Dockerfile

# ==============================================================================
# Dockerfile - Production Container for Render Deployment (Anvaya Speech AI)
# ==============================================================================
FROM python:3.11-slim-bookworm

# Set environment variables
ENV PYTHONUNBUFFERED=1 \
    PYTHONDONTWRITEBYTECODE=1 \
    DEBIAN_FRONTEND=noninteractive \
    PORT=8501 \
    STREAMLIT_SERVER_HEADLESS=true \
    STREAMLIT_SERVER_ENABLE_CORS=false \
    STREAMLIT_SERVER_ENABLE_XSRF_PROTECTION=false \
    STREAMLIT_SERVER_ENABLE_WEBSOCKET_COMPRESSION=false \
    STREAMLIT_SERVER_MAX_UPLOAD_SIZE=50

# Install required system audio and compilation libraries
RUN apt-get update && apt-get install -y --no-install-recommends \
    build-essential \
    libsndfile1 \
    ffmpeg \
    curl \
    git \
    && rm -rf /var/lib/apt/lists/*

# Set working directory
WORKDIR /app

# Install CPU-optimized PyTorch first (reduces image size from ~4GB to ~600MB)
RUN pip install --no-cache-dir --upgrade pip && \
    pip install --no-cache-dir torch torchaudio --index-url https://download.pytorch.org/whl/cpu

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

# Copy application codebase
COPY . /app

# Pre-cache base models during build so the application starts instantly in production
RUN python ml/precache_models.py

# Expose standard port
EXPOSE 8501

# Healthcheck
HEALTHCHECK CMD curl --fail http://localhost:${PORT}/_stcore/health || exit 1

# Start Streamlit binding to Render's dynamic PORT
CMD ["sh", "-c", "streamlit run webapp.py --server.port=${PORT:-8501} --server.address=0.0.0.0 --server.headless=true --browser.gatherUsageStats=false --server.enableWebsocketCompression=false"]