Added image classification

docker/docker-compose.gpu.yml

@@ -1,18 +1,17 @@
-version: '3.8'
-
+version: "3.8"
 
 services:
   immich_server:
     image: immich-server-dev:1.0.0
     build:
-      context: .
+      context: ../server
       target: development
-      dockerfile: ./Dockerfile
+      dockerfile: ../server/Dockerfile
     command: npm run start:dev
-    expose: 
+    expose:
       - "3000"
     volumes:
-      - .:/usr/src/app
+      - ../server:/usr/src/app
       - ${UPLOAD_LOCATION}:/usr/src/app/upload
       - /usr/src/app/node_modules
     env_file:
@@ -27,7 +26,7 @@ services:
     container_name: immich_redis
     image: redis:6.2
     networks:
-      - immich_network  
+      - immich_network
 
   database:
     container_name: immich_postgres
@@ -44,7 +43,7 @@ services:
     ports:
       - 5432:5432
     networks:
-      - immich_network  
+      - immich_network
 
   nginx:
     container_name: proxy_nginx
@@ -61,7 +60,36 @@ services:
     depends_on:
       - immich_server
 
+  immich_tf_fastapi:
+    container_name: immich_tf_fastapi
+    image: tensor_flow_fastapi:1.0.0
+    restart: always
+    command: uvicorn app.main:app --proxy-headers --host 0.0.0.0 --port 8000 --reload
+    build:
+      context: ../machine_learning
+      target: gpu
+      dockerfile: ../machine_learning/Dockerfile
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              capabilities: [gpu]
+    volumes:
+      - ../machine_learning/app:/code/app
+      - ${UPLOAD_LOCATION}:/code/app/upload
+    ports:
+      - 2285:8000
+    expose:
+      - "8000"
+    depends_on:
+      - database
+    networks:
+      - immich_network
+
+      
 networks:
   immich_network:
 volumes:
-  pgdata:
+  pgdata:

docker/docker-compose.yml

@@ -67,14 +67,8 @@ services:
     command: uvicorn app.main:app --proxy-headers --host 0.0.0.0 --port 8000 --reload
     build:
       context: ../machine_learning
+      target: cpu
       dockerfile: ../machine_learning/Dockerfile
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
     volumes:
       - ../machine_learning/app:/code/app
       - ${UPLOAD_LOCATION}:/code/app/upload
@@ -87,7 +81,6 @@ services:
     networks:
       - immich_network
 
-      
 networks:
   immich_network:
 volumes:

machine_learning/.gitignore

@@ -1 +1,2 @@
-app/__pycache__
+app/__pycache__
+/devenv

machine_learning/Dockerfile

@@ -1,4 +1,5 @@
-FROM tensorflow/tensorflow:latest-gpu 
+## GPU Build
+FROM tensorflow/tensorflow:latest-gpu as gpu
 
 WORKDIR /code
 
@@ -8,4 +9,14 @@ RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
 
 COPY ./app /code/app
 
-# CMD ["uvicorn", "app.main:app",  "--proxy-headers", "--host", "0.0.0.0", "--port", "8000", "--reload"]
+
+## CPU BUILD
+FROM python:3.8 as cpu
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY ./app /code/app

machine_learning/app/main.py

@@ -1,31 +1,40 @@
 from typing import Optional
 
 from fastapi import FastAPI
-from tensorflow.keras.applications.vgg16 import VGG16
-from tensorflow.keras.preprocessing import image
-from tensorflow.keras.applications.vgg16 import preprocess_input, decode_predictions
-import numpy as np
-import os
+from tensorflow.keras.applications import InceptionV3
+from tensorflow.keras.applications.inception_v3 import preprocess_input, decode_predictions
 
+from tensorflow.keras.preprocessing import image
+import numpy as np
+
+IMG_SIZE = 299
+PREDICTION_MODEL = InceptionV3(weights='imagenet')
 app = FastAPI()
 
 
 @app.get("/")
 def read_root():
 
-    return os.getcwd()
+    return "ok"
 
 
-@app.get("/predict")
-def predict():
-    model = VGG16(weights='imagenet', include_top=False)
+@app.get("/predict/{image_name}")
+def predict(image_name: str):
 
-    img_path = './app/upload/db6e94e1-ab1d-4ff0-a3b7-ba7d9e7b9d84/thumb/76f0308a9a53080e/zMGrPdjRoOU.jpeg'
-    img = image.load_img(img_path, target_size=(224, 224))
+    img_path = f'./app/upload/eb077301-2773-4ef1-aa2a-215ceb8a4383/thumb/C37F22E9-5F07-4DCA-A3EB-5BCCE93270C0/{image_name}'
+    img = image.load_img(img_path, target_size=(IMG_SIZE, IMG_SIZE))
     x = image.img_to_array(img)
     x = np.expand_dims(x, axis=0)
     x = preprocess_input(x)
 
-    features = model.predict(x)
+    preds = PREDICTION_MODEL.predict(x)
+    result = decode_predictions(preds, top=3)[0]
+    payload = []
+    for _, value, _ in result:
+        payload.append(value)
 
-    return "ok"
+    return payload
+
+
+def setup():
+    pass

machine_learning/requirements.txt

@@ -1,6 +1,6 @@
 fastapi>=0.68.0,<0.69.0
 pydantic>=1.8.0,<2.0.0
 uvicorn>=0.15.0,<0.16.0
-# tensorflow==2.2.0
-# numpy==1.22.2
+tensorflow==2.8.0
+numpy==1.22.2
 pillow==9.0.1