LiamKhoaLe commited on
Commit
609fc09
·
1 Parent(s): 17e1736

Upd syntax

Browse files
Files changed (2) hide show
  1. Dockerfile +1 -0
  2. vlm.py +2 -3
Dockerfile CHANGED
@@ -17,6 +17,7 @@ RUN pip install --no-cache-dir -r requirements.txt
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  # Set Hugging Face cache directory to persist model downloads
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  ENV HF_HOME="/home/user/.cache/huggingface"
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  ENV SENTENCE_TRANSFORMERS_HOME="/home/user/.cache/huggingface/sentence-transformers"
 
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  # Create cache directories and ensure permissions
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  RUN mkdir -p /app/model_cache /home/user/.cache/huggingface/sentence-transformers && \
 
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  # Set Hugging Face cache directory to persist model downloads
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  ENV HF_HOME="/home/user/.cache/huggingface"
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  ENV SENTENCE_TRANSFORMERS_HOME="/home/user/.cache/huggingface/sentence-transformers"
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+ ENV MEDGEMMA_HOME="/home/user/.cache/huggingface/sentence-transformers"
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  # Create cache directories and ensure permissions
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  RUN mkdir -p /app/model_cache /home/user/.cache/huggingface/sentence-transformers && \
vlm.py CHANGED
@@ -2,8 +2,7 @@
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  import os, logging, traceback, json, base64
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  from io import BytesIO
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  from PIL import Image
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- # from huggingface_hub import InferenceClient # Render model on HF hub
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- from transformers import pipeline # Render model on transformers
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  from translation import translate_query
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  # Initialise once
@@ -19,7 +18,7 @@ def load_vlm():
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  global vlm_pipe
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  if vlm_pipe is None:
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  logger.info("⏳ Loading MedGEMMA model via Transformers pipeline...")
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- vlm_pipe = pipeline("image-to-text", model="google/medgemma-4b", use_auth_token=HF_TOKEN, device_map="auto")
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  logger.info("✅ MedGEMMA model ready.")
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  return vlm_pipe
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  import os, logging, traceback, json, base64
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  from io import BytesIO
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  from PIL import Image
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+ from huggingface_hub import InferenceClient # Render model on HF hub
 
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  from translation import translate_query
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  # Initialise once
 
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  global vlm_pipe
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  if vlm_pipe is None:
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  logger.info("⏳ Loading MedGEMMA model via Transformers pipeline...")
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+ vlm_pipe = pipeline("image-to-text", model="google/medgemma-4b-it", use_auth_token=HF_TOKEN, device_map="auto")
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  logger.info("✅ MedGEMMA model ready.")
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  return vlm_pipe
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