ignore temp files

.gitignore

@@ -158,3 +158,5 @@ cython_debug/
 #  and can be added to the global gitignore or merged into this file.  For a more nuclear
 #  option (not recommended) you can uncomment the following to ignore the entire idea folder.
 #.idea/
+
+models/

Dockerfile

@@ -15,6 +15,15 @@ RUN apt-get update && apt-get install -y \
 # Copy the current directory contents into the container at /app
 COPY . /app
 
+# RUN useradd -m -u 1000 user
+# USER user
+# ENV HOME=/home/user \
+# 	PATH=/home/user/.local/bin:$PATH
+
+# WORKDIR $HOME/app
+
+# COPY --chown=user . $HOME/app
+
 # Install any needed packages specified in requirements.txt
 RUN pip install --no-cache-dir -r requirements.txt
 

docker-compose.yml

@@ -0,0 +1,14 @@
+version: "1.0"
+
+services:
+  backend:
+    container_name: backend
+    build: .
+    working_dir: /app
+    command: uvicorn main:app --host 0.0.0.0 --port 7680 --reload
+    environment:
+      - WATCHFILES_FORCE_POLLING=true
+    ports:
+      - "80:7680"
+    restart: on-failure
+  

main.py

@@ -6,12 +6,18 @@ from fastapi import FastAPI, File, UploadFile, HTTPException
 from fastapi.responses import JSONResponse
 
 import os
-PATH = '/home/ahmadzen/.cache/huggingface'
-DATASETPATH = '/home/ahmadzen/.cache/huggingface/datasests'
-os.environ['HF_HOME'] = PATH
-os.environ['HF_DATASETS_CACHE'] = DATASETPATH
-os.environ['TORCH_HOME'] = PATH
-os.environ['HF_HUB_CACHE'] = '/home/ahmadzen/.cache/huggingface'
+from os import path
+cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
+os.environ["HF_HUB_CACHE"] = cache_path
+os.environ["HF_HOME"] = cache_path
+
+# PATH = 'huggingface'
+# DATASETPATH = '/home/ahmadzen/.cache/huggingface/datasets'
+# MODEL_PATH = '/home/ahmadzen/ViT_Deepfake_Detection/SavedModel'
+# os.environ['HF_HOME'] = PATH
+# os.environ['HF_DATASETS_CACHE'] = DATASETPATH
+# os.environ['TORCH_HOME'] = PATH
+# os.environ['HF_HUB_CACHE'] = '/home/ahmadzen/.cache/huggingface'
 
 from transformers import AutoImageProcessor, ViTForImageClassification
 from PIL import Image
@@ -34,7 +40,9 @@ cache = Cache(maxsize=1000)
 
 # Load the model using the transformers pipeline
 # model = pipeline("image-classification", model="Wvolf/ViT_Deepfake_Detection")
-
+image_processor = AutoImageProcessor.from_pretrained("Wvolf/ViT_Deepfake_Detection")
+model = ViTForImageClassification.from_pretrained("Wvolf/ViT_Deepfake_Detection")
+    
 # Detect the device used by TensorFlow
 # DEVICE = "GPU" if tf.config.list_physical_devices("GPU") else "CPU"
 # logging.info("TensorFlow version: %s", tf.__version__)
@@ -87,8 +95,6 @@ async def classify_image(file: UploadFile = File(None)):
 
         image = Image.open(io.BytesIO(image_data))
 
-        image_processor = AutoImageProcessor.from_pretrained("Wvolf/ViT_Deepfake_Detection")
-        model = ViTForImageClassification.from_pretrained("Wvolf/ViT_Deepfake_Detection")
         inputs = image_processor(image, return_tensors="pt")
 
         with torch.no_grad():
@@ -174,8 +180,6 @@ async def classify_images(request: ImageUrlsRequest):
                 continue
 
             image = Image.open(io.BytesIO(image_data))
-            image_processor = AutoImageProcessor.from_pretrained("Wvolf/ViT_Deepfake_Detection")
-            model = ViTForImageClassification.from_pretrained("Wvolf/ViT_Deepfake_Detection")
             inputs = image_processor(image, return_tensors="pt")
 
             with torch.no_grad():