Apply enhancements

.python-version

@@ -0,0 +1 @@
+3.12

Dockerfile

@@ -3,15 +3,25 @@ FROM python:3.12-slim
 ENV PYTHONDONTWRITEBYTECODE=1 \
     PYTHONUNBUFFERED=1
 
-WORKDIR /app
-
 RUN apt-get update && apt-get install -y --no-install-recommends \
     git \
  && rm -rf /var/lib/apt/lists/*
 
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
+RUN useradd -m -u 1000 user
+USER user
+
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+RUN pip install --no-cache-dir --upgrade pip
+RUN pip install --no-cache-dir --user uv
+
+COPY --chown=user pyproject.toml .
+COPY --chown=user src ./src
+RUN uv sync --no-dev
 
-COPY app ./app
+ENV PATH="$HOME/app/.venv/bin:$PATH"
 
 CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "1"]

Makefile

@@ -0,0 +1,29 @@
+.PHONY: help docker-build docker-run local-install local-run local-test local-test-integration
+
+help:
+	@echo "---------------------------------------------------"
+	@echo "Targets:"
+	@echo "  docker-build            Build Docker image"
+	@echo "  docker-run              Run Docker image on :7860"
+	@echo "  local-install           Sync deps with uv"
+	@echo "  local-run               Run API locally on :7860"
+	@echo "  local-test              Run unit tests"
+	@echo "  local-test-integration  Run integration tests"
+
+docker-build:
+	docker build -t photo-classification .
+
+docker-run:
+	docker run --rm -p 7860:7860 photo-classification
+
+local-install:
+	uv sync --extra dev --python 3.12
+
+local-run:
+	uv run uvicorn app.main:app --host 0.0.0.0 --port 7860
+
+local-test:
+	uv run pytest -q
+
+local-test-integration:
+	uv run pytest -q -m integration

README.md

@@ -1,42 +1,176 @@
-'''
-.
-├── Dockerfile
-├── requirements.txt
-└── app
-    ├── __init__.py
-    ├── banks.py
-    ├── clip_service.py
-    ├── clip_store.py
-    ├── deps.py
-    ├── image_io.py
-    ├── label_hash.py
-    ├── logging_utils.py
-    ├── main.py
-    ├── middleware.py
-    ├── registry.py
-    ├── results.py
-    ├── schemas.py
-    └── settings.py
-'''
-
-
-Recommended runtime config
+---
+title: Photo Classification API
+emoji: 📷
+colorFrom: blue
+colorTo: gray
+sdk: docker
+app_port: 7860
+---
+
+# Photo Classification API
+
+```
+ ____  _           _           ____ _               
+|  _ \| |__   ___ | |_ ___    / ___| | __ _ ___ ___ 
+| |_) | '_ \ / _ \| __/ _ \  | |   | |/ _` / __/ __|
+|  __/| | | | (_) | || (_) | | |___| | (_| \__ \__ \
+|_|   |_| |_|\___/ \__\___/   \____|_|\__,_|___/___/
+```
+
+A small, prompt-driven photo classification API built on CLIP. You upload a label set
+(domains + labels with prompts), then classify images against that taxonomy without
+any fine-tuning.
+
+## Why this exists
+
+- Fast taxonomy iteration: change labels or prompts without retraining.
+- Simple deployment: CPU-only CLIP inference by default.
+- Clear outputs: domain hits, chosen domains, and label hits with scores.
+
+## Models
+
+- Text and image embeddings are produced by CLIP (`openai/clip-vit-base-patch32`).
+- All inference runs on CPU unless you adapt `ClipStore` to use GPU.
+- Scoring uses cosine-like similarity with CLIP's logit scale and a softmax.
+
+## Label datasets
+
+Label sets are JSON files with `domains` and `labels_by_domain`, each item defining:
+
+- `id`: stable identifier
+- `display`: human-readable name
+- `prompt`: the text prompt used for CLIP embedding
+
+Examples:
+
+- `label-dataset/personal-photos-lite-v1.json`
+- `label-dataset/personal-photos-large-v1.json`
+- `label-dataset/scene-dance-formation-group-v1.json`
+
+Use these as starting points or create your own taxonomy.
+
+## API quickstart
+
+1) Start the service (Docker or `uvicorn app.main:app`).
+2) Upload a label set.
+3) Optionally activate a label set.
+4) Classify images.
+
+Endpoints:
+
+- `POST /api/v1/label-sets` (upload)
+- `GET /api/v1/label-sets` (list)
+- `POST /api/v1/label-sets/{label_set_hash}/activate` (set default)
+- `POST /api/v1/classify` (classify image)
+
+`/api/v1/classify` body:
+
+```json
+{
+  "image_base64": "<base64>",
+  "domain_top_n": 2,
+  "top_k": 5
+}
+```
+
+Response includes `domain_hits`, `chosen_domains`, `label_hits`, and timings.
+
+## Recommended runtime config
+
+Default params for stable results:
+
+```json
 {
   "domain_top_n": 1,
   "top_k": 3
 }
+```
+
+Guard policy example:
+
+- Use `domain_top_n = 3`, `top_k = 4`.
+- If `non_dance` appears in `chosen_domains` and its top label score is high vs
+  `dance`, treat the photo as "not dancing" and skip dance-style conclusions.
+
+## Architecture
+
+- API layer: FastAPI endpoints and request/response schemas (`src/app/main.py`, `src/app/schemas.py`).
+- Use-case layer: two-stage classification (domain -> labels) (`src/app/clip_service.py`).
+- Model layer: CLIP model + processor + embedding banks (`src/app/clip_store.py`, `src/app/banks.py`).
+- Runtime support: registry, settings, logging, middleware (`src/app/registry.py`, `src/app/settings.py`,
+  `src/app/logging_utils.py`, `src/app/middleware.py`).
+
+## Coding rules (deeper)
+
+Separation of concerns:
 
