refactor: strip AI pipeline to binary photo/other classifier

Drops face recognition, OCR, object detection, and semantic embeddings.
The sole remaining vision task is a CLIP-based binary classifier
(photography vs other); photos in "other" get needs_review=true so
screenshots, documents, memes and scans can be triaged from a new
filter pill in the UI.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

backend/app/services/vision/base.py

@@ -1,105 +1,25 @@
 """
-Abstract base classes for vision backends.
+Abstract base classes for the vision backend.
 
-Each ABC defines the contract a backend must satisfy. The default
-implementation is ONNXBackend (onnx_backend.py). A ROCm backend can be
-added later by subclassing these ABCs and registering via
-settings.vision.backend.
+The pipeline is now a single binary classifier: photography vs other.
+Feature extraction is an internal detail of the classifier and is not
+exposed as a separate service.
 """
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
+
 import numpy as np
 
 
-@dataclass
-class DetectionBox:
-    """A single object detection result."""
-    label: str
-    confidence: float
-    bbox: list[float]  # [x1, y1, x2, y2] normalized 0-1
-
-
-@dataclass
-class OCRResult:
-    """A single OCR text region."""
-    text: str
-    confidence: float
-    bbox: list[float]  # [x1, y1, x2, y2] normalized 0-1
-    language: str = ""
-
-
-@dataclass
-class FaceDetection:
-    """A detected face with its recognition embedding."""
-    bbox: list[float]  # [x1, y1, x2, y2] normalized 0-1
-    embedding: np.ndarray  # float32 vector (128-d for SFace)
-    quality: float
-
-
 @dataclass
 class ClassificationResult:
-    """A content-type classification."""
     label: str
     confidence: float
 
 
-class Embedder(ABC):
-    """Generates image and text embeddings (e.g. OpenCLIP ViT-B/32)."""
-
-    @abstractmethod
-    def embed_image(self, image: np.ndarray) -> np.ndarray:
-        """Return a normalized float32 embedding vector for an RGB image."""
-        ...
-
-    @abstractmethod
-    def embed_text(self, text: str) -> np.ndarray:
-        """Return a normalized float32 embedding vector for a text query."""
-        ...
-
-    @property
-    @abstractmethod
-    def dim(self) -> int:
-        """Dimensionality of the output embedding."""
-        ...
-
-
-class OCREngine(ABC):
-    """Extracts text from images (e.g. rapidocr-onnxruntime)."""
-
-    @abstractmethod
-    def run(self, image: np.ndarray) -> list[OCRResult]:
-        """Return OCR results for an RGB image."""
-        ...
-
-
-class ObjectDetector(ABC):
-    """Detects objects in images (e.g. YOLOv8n)."""
-
-    @abstractmethod
-    def detect(self, image: np.ndarray) -> list[DetectionBox]:
-        """Return detections for an RGB image."""
-        ...
-
-
 class ContentClassifier(ABC):
-    """Classifies images into content types (screenshot, document, etc.)."""
+    """Classifies an image into 'photography' or 'other'."""
 
     @abstractmethod
-    def classify(self, image: np.ndarray) -> list[ClassificationResult]:
-        """Return content type classifications for an RGB image."""
-        ...
-
-
-class FaceProcessor(ABC):
-    """Detects faces and extracts recognition embeddings (e.g. YuNet + SFace)."""
-
-    @abstractmethod
-    def process(self, image: np.ndarray) -> list[FaceDetection]:
-        """Return face detections with embeddings for an RGB image."""
-        ...
-
-    @property
-    @abstractmethod
-    def embedding_dim(self) -> int:
-        """Dimensionality of face embedding vectors."""
+    def classify(self, image: np.ndarray) -> ClassificationResult:
         ...