Dockerfile

# Use Python 3.9 with CUDA support for better performance
FROM nvidia/cuda:11.8-devel-ubuntu20.04

# Set environment variables
ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1
ENV DEBIAN_FRONTEND=noninteractive

# Install system dependencies
RUN apt-get update && apt-get install -y \
    python3.9 \
    python3.9-dev \
    python3-pip \
    wget \
    curl \
    git \
    libglib2.0-0 \
    libsm6 \
    libxext6 \
    libxrender-dev \
    libgomp1 \
    libgl1-mesa-glx \
    libglib2.0-0 \
    libgtk-3-0 \
    && rm -rf /var/lib/apt/lists/*

# Create symbolic link for python
RUN ln -s /usr/bin/python3.9 /usr/bin/python

# Set working directory
WORKDIR /app

# Copy requirements first for better Docker layer caching
COPY requirements.txt .

# Install Python dependencies
RUN pip3 install --no-cache-dir --upgrade pip
RUN pip3 install --no-cache-dir -r requirements.txt

# Create necessary directories
RUN mkdir -p app/models app/static app/templates data

# Download SAM model weights
RUN wget -O app/models/sam_vit_h_4b8939.pth \
    https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth

# Copy application code
COPY app/ ./app/
COPY run.py .

# Create .env file with default values
RUN echo 'APP_NAME="AI Disaster Mapping API"' > .env && \
    echo 'DEBUG=True' >> .env && \
    echo 'MODEL_PATH="app/models/sam_vit_h_4b8939.pth"' >> .env && \
    echo 'MODEL_TYPE="vit_h"' >> .env

# Expose port
EXPOSE 8080

# Health check
HEALTHCHECK --interval=30s --timeout=30s --start-period=60s --retries=3 \
    CMD curl -f http://localhost:8080/health || exit 1

# Run the application
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8080"]