-How to use the guard (simple policy)
+- API layer does IO only: parse input, validate, load image, call use-case, return typed DTOs.
+- Use-case layer owns business logic: the two-stage classification and result shaping.
+- Model layer owns ML specifics: CLIP loading, text/image encoding, and logit scaling.
+- Data layer owns taxonomy inputs: JSON label sets, hashing, and embedding banks.
 
-Recommended default call:
+API vs use-case vs torch:
 
-domain_top_n = 3
+- API should not import torch or transformers; it deals in base64, Pydantic models, and HTTP.
+- Use-case should not depend on HTTP; it accepts a bank + image and returns a typed result.
+- Torch code lives in `ClipStore` only; the rest of the code treats embeddings as opaque tensors.
 
-top_k = 4
+Typed outputs and clean steps:
 
-Then in your client logic:
+- Each step returns a typed value (`LabelSetBank`, `ClassificationResult`, `Hit` lists).
+- Keep operations in small pure functions or methods that express a single step:
+  - validate input
+  - load/normalize image
+  - encode image
+  - score domains
+  - merge label banks
+  - score labels
 
-if non_dance appears in chosen_domains and
+Dataset usage:
 
-its top label score is high vs dance,
-treat it as “not dancing” and skip dance-style conclusions.
+- Label sets are data, not code. Changes to taxonomy are done in JSON files or user payloads.
+- Stable hashes (`label_set_hash`) are derived from canonical JSON for reproducibility.
+
+Avoid old PyTorch habits:
+
+- No training loops, optimizers, or manual grad handling; this is inference-only.
+- Use `torch.no_grad()` and normalized embeddings for stable cosine-like comparisons.
+- Keep tensors on the same device; `ClipStore` owns device placement.
+- Prefer small, readable tensor ops over complex pipelines.
+
+Error handling and HTTP boundaries:
+
+- Decode/validate base64 and size limits in `image_io`, not inside ML code.
+- Convert internal errors to HTTP responses at the boundary (e.g., 400/404/413).
+- Log JSON events with request IDs for traceability.
+
+## Tests
+
+- Fast, deterministic tests use fakes for classifier and store (`tests/fakes.py`).
+- Integration test optionally loads real CLIP (`tests/test_integration_real_clip.py`).
+- Run:
+  - `pytest -q`
+  - `pytest -q -m integration`
+
+## Project layout
+
+```
+.
+├── Dockerfile
+├── requirements.txt
+└── src
+    └── app
+        ├── banks.py
+        ├── clip_service.py
+        ├── clip_store.py
+        ├── deps.py
+        ├── image_io.py
+        ├── label_hash.py
+        ├── logging_utils.py
+        ├── main.py
+        ├── middleware.py
+        ├── registry.py
+        ├── results.py
+        ├── schemas.py
+        └── settings.py
+```

