Dockerfile

FROM nvidia/cuda:12.1.1-devel-ubuntu22.04

# Set environment variables
ENV PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    DEBIAN_FRONTEND=noninteractive \
    # Set cache directories to locations with write permissions
    KAGGLE_CONFIG_DIR=/app/.cache/kaggle \
    KAGGLEHUB_CACHE_FOLDER=/app/.cache/kagglehub \
    MPLCONFIGDIR=/app/.cache/matplotlib \
    TRANSFORMERS_CACHE=/app/.cache/transformers \
    HF_HOME=/app/.cache/huggingface \
    XDG_CACHE_HOME=/app/.cache \
    HOME=/app

WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    build-essential \
    python3-pip \
    python3-dev \
    git \
    wget \
    libcairo2-dev \
    pkg-config \
    libgl1 \
    libglib2.0-0 \
    libsm6 \
    libxrender1 \
    libxext6 \
    ffmpeg \
    curl \
    && rm -rf /var/lib/apt/lists/*

# Create a non-root user to run our application
RUN groupadd -r appuser && useradd -r -g appuser -m -d /app appuser

# Copy requirements first to leverage Docker cache
COPY requirements.txt .

# Install Python dependencies
RUN pip install --no-cache-dir --upgrade pip && \
    pip install --no-cache-dir -r requirements.txt && \
    pip install --no-cache-dir 'tensorflow[and-cuda]' && \
    pip install --no-cache-dir git+https://github.com/openai/CLIP.git

# Copy the whole application
COPY . .

# Create cache directories and set proper ownership
RUN mkdir -p /app/.cache/kaggle /app/.cache/kagglehub /app/.cache/matplotlib /app/.cache/transformers /app/.cache/huggingface && \
    chown -R appuser:appuser /app

# Set environment variables for GPU usage
ENV NVIDIA_VISIBLE_DEVICES=all \
    NVIDIA_DRIVER_CAPABILITIES=compute,utility

# Switch to non-root user
USER appuser

# Expose port for Streamlit
EXPOSE 8501

# Create a healthcheck
HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health || exit 1

# Set entry point
CMD yes | streamlit run app.py --server.port=8501 --server.address=0.0.0.0