / "example_dng_02.dng",
-    DNG_EXAMPLES_DIR / "example_dng_03.dng",
-    DNG_EXAMPLES_DIR / "example_dng_04.dng",
-    DNG_EXAMPLES_DIR / "example_dng_05.dng",
-]
-
-# (PIL mode) -> file format used to persist the test image
-MODE_TO_FORMAT = {
-    "RGB": "PNG",
-    "L": "PNG",
-    "P": "PNG",
-    "RGBA": "PNG",
-    "LA": "PNG",
-    "CMYK": "TIFF",
-    "1": "PNG",
-    "I": "TIFF",
-    "F": "TIFF",
-}
-ALL_MODES = list(MODE_TO_FORMAT)
-JPEG_COMPATIBLE_MODES = ["RGB", "L", "CMYK", "1"]
-JPEG_INCOMPATIBLE_MODES = ["P", "RGBA", "LA", "I", "F"]
-
-
-def _make_image_file(tmp_path, mode: str, size=(64, 48)):
-    """Create an image in the given mode, save it to disk and return its path."""
-    fmt = MODE_TO_FORMAT[mode]
-    img = Image.new(mode, size)
-    path = tmp_path / f"test_{mode}.{fmt.lower()}"
-    img.save(path, format=fmt)
-    return path
-
-
-def test_load_photo():
-    photo = Photo(PATHS_DNG_EXAMPLES[0])
-    assert photo
-    assert photo.path == PATHS_DNG_EXAMPLES[0]
-    assert isinstance(photo.image, Image.Image)
-
-
-def test_photo_context_manager_closes_image(tmp_path):
-    path = _make_image_file(tmp_path, "RGB")
-    with Photo(path) as photo:
-        image = photo.image
-        assert image.fp is not None
-
-    assert image.fp is None
-
-
-def test_resize_method_exists():
-    """Test that resize method exists and returns a Photo instance"""
-    photo = Photo(PATHS_DNG_EXAMPLES[0])
-    resized_photo = photo.resize()
-    assert isinstance(resized_photo, Photo)
-    assert resized_photo is photo  # Should return self for chaining
-
-
-def test_resize_with_custom_max_size():
-    """Test resize with a custom maximum size"""
-    photo = Photo(PATHS_DNG_EXAMPLES[0])
-    original_width, original_height = photo.image.size
-    assert original_width > 100 or original_height > 100  # Assume it's larger
-
-    resized_photo = photo.resize(image_max_size=50)
-
-    new_width, new_height = resized_photo.image.size
-    max_dim = max(new_width, new_height)
-    assert max_dim <= 50, f"Max dimension {max_dim} exceeds limit of 50"
-
-
-def test_resize_maintains_aspect_ratio():
-    """Test that resize maintains the original aspect ratio"""
-    photo = Photo(PATHS_DNG_EXAMPLES[0])
-    original_width, original_height = photo.image.size
-
-    resized_photo = photo.resize(image_max_size=100)
-    new_width, new_height = resized_photo.image.size
-
-    original_ratio = original_width / original_height
-    resized_ratio = new_width / new_height
-
-    assert (
-        abs(original_ratio - resized_ratio) < 0.01
-    ), f"Aspect ratio changed: {original_ratio} -> {resized_ratio}"
-
-
-def test_resize_image_thumbnail_called():
-    """Test that the image was actually resized using thumbnail"""
-    photo = Photo(PATHS_DNG_EXAMPLES[0])
-    original_size = photo.image.size
-
-    # Resize to a smaller dimension
-    resized_photo = photo.resize(image_max_size=10)
-
-    new_size = resized_photo.image.size
-    assert (
-        new_size != original_size
-    ), "Image should be resized when max_size is smaller than original"
-
-
-def test_resize_chaining():
-    """Test that resize returns self for method chaining"""
-    photo = Photo(PATHS_DNG_EXAMPLES[0])
-
-    # Should be able to chain multiple operations
-    result = photo.resize().resize(image_max_size=50)
-    assert result is photo
-
-
-def test_resize_default_parameter():
-    """Test resize with default parameter (no arguments)"""
-    photo = Photo(PATHS_DNG_EXAMPLES[0])
-    resized_photo = photo.resize()
-
-    # Should return a valid Photo instance
-    assert isinstance(resized_photo, Photo)
-    assert resized_photo.path == photo.path
-
-
-# --- to_rgb() ---
-
-
-@pytest.mark.parametrize("mode", ALL_MODES)
-def test_to_rgb_converts_to_rgb_mode(tmp_path, mode):
-    """to_rgb() must leave the image in RGB mode regardless of the initial mode"""
-    path = _make_image_file(tmp_path, mode)
-    photo = Photo(path)
-    result = photo.to_rgb()
-    assert result.image.mode == "RGB"
-
-
-@pytest.mark.parametrize("mode", ALL_MODES)
-def test_to_rgb_returns_self_for_chaining(tmp_path, mode):
-    """to_rgb() returns self so it can be chained"""
-    path = _make_image_file(tmp_path, mode)
-    photo = Photo(path)
-    assert photo.to_rgb() is photo
-
-
-def test_to_rgb_keeps_rgb_image_unmodified(tmp_path):
-    """RGB images are not re-converted: the same image object is kept"""
-    path = _make_image_file(tmp_path, "RGB")
-    photo = Photo(path)
-    original_image = photo.image
-    photo.to_rgb()
-    assert photo.image is original_image
-
-
-@pytest.mark.parametrize("mode", ["L", "P", "RGBA", "LA", "CMYK", "1", "I", "F"])
-def test_to_rgb_replaces_non_rgb_image(tmp_path, mode):