main.py

@@ -1,163 +0,0 @@
-from __future__ import annotations
-
-from fastapi import FastAPI, HTTPException, Request, Depends
-from fastapi.responses import JSONResponse
-
-from app.clip_store import ClipStore
-from app.clip_service import TwoStageClassifier
-from app.deps import get_bank, get_request_id, resolve_bank
-from app.image_io import load_image_from_base64
-from app.logging_utils import setup_logging, log_json
-from app.middleware import RequestIdMiddleware
-from app.registry import LabelSetRegistry
-from app.schemas import (
-    ActivateResponse,
-    ClassifyRequest,
-    ClassifyResponse,
-    Hit,
-    LabelSet,
-    LabelSetCreateResponse,
-    LabelSetInfo,
-)
-from app.settings import settings
-
-
-logger = setup_logging()
-
-def build_app() -> FastAPI:
-    logger = setup_logging()
-
-    app = FastAPI(
-        title="Photo Classification API",
-        version="1.0.0",
-        docs_url="/docs",
-        redoc_url=None,
-        openapi_url="/openapi.json",
-    )
-    app.add_middleware(RequestIdMiddleware)
-
-    store = ClipStore()
-    classifier = TwoStageClassifier(store=store)
-    registry = LabelSetRegistry(banks={})
-
-    # register routes here (same code as before, using closure vars)
-
-    return app
-
-app = build_app()
-
-store = ClipStore()
-classifier = TwoStageClassifier(store=store)
-registry = LabelSetRegistry(banks={})
-
-
-@app.exception_handler(Exception)
-async def unhandled_exception_handler(request: Request, exc: Exception):
-    rid = getattr(request.state, "request_id", None)
-    log_json(logger, event="error.unhandled", request_id=rid, error=str(exc), path=str(request.url.path))
-    return JSONResponse(status_code=500, content={"detail": "internal server error"})
-
-
-@app.post("/api/v1/label-sets", response_model=LabelSetCreateResponse)
-def create_label_set(payload: LabelSet, request_id: str = Depends(get_request_id)) -> LabelSetCreateResponse:
-    bank = store.build_bank(payload)
-    registry.upsert(bank)
-
-    label_count = sum(len(b.ids) for b in bank.labels_by_domain.values())
-    is_default = (registry.default_hash == bank.label_set_hash)
-
-    log_json(
-        logger,
-        event="label_sets.upsert",
-        request_id=request_id,
-        label_set_hash=bank.label_set_hash,
-        name=bank.name,
-        domain_count=len(bank.domains.ids),
-        label_count=label_count,
-        is_default=is_default,
-    )
-
-    return LabelSetCreateResponse(
-        label_set_hash=bank.label_set_hash,
-        name=bank.name,
-        domain_count=len(bank.domains.ids),
-        label_count=label_count,
-        is_default=is_default,
-    )
-
-
-@app.get("/api/v1/label-sets", response_model=list[LabelSetInfo])
-def list_label_sets(request_id: str = Depends(get_request_id)) -> list[LabelSetInfo]:
-    out: list[LabelSetInfo] = []
-    for bank in registry.banks.values():
-        label_count = sum(len(b.ids) for b in bank.labels_by_domain.values())
-        out.append(
-            LabelSetInfo(
-                label_set_hash=bank.label_set_hash,
-                name=bank.name,
-                domain_count=len(bank.domains.ids),
-                label_count=label_count,
-                is_default=(registry.default_hash == bank.label_set_hash),
-            )
-        )
-
-    log_json(logger, event="label_sets.list", request_id=request_id, count=len(out))
-    return out
-
-
-@app.post("/api/v1/label-sets/{label_set_hash}/activate", response_model=ActivateResponse)
-def activate_label_set(label_set_hash: str, request_id: str = Depends(get_request_id)) -> ActivateResponse:
-    try:
-        registry.activate(label_set_hash)
-    except KeyError:
-        raise HTTPException(status_code=404, detail="Unknown label_set_hash")
-
-    log_json(logger, event="label_sets.activate", request_id=request_id, default_label_set_hash=label_set_hash)
-    return ActivateResponse(default_label_set_hash=label_set_hash)
-
-
-@app.post("/api/v1/classify", response_model=ClassifyResponse)
-def classify(
-    payload: ClassifyRequest,
-    request: Request,
-    request_id: str = Depends(get_request_id),
-    label_set_hash: Optional[str] = Query(default=None, description="If omitted, uses the default label set."),
-) -> ClassifyResponse:
-    bank = resolve_bank(registry, label_set_hash)
-
-    image = load_image_from_base64(
-        payload.image_base64,
-        max_bytes=settings.max_image_mb * 1024 * 1024,
-    )
-
-    res = classifier.classify(
-        bank=bank,
-        image=image,
-        domain_top_n=payload.domain_top_n or settings.default_domain_top_n,
-        top_k=payload.top_k or settings.default_top_k,
-    )
-
-    log_json(
-        logger,
-        event="classify",
-        request_id=request_id,
-        label_set_hash=bank.label_set_hash,
-        model_id=settings.clip_model_id,
-        domain_top_n=payload.domain_top_n,
-        top_k=payload.top_k,
-        chosen_domains=res.chosen_domains,
-        elapsed_ms=res.timings.total_ms,
-        elapsed_domain_ms=res.timings.domain_ms,
-        elapsed_labels_ms=res.timings.labels_ms,
-    )
-
-    return ClassifyResponse(
-        label_set_hash=bank.label_set_hash,
-        model_id=settings.clip_model_id,
-        domain_hits=[Hit(id=i, score=s) for i, s in res.domain_hits],
-        chosen_domains=res.chosen_domains,
-        label_hits=[Hit(id=i, score=s) for i, s in res.label_hits],
-        elapsed_ms=res.timings.total_ms,
-        elapsed_domain_ms=res.timings.domain_ms,
-        elapsed_labels_ms=res.timings.labels_ms,
-    )

