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| # ============================================================ | |
| # Arabic Nativity Classifier — Multi-stage Docker Build | |
| # ============================================================ | |
| # Stage 1: Install Python deps (cached unless requirements change) | |
| # Stage 2: Copy app code + models → slim runtime image | |
| # ============================================================ | |
| FROM python:3.11-slim AS base | |
| # Avoid interactive prompts, set locale | |
| ENV DEBIAN_FRONTEND=noninteractive \ | |
| PYTHONUNBUFFERED=1 \ | |
| PYTHONDONTWRITEBYTECODE=1 \ | |
| PIP_NO_CACHE_DIR=1 \ | |
| PIP_DISABLE_PIP_VERSION_CHECK=1 | |
| # System deps for librosa (libsndfile), soundfile, and general audio | |
| RUN apt-get update && apt-get install -y --no-install-recommends \ | |
| libsndfile1 \ | |
| ffmpeg \ | |
| libgomp1 \ | |
| curl \ | |
| && rm -rf /var/lib/apt/lists/* | |
| WORKDIR /app | |
| # ---- Stage: deps (cached layer) ---- | |
| FROM base AS deps | |
| COPY requirements.txt . | |
| RUN pip install --no-cache-dir -r requirements.txt && \ | |
| pip install --no-cache-dir gunicorn | |
| # Pre-download the HuggingFace Wav2Vec2 model so it's baked into the image | |
| # (avoids ~1.2GB download on every container start) | |
| # MUST match extract_embeddings.py model used for training embeddings | |
| RUN python -c "\ | |
| from transformers import Wav2Vec2Model, AutoFeatureExtractor; \ | |
| AutoFeatureExtractor.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'); \ | |
| Wav2Vec2Model.from_pretrained('facebook/wav2vec2-lv-60-espeak-cv-ft'); \ | |
| print('Wav2Vec2 lv-60-espeak-cv-ft cached.')" | |
| # ---- Stage: runtime ---- | |
| FROM deps AS runtime | |
| WORKDIR /app | |
| # Copy application code | |
| COPY scripts/ ./scripts/ | |
| COPY webapp/ ./webapp/ | |
| COPY manifests/embedding_dim.txt ./manifests/embedding_dim.txt | |
| COPY manifests/test_data.csv ./manifests/test_data.csv | |
| # Copy trained models (these must exist locally — they're gitignored) | |
| COPY models/nativity_model.pth ./models/nativity_model.pth | |
| COPY models/best_threshold.txt ./models/best_threshold.txt | |
| COPY models/lgbm_nativity.txt ./models/lgbm_nativity.txt | |
| COPY models/pca_transform.pkl ./models/pca_transform.pkl | |
| # Create uploads dir for audio temp files | |
| RUN mkdir -p /app/webapp/uploads | |
| # Expose Flask port (7860 for Hugging Face Spaces) | |
| EXPOSE 7860 | |
| # Health check — hit the /api/health endpoint | |
| HEALTHCHECK --interval=30s --timeout=10s --start-period=60s --retries=3 \ | |
| CMD curl -f http://localhost:7860/api/health || exit 1 | |
| # Run with gunicorn for production (2 workers, 600s timeout for long audio) | |
| CMD ["gunicorn", \ | |
| "--bind", "0.0.0.0:7860", \ | |
| "--workers", "2", \ | |
| "--threads", "4", \ | |
| "--timeout", "600", \ | |
| "--access-logfile", "-", \ | |
| "--error-logfile", "-", \ | |
| "webapp.app:app"] | |