Dockerfile

# BMI Predictor - Docker Containerization
# Multi-stage build for optimized image size

# Stage 1: Dependencies
FROM python:3.11-slim as builder

WORKDIR /app

# Install build dependencies
RUN apt-get update && apt-get install -y --no-install-recommends \
    gcc \
    build-essential \
    && rm -rf /var/lib/apt/lists/*

# Copy and install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir --user -r requirements.txt

# Stage 2: Runtime
FROM python:3.11-slim

WORKDIR /app

# Copy installed packages from builder
COPY --from=builder /root/.local /root/.local

# Make sure scripts in .local are usable
ENV PATH=/root/.local/bin:$PATH

# Copy application code and model
COPY dashboard/ dashboard/
COPY models/ models/

# Set environment variables
ENV PYTHONUNBUFFERED=1
ENV MODEL_PATH=/app/models/models_exported/bmi_model.pkl
ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
ENV STREAMLIT_SERVER_PORT=8501

# Health check
HEALTHCHECK --interval=30s --timeout=10s --start-period=5s --retries=3 \
    CMD python -c "import urllib.request; urllib.request.urlopen('http://localhost:8501/_stcore/health')" || exit 1

# Expose Streamlit port
EXPOSE 8501

# Run Streamlit
CMD ["streamlit", "run", "dashboard/app.py", "--server.address=0.0.0.0", "--server.port=8501"]