pyproject.toml

@@ -0,0 +1,30 @@
+[build-system]
+requires = ["setuptools>=68", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "photo-classification"
+version = "0.1.0"
+description = "Prompt-driven photo classification API built on CLIP"
+readme = "README.md"
+requires-python = ">=3.12,<3.13"
+dependencies = [
+  "fastapi==0.115.6",
+  "pillow==10.4.0",
+  "pydantic==2.8.2",
+  "torch==2.3.1",
+  "transformers==4.44.2",
+  "uvicorn[standard]==0.30.6",
+]
+
+[project.optional-dependencies]
+dev = [
+  "httpx==0.27.2",
+  "pytest==8.3.2",
+]
+
+[tool.setuptools]
+package-dir = {"" = "src"}
+
+[tool.setuptools.packages.find]
+where = ["src"]

clip_store.py → src/app/clip_store.py

@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+import warnings
+
 import torch
 from transformers import CLIPModel, CLIPProcessor
 
@@ -18,7 +20,16 @@ class ClipStore:
     def __init__(self) -> None:
         self.device = torch.device("cpu")
         self.model = CLIPModel.from_pretrained(settings.clip_model_id).to(self.device).eval()
-        self.processor = CLIPProcessor.from_pretrained(settings.clip_model_id)
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                message="`clean_up_tokenization_spaces` was not set",
+                category=FutureWarning,
+                module="transformers.tokenization_utils_base",
+            )
+            self.processor = CLIPProcessor.from_pretrained(settings.clip_model_id)
+        if hasattr(self.processor, "tokenizer") and hasattr(self.processor.tokenizer, "clean_up_tokenization_spaces"):
+            self.processor.tokenizer.clean_up_tokenization_spaces = True
 
     def build_bank(self, label_set: LabelSet) -> LabelSetBank:
         payload = label_set.model_dump()

