Dockerfile

# Base image with GPU support and TensorFlow pre-installed
FROM tensorflow/tensorflow:2.15.0-gpu

# Install system dependencies
RUN apt-get update && apt-get install -y \
    git \
    wget \
    && rm -rf /var/lib/apt/lists/*

# Install system-level dependencies for OpenCV
RUN apt-get update && apt-get install -y \
    libglib2.0-0 \
    libsm6 \
    libxext6 \
    libxrender-dev \
    libgl1-mesa-glx \
    && rm -rf /var/lib/apt/lists/*

# Install Python packages excluding blinker
RUN pip install --no-cache-dir --ignore-installed \
    torch \
    torchvision \
    transformers \
    requests \
    Flask \
    Pillow \
    huggingface_hub \
    tensorflow==2.15.0 \
    tensorflow_hub \
    opencv-python

# Set Hugging Face cache to a guaranteed writable directory
ENV TRANSFORMERS_CACHE=/app/cache
ENV HF_HOME=/app/cache
RUN mkdir -p /app/cache && chmod -R 777 /app/cache

# Set up CUDA environment variables
ENV CUDA_VISIBLE_DEVICES=0
ENV NVIDIA_VISIBLE_DEVICES=all
ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility

# Create directories for the models
RUN mkdir -p /models/blip /models/clip

# Python script to download models using tensorflow_hub and huggingface_hub
RUN echo "import os\n\
import tensorflow_hub as hub\n\n\
# Download MoveNet model from TensorFlow Hub (loaded directly in app, not saved)\n\
movenet_model = hub.load('https://tfhub.dev/google/movenet/singlepose/lightning/4')\n\n\
# Download BLIP model and tokenizer using huggingface_hub\n\
from transformers import BlipForConditionalGeneration, BlipProcessor\n\
BlipForConditionalGeneration.from_pretrained('Salesforce/blip-image-captioning-base').save_pretrained('/models/blip')\n\
BlipProcessor.from_pretrained('Salesforce/blip-image-captioning-base').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-base-patch32').save_pretrained('/models/clip')\n\
CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32').save_pretrained('/models/clip')" > download_models.py

# Run the script to download models
RUN python download_models.py

# Copy the inference script (app.py) into the container
COPY app.py /app/app.py

# Expose the default port for Flask
EXPOSE 7860

# Run the Flask app
CMD ["python", "/app/app.py"]