Update Dockerfile

Dockerfile

@@ -23,7 +23,7 @@ ENV TRANSFORMERS_CACHE=/tmp/cache
 RUN mkdir -p /tmp/cache
 
 # Create directories for the models
-RUN mkdir -p /models/movenet /models/motionbert
+RUN mkdir -p /models/movenet /models/blip /models/clip
 
 # Python script to download models using tensorflow_hub and huggingface_hub
 RUN echo "import os\n\
@@ -35,11 +35,15 @@ movenet_model_path = '/models/movenet/movenet_lightning'\n\
 os.makedirs(movenet_model_path, exist_ok=True)\n\
 movenet_model.save(movenet_model_path)\n\
 \n\
-# Download MotionBERT model and tokenizer using huggingface_hub\n\
-from huggingface_hub import hf_hub_download\n\
-from transformers import AutoModel, AutoTokenizer\n\
-AutoModel.from_pretrained('walterzhu/MotionBERT').save_pretrained('/models/motionbert')\n\
-AutoTokenizer.from_pretrained('walterzhu/MotionBERT').save_pretrained('/models/motionbert')" > download_models.py
+# Download BLIP model and tokenizer using huggingface_hub\n\
+from transformers import BlipForConditionalGeneration, BlipProcessor\n\
+BlipForConditionalGeneration.from_pretrained('Salesforce/blip-image-captioning-large').save_pretrained('/models/blip')\n\
+BlipProcessor.from_pretrained('Salesforce/blip-image-captioning-large').save_pretrained('/models/blip')\n\
+\n\
+# Download CLIP model and processor using huggingface_hub\n\
+from transformers import CLIPModel, CLIPProcessor\n\
+CLIPModel.from_pretrained('openai/clip-vit-large-patch14').save_pretrained('/models/clip')\n\
+CLIPProcessor.from_pretrained('openai/clip-vit-large-patch14').save_pretrained('/models/clip')" > download_models.py
 
 # Run the script to download models
 RUN python download_models.py
@@ -51,5 +55,4 @@ COPY app.py /app/app.py
 EXPOSE 7860
 
 # Run the Flask app
-CMD ["python", "/app/app.py"]
-
+CMD ["python", "/app/app.py"]