src/app/main.py

@@ -0,0 +1,265 @@
+from __future__ import annotations
+
+from contextlib import asynccontextmanager
+from dataclasses import dataclass
+from typing import Optional
+
+from fastapi import Depends, FastAPI, HTTPException, Query, Request, Response
+from fastapi.responses import JSONResponse
+
+from app.clip_store import ClipStore
+from app.clip_service import TwoStageClassifier
+from app.deps import get_request_id, resolve_bank
+from app.image_io import load_image_from_base64
+from app.logging_utils import setup_logging, log_json
+from app.middleware import RequestIdMiddleware
+from app.registry import LabelSetRegistry
+from app.schemas import (
+    ActivateResponse,
+    ClassifyRequest,
+    ClassifyResponse,
+    Hit,
+    LabelSet,
+    LabelSetCreateResponse,
+    LabelSetInfo,
+)
+from app.settings import settings
+
+
+logger = setup_logging()
+
+BANNER = r"""
+ ____  _           _           ____ _               
+|  _ \| |__   ___ | |_ ___    / ___| | __ _ ___ ___ 
+| |_) | '_ \ / _ \| __/ _ \  | |   | |/ _` / __/ __|
+|  __/| | | | (_) | || (_) | | |___| | (_| \__ \__ \
+|_|   |_| |_|\___/ \__\___/   \____|_|\__,_|___/___/
+"""
+
+
+@dataclass
+class Resources:
+    store: ClipStore
+    classifier: TwoStageClassifier
+    registry: LabelSetRegistry
+
+
+async def _maybe_aclose(obj) -> None:
+    aclose = getattr(obj, "aclose", None)
+    if callable(aclose):
+        await aclose()
+        return
+    close = getattr(obj, "close", None)
+    if callable(close):
+        close()
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    store = ClipStore()
+    classifier = TwoStageClassifier(store=store)
+    registry = LabelSetRegistry(banks={})
+
+    app.state.resources = Resources(store=store, classifier=classifier, registry=registry)
+    try:
+        yield
+    finally:
+        await _maybe_aclose(registry)
+        await _maybe_aclose(classifier)
+        await _maybe_aclose(store)
+
+
+def get_resources(request: Request) -> Resources:
+    return request.app.state.resources
+
+
+def get_store(res: Resources = Depends(get_resources)) -> ClipStore:
+    return res.store
+
+
+def get_classifier(res: Resources = Depends(get_resources)) -> TwoStageClassifier:
+    return res.classifier
+
+
+def get_registry(res: Resources = Depends(get_resources)) -> LabelSetRegistry:
+    return res.registry
+
+
+def create_app(*, resources: Resources | None = None) -> FastAPI:
+    app = FastAPI(
+        lifespan=lifespan,
+        title="Photo Classification API",
+        version="1.0.0",
+        description=f"```\n{BANNER.strip()}\n```",
+        docs_url="/docs",
+        redoc_url=None,
+        openapi_url="/openapi.json",
+    )
+    app.add_middleware(RequestIdMiddleware)
+
+    if resources is not None:
+        app.state.resources = resources
+
+        @asynccontextmanager
+        async def _lifespan_override(_app: FastAPI):
+            _app.state.resources = resources
+            try:
+                yield
+            finally:
+                await _maybe_aclose(resources.registry)
+                await _maybe_aclose(resources.classifier)
+                await _maybe_aclose(resources.store)
+
+        app.router.lifespan_context = _lifespan_override
+
+    @app.get("/favicon.ico", include_in_schema=False)
+    def favicon() -> Response:
+        svg = (
+            "<svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 64 64'>"
+            "<rect width='64' height='64' rx='12' fill='#1f2937'/>"
+            "<circle cx='24' cy='28' r='10' fill='#f59e0b'/>"
+            "<circle cx='44' cy='28' r='10' fill='#60a5fa'/>"
+            "<rect x='18' y='38' width='28' height='10' rx='5' fill='#e5e7eb'/>"
+            "</svg>"
+        )
+        return Response(content=svg, media_type="image/svg+xml")
+
+    @app.exception_handler(Exception)
+    async def unhandled_exception_handler(request: Request, exc: Exception):
+        rid = getattr(request.state, "request_id", None)
+        log_json(logger, event="error.unhandled", request_id=rid, error=str(exc), path=str(request.url.path))
+        return JSONResponse(status_code=500, content={"detail": "internal server error"})
+
+    @app.post("/api/v1/label-sets", response_model=LabelSetCreateResponse)
+    def create_label_set(
+        payload: LabelSet,
+        request_id: str = Depends(get_request_id),
+        store: ClipStore = Depends(get_store),
+        registry: LabelSetRegistry = Depends(get_registry),
+    ) -> LabelSetCreateResponse:
+        bank = store.build_bank(payload)
+        registry.upsert(bank)
+
+        label_count = sum(len(b.ids) for b in bank.labels_by_domain.values())
