chore(ml): updated dockerfile, added typing, packaging (#2642)

* updated dockerfile, added typing, packaging

apply env change

* added arm64 support

* added ml version pump, second try for arm64

* added linting config to pyproject.toml

* renamed ml input field

* fixed linter config

* fixed dev docker compose

machine-learning/app/main.py

@@ -0,0 +1,179 @@
+import os
+from typing import Any
+from schemas import (
+    EmbeddingResponse,
+    FaceResponse,
+    TagResponse,
+    MessageResponse,
+    TextModelRequest,
+    TextResponse,
+    VisionModelRequest,
+)
+import cv2 as cv
+import uvicorn
+
+from insightface.app import FaceAnalysis
+from transformers import pipeline
+from sentence_transformers import SentenceTransformer
+from transformers import Pipeline
+from PIL import Image
+from fastapi import FastAPI
+
+
+classification_model = os.getenv(
+    "MACHINE_LEARNING_CLASSIFICATION_MODEL", "microsoft/resnet-50"
+)
+clip_image_model = os.getenv("MACHINE_LEARNING_CLIP_IMAGE_MODEL", "clip-ViT-B-32")
+clip_text_model = os.getenv("MACHINE_LEARNING_CLIP_TEXT_MODEL", "clip-ViT-B-32")
+facial_recognition_model = os.getenv(
+    "MACHINE_LEARNING_FACIAL_RECOGNITION_MODEL", "buffalo_l"
+)
+
+min_face_score = float(os.getenv("MACHINE_LEARNING_MIN_FACE_SCORE", 0.7))
+min_tag_score = float(os.getenv("MACHINE_LEARNING_MIN_TAG_SCORE", 0.9))
+eager_startup = (
+    os.getenv("MACHINE_LEARNING_EAGER_STARTUP", "true") == "true"
+)  # loads all models at startup
+
+cache_folder = os.getenv("MACHINE_LEARNING_CACHE_FOLDER", "/cache")
+
+_model_cache = {}
+
+app = FastAPI()
+
+
+@app.on_event("startup")
+async def startup_event() -> None:
+    models = [
+        (classification_model, "image-classification"),
+        (clip_image_model, "clip"),
+        (clip_text_model, "clip"),
+        (facial_recognition_model, "facial-recognition"),
+    ]
+
+    # Get all models
+    for model_name, model_type in models:
+        if eager_startup:
+            get_cached_model(model_name, model_type)
+        else:
+            _get_model(model_name, model_type)
+
+
+@app.get("/", response_model=MessageResponse)
+async def root() -> dict[str, str]:
+    return {"message": "Immich ML"}
+
+
+@app.get("/ping", response_model=TextResponse)
+def ping() -> str:
+    return "pong"
+
+
+@app.post("/image-classifier/tag-image", response_model=TagResponse, status_code=200)
+def image_classification(payload: VisionModelRequest) -> list[str]:
+    model = get_cached_model(classification_model, "image-classification")
+    assetPath = payload.image_path
+    labels = run_engine(model, assetPath)
+    return labels
+
+
+@app.post(
+    "/sentence-transformer/encode-image",
+    response_model=EmbeddingResponse,
+    status_code=200,
+)
+def clip_encode_image(payload: VisionModelRequest) -> list[float]:
+    model = get_cached_model(clip_image_model, "clip")
+    image = Image.open(payload.image_path)
+    return model.encode(image).tolist()
+
+
+@app.post(
+    "/sentence-transformer/encode-text",
+    response_model=EmbeddingResponse,
+    status_code=200,
+)
+def clip_encode_text(payload: TextModelRequest) -> list[float]:
+    model = get_cached_model(clip_text_model, "clip")
+    return model.encode(payload.text).tolist()
+
+
+@app.post(
+    "/facial-recognition/detect-faces", response_model=FaceResponse, status_code=200
+)
+def facial_recognition(payload: VisionModelRequest) -> list[dict[str, Any]]:
+    model = get_cached_model(facial_recognition_model, "facial-recognition")
+    img = cv.imread(payload.image_path)
+    height, width, _ = img.shape
+    results = []
+    faces = model.get(img)
+
+    for face in faces:
+        if face.det_score < min_face_score:
+            continue
+        x1, y1, x2, y2 = face.bbox
+
+        results.append(
+            {
+                "imageWidth": width,
+                "imageHeight": height,
+                "boundingBox": {
+                    "x1": round(x1),
+                    "y1": round(y1),
+                    "x2": round(x2),
+                    "y2": round(y2),
+                },
+                "score": face.det_score.item(),
+                "embedding": face.normed_embedding.tolist(),
+            }
+        )
+    return results
+
+
+def run_engine(engine: Pipeline, path: str) -> list[str]:
+    result: list[str] = []
+    predictions: list[dict[str, Any]] = engine(path)  # type: ignore
+
+    for pred in predictions:
+        tags = pred["label"].split(", ")
+        if pred["score"] > min_tag_score:
+            result = [*result, *tags]
+
+    if len(result) > 1:
+        result = list(set(result))
+
+    return result
+
+
+def get_cached_model(model, task) -> Any:
+    global _model_cache
+    key = "|".join([model, str(task)])
+    if key not in _model_cache:
+        model = _get_model(model, task)
+        _model_cache[key] = model
+
+    return _model_cache[key]
+
+
+def _get_model(model, task) -> Any:
+    match task:
+        case "facial-recognition":
+            model = FaceAnalysis(
+                name=model,
+                root=cache_folder,
+                allowed_modules=["detection", "recognition"],
+            )
+            model.prepare(ctx_id=0, det_size=(640, 640))
+        case "clip":
+            model = SentenceTransformer(model, cache_folder=cache_folder)
+        case _:
+            model = pipeline(model=model, task=task)
+    return model
+
+
+if __name__ == "__main__":
+    host = os.getenv("MACHINE_LEARNING_HOST", "0.0.0.0")
+    port = int(os.getenv("MACHINE_LEARNING_PORT", 3003))
+    is_dev = os.getenv("NODE_ENV") == "development"
+
+    uvicorn.run("main:app", host=host, port=port, reload=is_dev, workers=1)