-    """Non-RGB images are converted: a new image object is assigned"""
-    path = _make_image_file(tmp_path, mode)
-    photo = Photo(path)
-    original_image = photo.image
-    photo.to_rgb()
-    assert photo.image is not original_image
-    assert photo.image.mode == "RGB"
-
-
-@pytest.mark.parametrize("mode", ["RGBA", "LA"])
-def test_to_rgb_drops_alpha_channel(tmp_path, mode):
-    """Converting alpha-bearing modes to RGB drops the alpha band"""
-    path = _make_image_file(tmp_path, mode)
-    photo = Photo(path)
-    assert "A" in photo.image.getbands()
-    photo.to_rgb()
-    assert photo.image.getbands() == ("R", "G", "B")
-
-
-def test_to_rgb_on_dng_examples(tmp_path):
-    """DNG examples are already RGB; to_rgb() is a safe no-op on every file"""
-    for path in PATHS_DNG_EXAMPLES:
-        photo = Photo(path)
-        original_image = photo.image
-        assert photo.to_rgb() is photo
-        assert photo.image is original_image
-        assert photo.image.mode == "RGB"
-
-
-# --- get_encoded() ---
-
-
-@pytest.mark.parametrize("mode", JPEG_COMPATIBLE_MODES)
-def test_get_encoded_returns_valid_base64(tmp_path, mode):
-    """get_encoded() returns a non-empty, decodable base64 string"""
-    path = _make_image_file(tmp_path, mode)
-    photo = Photo(path)
-    encoded = photo.get_encoded()
-    assert isinstance(encoded, str)
-    assert len(encoded) > 0
-    raw = base64.b64decode(encoded, validate=True)
-    assert len(raw) > 0
-
-
-@pytest.mark.parametrize("mode", JPEG_COMPATIBLE_MODES)
-def test_get_encoded_produces_openable_jpeg(tmp_path, mode):
-    """The decoded payload must be a valid JPEG with the original dimensions"""
-    path = _make_image_file(tmp_path, mode)
-    photo = Photo(path)
-    original_size = photo.image.size
-    encoded = photo.get_encoded()
-    img = Image.open(BytesIO(base64.b64decode(encoded)))
-    assert img.format == "JPEG"
-    assert img.size == original_size
-
-
-def test_get_encoded_starts_with_jpeg_magic_bytes(tmp_path):
-    """JPEG files start with the FFD8FF magic bytes"""
-    path = _make_image_file(tmp_path, "RGB")
-    photo = Photo(path)
-    raw = base64.b64decode(photo.get_encoded())
-    assert raw[:3] == b"\xff\xd8\xff"
-
-
-def test_get_encoded_reencodes_instead_of_passing_raw_file_bytes(tmp_path):
-    """The output must be a fresh JPEG encoding, not the source file bytes"""
-    path = _make_image_file(tmp_path, "RGB")
-    photo = Photo(path)
-    raw = base64.b64decode(photo.get_encoded())
-    source_bytes = path.read_bytes()
-    assert raw != source_bytes
-    assert source_bytes[:4] == b"\x89PNG"  # source is a PNG...
-    assert raw[:3] == b"\xff\xd8\xff"  # ...but the encoding is a JPEG
-
-
-def test_get_encoded_is_deterministic(tmp_path):
-    """Encoding the same image twice yields identical base64"""
-    path = _make_image_file(tmp_path, "RGB")
-    assert Photo(path).get_encoded() == Photo(path).get_encoded()
-
-
-@pytest.mark.parametrize("mode", JPEG_INCOMPATIBLE_MODES)
-def test_get_encoded_raises_for_non_jpeg_modes(tmp_path, mode):
-    """Modes JPEG cannot store (palette, alpha, integer/float) must raise OSError"""
-    path = _make_image_file(tmp_path, mode)
-    photo = Photo(path)
-    with pytest.raises(OSError):
-        photo.get_encoded()
-
-
-@pytest.mark.parametrize("mode", JPEG_INCOMPATIBLE_MODES)
-def test_to_rgb_then_get_encoded_works_around_unsupported_modes(tmp_path, mode):
-    """Chaining to_rgb().get_encoded() fixes modes that JPEG cannot store"""
-    path = _make_image_file(tmp_path, mode)
-    photo = Photo(path)
-    with pytest.raises(OSError):
-        photo.get_encoded()
-    encoded = photo.to_rgb().get_encoded()
-    assert isinstance(encoded, str)
-    img = Image.open(BytesIO(base64.b64decode(encoded)))
-    assert img.format == "JPEG"
-
-
-@pytest.mark.parametrize("path", PATHS_DNG_EXAMPLES)
-def test_get_encoded_works_on_all_dng_examples(path):
-    """Every DNG example (all different files) encodes to a valid JPEG"""
-    photo = Photo(path)
-    encoded = photo.get_encoded()
-    assert isinstance(encoded, str)
-    raw = base64.b64decode(encoded, validate=True)
-    assert raw[:3] == b"\xff\xd8\xff"
-    img = Image.open(BytesIO(raw))
-    assert img.format == "JPEG"
-    assert img.size == photo.image.size
-
-
-def test_full_chain_resize_to_rgb_get_encoded(tmp_path):
-    """The intended workflow: resize -> to_rgb -> get_encoded"""
-    path = _make_image_file(tmp_path, "RGBA")
-    photo = Photo(path)
-    photo.resize(32).to_rgb()
-    assert photo.image.mode == "RGB"
-    img = Image.open(BytesIO(base64.b64decode(photo.get_encoded())))
-    assert img.format == "JPEG"
-    assert max(img.size) <= 32