+        is_default = (registry.default_hash == bank.label_set_hash)
+
+        log_json(
+            logger,
+            event="label_sets.upsert",
+            request_id=request_id,
+            label_set_hash=bank.label_set_hash,
+            name=bank.name,
+            domain_count=len(bank.domains.ids),
+            label_count=label_count,
+            is_default=is_default,
+        )
+
+        return LabelSetCreateResponse(
+            label_set_hash=bank.label_set_hash,
+            name=bank.name,
+            domain_count=len(bank.domains.ids),
+            label_count=label_count,
+            is_default=is_default,
+        )
+
+    @app.get("/api/v1/label-sets", response_model=list[LabelSetInfo])
+    def list_label_sets(
+        request_id: str = Depends(get_request_id),
+        registry: LabelSetRegistry = Depends(get_registry),
+    ) -> list[LabelSetInfo]:
+        out: list[LabelSetInfo] = []
+        for bank in registry.banks.values():
+            label_count = sum(len(b.ids) for b in bank.labels_by_domain.values())
+            out.append(
+                LabelSetInfo(
+                    label_set_hash=bank.label_set_hash,
+                    name=bank.name,
+                    domain_count=len(bank.domains.ids),
+                    label_count=label_count,
+                    is_default=(registry.default_hash == bank.label_set_hash),
+                )
+            )
+
+        log_json(logger, event="label_sets.list", request_id=request_id, count=len(out))
+        return out
+
+    @app.post("/api/v1/label-sets/{label_set_hash}/activate", response_model=ActivateResponse)
+    def activate_label_set(
+        label_set_hash: str,
+        request_id: str = Depends(get_request_id),
+        registry: LabelSetRegistry = Depends(get_registry),
+    ) -> ActivateResponse:
+        try:
+            registry.activate(label_set_hash)
+        except KeyError:
+            raise HTTPException(status_code=404, detail="Unknown label_set_hash")
+
+        log_json(logger, event="label_sets.activate", request_id=request_id, default_label_set_hash=label_set_hash)
+        return ActivateResponse(default_label_set_hash=label_set_hash)
+
+    @app.post("/api/v1/classify", response_model=ClassifyResponse)
+    def classify(
+        payload: ClassifyRequest,
+        request: Request,
+        request_id: str = Depends(get_request_id),
+        label_set_hash: Optional[str] = Query(default=None, description="If omitted, uses the default label set."),
+        classifier: TwoStageClassifier = Depends(get_classifier),
+        registry: LabelSetRegistry = Depends(get_registry),
+    ) -> ClassifyResponse:
+        bank = resolve_bank(registry, label_set_hash)
+
+        image = load_image_from_base64(
+            payload.image_base64,
+            max_bytes=settings.max_image_mb * 1024 * 1024,
+        )
+
+        res = classifier.classify(
+            bank=bank,
+            image=image,
+            domain_top_n=payload.domain_top_n or settings.default_domain_top_n,
+            top_k=payload.top_k or settings.default_top_k,
+        )
+
+        log_json(
+            logger,
+            event="classify",
+            request_id=request_id,
+            label_set_hash=bank.label_set_hash,
+            model_id=settings.clip_model_id,
+            domain_top_n=payload.domain_top_n,
+            top_k=payload.top_k,
+            chosen_domains=res.chosen_domains,
+            elapsed_ms=res.timings.total_ms,
+            elapsed_domain_ms=res.timings.domain_ms,
+            elapsed_labels_ms=res.timings.labels_ms,
+        )
+
+        return ClassifyResponse(
+            label_set_hash=bank.label_set_hash,
+            model_id=settings.clip_model_id,
+            domain_hits=[Hit(id=i, score=s) for i, s in res.domain_hits],
+            chosen_domains=res.chosen_domains,
+            label_hits=[Hit(id=i, score=s) for i, s in res.label_hits],
+            elapsed_ms=res.timings.total_ms,
+            elapsed_domain_ms=res.timings.domain_ms,
+            elapsed_labels_ms=res.timings.labels_ms,
+        )
+
+    return app
+
+
+def build_app(
+    store: ClipStore | None = None,
+    classifier: TwoStageClassifier | None = None,
+    registry: LabelSetRegistry | None = None,
+) -> FastAPI:
+    if store is None and classifier is None and registry is None:
+        return create_app()
+    store = store or ClipStore()
+    classifier = classifier or TwoStageClassifier(store=store)
+    registry = registry or LabelSetRegistry(banks={})
+    resources = Resources(store=store, classifier=classifier, registry=registry)
+    return create_app(resources=resources)
+
+
+app = create_app()

