Dockerfile

# ==========================================
# Stage 1: The Base Environment
# ==========================================
# We start with a slimmed-down Linux image that has Python 3.11 pre-installed
FROM python:3.11-slim AS base

# Prevent Python from writing .pyc files and keep stdout/stderr unbuffered for clean logging
ENV PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    PIP_NO_CACHE_DIR=off

WORKDIR /workspace

# Install system tools needed to compile certain Python ML packages
RUN apt-get update && apt-get install -y --no-install-recommends \
    build-essential \
    curl \
    && rm -rf /var/lib/apt/lists/*

# Copy your dependencies list and install them inside the image
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy your actual application code into the image
COPY app/ ./app/
COPY main.py .

COPY streamlit_app.py .

# ==========================================
# Stage 2: The Backend Runtime
# ==========================================
FROM base AS backend
RUN python -c "from sentence_transformers import SentenceTransformer; SentenceTransformer('all-MiniLM-L6-v2')"
EXPOSE 8080
CMD ["sh", "-c", "uvicorn main:app --host 0.0.0.0 --port ${PORT:-8080}"]

# ==========================================
# Stage 3: The Frontend Runtime
# ==========================================
FROM base AS frontend
EXPOSE 8501
# Point this directly to your root-level UI file!
CMD ["streamlit", "run", "streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]