test/test_processing.py

@@ -1,129 +0,0 @@
-from pathlib import Path
-from unittest.mock import MagicMock, patch
-
-import pytest
-from PIL import Image
-
-from photools3.models import (
-    LlmResponseCaption,
-    LlmResponseCaptionsAndKeywords,
-    LlmResponseKeywords,
-)
-from photools3.processing import (
-    ProcessingMode,
-    _create_session,
-    process_batch,
-    process_photo,
-)
-
-
-@pytest.fixture
-def image_path(tmp_path):
-    path = tmp_path / "photo.png"
-    Image.new("RGB", (20, 20)).save(path)
-    return path
-
-
-@pytest.mark.parametrize(
-    ("mode", "response", "expected_metadata"),
-    [
-        (
-            ProcessingMode.KEYWORDS,
-            LlmResponseKeywords(tags=["architektur"]),
-            {"caption": None, "keywords": ["architektur"]},
-        ),
-        (
-            ProcessingMode.CAPTIONS,
-            LlmResponseCaption(caption="Ein Haus"),
-            {"caption": "Ein Haus", "keywords": None},
-        ),
-        (
-            ProcessingMode.BOTH,
-            LlmResponseCaptionsAndKeywords(caption="Ein Haus", tags=["architektur"]),
-            {"caption": "Ein Haus", "keywords": ["architektur"]},
-        ),
-    ],
-)
-def test_process_photo_writes_only_requested_metadata(
-    image_path, mode, response, expected_metadata
-):
-    session = MagicMock()
-    session.call.return_value = response
-
-    with (
-        patch("photools3.processing._create_session", return_value=session),
-        patch("photools3.processing.MetadataWriter") as writer_class,
-    ):
-        result = process_photo(image_path, mode)
-
-    assert result == response
-    session.call.assert_called_once()
-    session.close.assert_called_once()
-    writer_class.assert_called_once_with(image_path)
-    writer_class.return_value.write.assert_called_once_with(**expected_metadata)
-
-
-def test_process_photo_does_not_close_a_caller_owned_session(image_path):
-    session = MagicMock()
-    session.call.return_value = LlmResponseCaption(caption="Ein Haus")
-
-    with patch("photools3.processing.MetadataWriter"):
-        process_photo(image_path, ProcessingMode.CAPTIONS, session=session)
-
-    session.close.assert_not_called()
-
-
-@pytest.mark.parametrize(
-    ("mode", "constructor"),
-    [
-        (ProcessingMode.KEYWORDS, "LlmSessionKeywords"),
-        (ProcessingMode.CAPTIONS, "LlmSessionCaption"),
-        (ProcessingMode.BOTH, "LlmSessionCaptionsAndKeywords"),
-    ],
-)
-def test_create_session_selects_mode_specific_response_model(mode, constructor):
-    with patch(f"photools3.processing.{constructor}") as session_class:
-        assert _create_session(mode) is session_class.return_value
-
-    session_class.assert_called_once_with()
-
-
-def test_process_batch_is_sequential_reuses_session_and_continues_after_error():
-    active_photos = 0
-    max_active_photos = 0
-
-    class TrackingPhoto:
-        def __init__(self, path):
-            self.path = path
-
-        def __enter__(self):
-            nonlocal active_photos, max_active_photos
-            active_photos += 1
-            max_active_photos = max(max_active_photos, active_photos)
-            return self
-
-        def __exit__(self, exc_type, exc_value, traceback):
-            nonlocal active_photos
-            active_photos -= 1
-
-    session = MagicMock()
-    response = LlmResponseCaptionsAndKeywords(caption="Ein Haus", tags=["architektur"])
-    session.call.side_effect = [response, RuntimeError("temporary error"), response]
-    paths = [Path("one.jpg"), Path("two.jpg"), Path("three.jpg")]
-
-    with (
-        patch("photools3.processing._create_session", return_value=session),
-        patch("photools3.processing.Photo", TrackingPhoto),
-        patch("photools3.processing.MetadataWriter") as writer_class,
-    ):
-        outcomes = process_batch(paths, ProcessingMode.BOTH)
-
-    assert [outcome.succeeded for outcome in outcomes] == [True, False, True]
-    assert [outcome.path for outcome in outcomes] == paths
-    assert outcomes[1].error == "RuntimeError: temporary error"
-    assert max_active_photos == 1
-    assert active_photos == 0
-    assert session.call.call_count == 3
-    assert all(len(call.args[0]) == 1 for call in session.call.call_args_list)
-    assert writer_class.return_value.write.call_count == 2
-    session.close.assert_called_once()