schemas.py → src/app/schemas.py

@@ -60,7 +60,7 @@ class Hit(BaseModel):
 
 
 class ClassifyResponse(BaseModel):
-    model_config = ConfigDict(extra="forbid")
+    model_config = ConfigDict(extra="forbid", protected_namespaces=())
     label_set_hash: str
     model_id: str
     domain_hits: List[Hit]

src/photo_classification.egg-info/PKG-INFO

@@ -0,0 +1,192 @@
+Metadata-Version: 2.4
+Name: photo-classification
+Version: 0.1.0
+Summary: Prompt-driven photo classification API built on CLIP
+Requires-Python: <3.13,>=3.12
+Description-Content-Type: text/markdown
+Requires-Dist: fastapi==0.115.6
+Requires-Dist: pillow==10.4.0
+Requires-Dist: pydantic==2.8.2
+Requires-Dist: torch==2.3.1
+Requires-Dist: transformers==4.44.2
+Requires-Dist: uvicorn[standard]==0.30.6
+Provides-Extra: dev
+Requires-Dist: httpx==0.27.2; extra == "dev"
+Requires-Dist: pytest==8.3.2; extra == "dev"
+
+---
+title: Photo Classification API
+emoji: 📷
+colorFrom: blue
+colorTo: gray
+sdk: docker
+app_port: 7860
+---
+
+# Photo Classification API
+
+```
+ ____  _           _           ____ _               
+|  _ \| |__   ___ | |_ ___    / ___| | __ _ ___ ___ 
+| |_) | '_ \ / _ \| __/ _ \  | |   | |/ _` / __/ __|
+|  __/| | | | (_) | || (_) | | |___| | (_| \__ \__ \
+|_|   |_| |_|\___/ \__\___/   \____|_|\__,_|___/___/
+```
+
+A small, prompt-driven photo classification API built on CLIP. You upload a label set
+(domains + labels with prompts), then classify images against that taxonomy without
+any fine-tuning.
+
+## Why this exists
+
+- Fast taxonomy iteration: change labels or prompts without retraining.
+- Simple deployment: CPU-only CLIP inference by default.
+- Clear outputs: domain hits, chosen domains, and label hits with scores.
+
+## Models
+
+- Text and image embeddings are produced by CLIP (`openai/clip-vit-base-patch32`).
+- All inference runs on CPU unless you adapt `ClipStore` to use GPU.
+- Scoring uses cosine-like similarity with CLIP's logit scale and a softmax.
+
+## Label datasets
+
+Label sets are JSON files with `domains` and `labels_by_domain`, each item defining:
+
+- `id`: stable identifier
+- `display`: human-readable name
+- `prompt`: the text prompt used for CLIP embedding
+
+Examples:
+
+- `label-dataset/personal-photos-lite-v1.json`
+- `label-dataset/personal-photos-large-v1.json`
+- `label-dataset/scene-dance-formation-group-v1.json`
+
+Use these as starting points or create your own taxonomy.
+
+## API quickstart
+
+1) Start the service (Docker or `uvicorn app.main:app`).
+2) Upload a label set.
+3) Optionally activate a label set.
+4) Classify images.
+
+Endpoints:
+
+- `POST /api/v1/label-sets` (upload)
+- `GET /api/v1/label-sets` (list)
+- `POST /api/v1/label-sets/{label_set_hash}/activate` (set default)
+- `POST /api/v1/classify` (classify image)
+
+`/api/v1/classify` body:
+
+```json
+{
+  "image_base64": "<base64>",
+  "domain_top_n": 2,
+  "top_k": 5
+}
+```
+
+Response includes `domain_hits`, `chosen_domains`, `label_hits`, and timings.
+
+## Recommended runtime config
+
+Default params for stable results:
+
+```json
+{
+  "domain_top_n": 1,
+  "top_k": 3
+}
+```
+
+Guard policy example:
+
+- Use `domain_top_n = 3`, `top_k = 4`.
+- If `non_dance` appears in `chosen_domains` and its top label score is high vs
+  `dance`, treat the photo as "not dancing" and skip dance-style conclusions.
+
+## Architecture
+
+- API layer: FastAPI endpoints and request/response schemas (`src/app/main.py`, `src/app/schemas.py`).
+- Use-case layer: two-stage classification (domain -> labels) (`src/app/clip_service.py`).
+- Model layer: CLIP model + processor + embedding banks (`src/app/clip_store.py`, `src/app/banks.py`).
+- Runtime support: registry, settings, logging, middleware (`src/app/registry.py`, `src/app/settings.py`,
+  `src/app/logging_utils.py`, `src/app/middleware.py`).
+
+## Coding rules (deeper)
+
+Separation of concerns:
+
+- API layer does IO only: parse input, validate, load image, call use-case, return typed DTOs.
+- Use-case layer owns business logic: the two-stage classification and result shaping.
+- Model layer owns ML specifics: CLIP loading, text/image encoding, and logit scaling.
+- Data layer owns taxonomy inputs: JSON label sets, hashing, and embedding banks.
+
+API vs use-case vs torch:
+
+- API should not import torch or transformers; it deals in base64, Pydantic models, and HTTP.
+- Use-case should not depend on HTTP; it accepts a bank + image and returns a typed result.
+- Torch code lives in `ClipStore` only; the rest of the code treats embeddings as opaque tensors.
+
+Typed outputs and clean steps:
+
+- Each step returns a typed value (`LabelSetBank`, `ClassificationResult`, `Hit` lists).
+- Keep operations in small pure functions or methods that express a single step:
+  - validate input
+  - load/normalize image
+  - encode image
+  - score domains
+  - merge label banks
+  - score labels
+
+Dataset usage:
+
+- Label sets are data, not code. Changes to taxonomy are done in JSON files or user payloads.
+- Stable hashes (`label_set_hash`) are derived from canonical JSON for reproducibility.
+
+Avoid old PyTorch habits:
+
+- No training loops, optimizers, or manual grad handling; this is inference-only.
+- Use `torch.no_grad()` and normalized embeddings for stable cosine-like comparisons.
+- Keep tensors on the same device; `ClipStore` owns device placement.
+- Prefer small, readable tensor ops over complex pipelines.
+
+Error handling and HTTP boundaries:
+
+- Decode/validate base64 and size limits in `image_io`, not inside ML code.
+- Convert internal errors to HTTP responses at the boundary (e.g., 400/404/413).
+- Log JSON events with request IDs for traceability.
+
+## Tests
+
+- Fast, deterministic tests use fakes for classifier and store (`tests/fakes.py`).
+- Integration test optionally loads real CLIP (`tests/test_integration_real_clip.py`).
+- Run:
+  - `pytest -q`
+  - `pytest -q -m integration`
+
+## Project layout
+
+```
+.
+├── Dockerfile
+├── requirements.txt
+└── src
+    └── app
+        ├── banks.py
+        ├── clip_service.py
+        ├── clip_store.py
+        ├── deps.py
+        ├── image_io.py
+        ├── label_hash.py
+        ├── logging_utils.py
+        ├── main.py
+        ├── middleware.py
+        ├── registry.py
+        ├── results.py
+        ├── schemas.py
+        └── settings.py
+```

