src/photools3/llm_session.py

@@ -12,8 +12,10 @@ from photools3.models import (
     LlmMessageType,
     LlmRequest,
     LlmRequestMessage,
+    LlmResponseCaption,
     LlmResponseCaptionsAndKeywords,
     LlmResponseGeneral,
+    LlmResponseKeywords,
     LlmResponseQuality,
 )
 from photools3.photo import Photo
@@ -63,6 +65,15 @@ class LlmSession:
             api_key=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 client_chat_completion_create(self, request: LlmRequest, choice_no: int = 0):
         response = self.client.chat.completions.create(**request.model_dump())
         return response.choices[choice_no].message.content
@@ -171,6 +182,38 @@ class LlmSessionCaptionsAndKeywords(LlmSession):
         )
 
 
+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"
+                "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"

src/photools3/metadata_writer.py

@@ -87,12 +87,12 @@ class MetadataWriter:
         return metadata
 
     def get_caption(self) -> str:
-        result = self.metadata.get(XMP_KEYS_CAPTION[0], "")
+        result = (self.metadata or {}).get(XMP_KEYS_CAPTION[0], "")
         assert isinstance(result, str)
         return result
 
     def get_keywords(self) -> list[str]:
-        result = self.metadata.get(XMP_KEYS_KEYWORDS[0], []) or []
+        result = (self.metadata or {}).get(XMP_KEYS_KEYWORDS[0], []) or []
         if isinstance(result, str):
             result = [result]
         assert isinstance(result, list)
@@ -108,8 +108,11 @@ class MetadataWriter:
                     tags=metadata_tags,
                     params=["-overwrite_original"],
                 )
-        except Exception as exc:  # noqa: BLE001
+        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
@@ -151,9 +154,27 @@ class MetadataWriter:
         keywords: Keywords | None = None,
         color: Color | None = None,
     ):
+        metadata_tags: XmpMetadata = {}
         if caption is not None:
-            self.write_caption(caption)
+            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:
-            self.write_keywords(keywords)
+            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:
-            self.write_color(color)
+            metadata_tags["XMP-darktable:Colorlabels"] = color
+
+        if metadata_tags:
+            self.set_metadata(metadata_tags)

src/photools3/models.py

@@ -86,6 +86,42 @@ 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"
+            )
+        ),
+    ]
+
+
+class LlmResponseKeywords(BaseModel):
+    tags: list[str] = Field(
+        description=(
+            "A list of keywords that properly tag the photo. "
+            "Allowed keywords: "
+            + str(load_valid_keywords())
+            .replace("'", '"')
+            .replace("{", "[")
+            .replace("}", "]")
+        )
+    )
+
+    @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 LlmResponseCaptionsAndKeywords(BaseModel):
     caption: Annotated[
         str,

src/photools3/photo.py

@@ -1,6 +1,7 @@
 import base64
 from io import BytesIO
 from pathlib import Path
+from typing import Self
 
 from PIL import Image
 
@@ -17,6 +18,15 @@ class Photo:
         self.path = path
         self.image = Image.open(path)
 
+    def close(self):
+        self.image.close()
+
+    def __enter__(self) -> Self:
+        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)
@@ -24,7 +34,9 @@ class Photo:
 
     def to_rgb(self):
         if self.image.mode != "RGB":
-            self.image = self.image.convert("RGB")
+            original = self.image
+            self.image = original.convert("RGB")
+            original.close()
         return self
 
     def get_encoded(self):

src/photools3/processing.py

@@ -0,0 +1,128 @@
+import logging
+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)
+
+    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)
+            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:
+        session.close()
+
+    return outcomes

test/test_metadata_writer.py

@@ -1,5 +1,6 @@
 import shutil
 from pathlib import Path
+from unittest.mock import patch
 
 import pytest
 
@@ -211,3 +212,32 @@ def test_set_keywords(make_dng_file, pic, expected_keywords):
 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

@@ -48,6 +48,15 @@ def test_load_photo():
     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])

test/test_processing.py

@@ -0,0 +1,129 @@
+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()