ignore temp files

Dockerfile

@@ -14,9 +14,9 @@ RUN apt-get update && apt-get install -y \
 
 # Copy the current directory contents into the container at /app
 COPY . /app
-RUN chown daemon:daemon -R /app/*
+# RUN chown daemon:daemon -R /app/*
 # Install any needed packages specified in requirements.txt
-RUN pip install --no-cache-dir -r requirements.txt
+# RUN pip install --no-cache-dir -r requirements.txt
 
 # Make port 7680 available to the world outside this container
 EXPOSE 7860

main.py

@@ -5,7 +5,7 @@ import aiohttp
 from fastapi import FastAPI, File, UploadFile, HTTPException
 from fastapi.responses import JSONResponse
 
-import os
+# import os
 # from os import path
 # cache_path = path.join(path.dirname(path.abspath(__file__)), "models")
 
@@ -21,7 +21,9 @@ import os
 # os.environ['TORCH_HOME'] = PATH
 # os.environ['HF_HUB_CACHE'] = '/home/ahmadzen/.cache/huggingface'
 
-from transformers import AutoImageProcessor, ViTForImageClassification
+# from transformers import AutoImageProcessor, ViTForImageClassification
+from transformers import pipeline
+from transformers.pipelines import PipelineException
 from PIL import Image
 from cachetools import Cache
 import torch
@@ -41,9 +43,9 @@ logging.basicConfig(
 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")
+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"
@@ -153,7 +155,7 @@ async def classify_image(file: UploadFile = File(None)):
 
         return FileImageDetectionResponse(**response_data)
 
-    except Exception as e:
+    except PipelineException as e:
         logging.error("Error processing image: %s", str(e))
         raise HTTPException(
             status_code=500, detail=f"Error processing image: {str(e)}"
@@ -233,7 +235,7 @@ async def classify_images(request: ImageUrlsRequest):
 
             response_data.append(detection_result)
 
-        except Exception as e:
+        except PipelineException as e:
             logging.error("Error processing image from %s: %s", image_url, str(e))
             raise HTTPException(
                 status_code=500,