src/photo_classification.egg-info/SOURCES.txt

@@ -0,0 +1,24 @@
+README.md
+pyproject.toml
+src/app/__init__.py
+src/app/app_factory.py
+src/app/banks.py
+src/app/clip_service.py
+src/app/clip_store.py
+src/app/deps.py
+src/app/image_io.py
+src/app/label_hash.py
+src/app/logging_utils.py
+src/app/main.py
+src/app/middleware.py
+src/app/registry.py
+src/app/results.py
+src/app/schemas.py
+src/app/settings.py
+src/photo_classification.egg-info/PKG-INFO
+src/photo_classification.egg-info/SOURCES.txt
+src/photo_classification.egg-info/dependency_links.txt
+src/photo_classification.egg-info/requires.txt
+src/photo_classification.egg-info/top_level.txt
+tests/test_api.py
+tests/test_integration_real_clip.py

src/photo_classification.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

src/photo_classification.egg-info/requires.txt

@@ -0,0 +1,10 @@
+fastapi==0.115.6
+pillow==10.4.0
+pydantic==2.8.2
+torch==2.3.1
+transformers==4.44.2
+uvicorn[standard]==0.30.6
+
+[dev]
+httpx==0.27.2
+pytest==8.3.2

src/photo_classification.egg-info/top_level.txt

@@ -0,0 +1 @@
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tests/__init__.py

@@ -0,0 +1 @@
+"""Test package marker for src layout imports."""

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