test/test_settings.py

@@ -1,35 +1,6 @@
-from pathlib import Path
+from photools3.settings import Settings
 
-from photools3.settings import (
+settings = Settings()
-    DNG_EXAMPLES_DIR,
-    EXAMPLES_DIR,
-    PHOTOOLS3_DIR,
-    ROOT_DIR,
-    SRC_DIR,
-    settings,
-)
-
-
-def test_photools3_dir():
-    assert PHOTOOLS3_DIR == Path(__file__).parent.parent.resolve() / "src" / "photools3"
-
-
-def test_src_dir():
-    assert SRC_DIR == Path(__file__).parent.parent.resolve() / "src"
-
-
-def test_root_dir():
-    assert ROOT_DIR == Path(__file__).parent.parent.resolve()
-
-
-def test_examples_dir():
-    assert EXAMPLES_DIR == Path(__file__).parent.parent.resolve() / "examples"
-
-
-def test_dng_examples_dir():
-    assert (
-        DNG_EXAMPLES_DIR == Path(__file__).parent.parent.resolve() / "examples" / "dng"
-    )
 
 
 def test_settings_base():

uv.lock

@@ -1,19 +1,6 @@
 version = 1
-revision = 5
+revision = 3
 requires-python = ">=3.14"
-resolution-markers = [
-    "python_full_version >= '3.15'",
-    "python_full_version < '3.15'",
-]
-
-[[package]]
-name = "annotated-doc"
-version = "0.0.5"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/5a/8e/38aa427ed5402449e226975b649c5dc73ccadfefeb95e6aecb8f8ea4b6b6/annotated_doc-0.0.5.tar.gz", hash = "sha256:c7e58ce09192557605d8bbd92836d7e1d520ac9580096042c0bfd197efacf1bb", size = 10758, upload-time = "2026-07-28T13:50:58.129Z" }
-wheels = [
-    { url = "https://files.pythonhosted.org/packages/3e/30/e900b21425a860e195f32e37657aa1f7c7f2b1bfb26f03ca209b90933c06/annotated_doc-0.0.5-py3-none-any.whl", hash = "sha256:117bac03a25ede5df5440e855b32d556049ca169ead221505badf432fed4b101", size = 5302, upload-time = "2026-07-28T13:50:57.239Z" },
-]
 
 [[package]]
 name = "annotated-types"
@@ -37,235 +24,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/12/b8/4bd346e22b28902df4d651910f5242c28d84e4a5c2435ca5c3f797ed7e2e/anyio-4.15.1-py3-none-any.whl", hash = "sha256:6152fdbbf9a77fdec97731721bebf7c4c44f7c29b424b0065826173efc7ed101", size = 132079, upload-time = "2026-09-05T10:42:37.923Z" },
 ]
 
-[[package]]
-name = "ast-serialize"
-version = "0.12.1"
-source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/c2/1c/7257e6ec9382843915ce475558ce4492ccb5ed39122c256bb369c27e2ebf/ast_serialize-0.12.1.tar.gz", hash = "sha256:5285a390caf1c44368ae270f037f797b91427d138b7d43cad0f1fda4c83518d9", size = 954408, upload-time = "2026-10-03T12:25:00.221Z" }
-wheels = [
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