b392bc3570
Create libs/ai-services/ with FastAPI app providing: - POST /avm/predict — XGBoost-backed property price prediction (heuristic fallback) - POST /avm/extract-features — Vietnamese NLP feature extraction from listing text - POST /moderation/check — content moderation with rule-based flagging - GET /health — health check endpoint Includes Dockerfile (Python 3.12), docker-compose integration, Pydantic models, and 9 passing tests covering all endpoints. Co-Authored-By: Paperclip <noreply@paperclip.ing>
32 lines
925 B
Docker
32 lines
925 B
Docker
FROM python:3.12-slim
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WORKDIR /app
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# Install system deps for underthesea / numpy
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RUN apt-get update && \
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apt-get install -y --no-install-recommends gcc g++ && \
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rm -rf /var/lib/apt/lists/*
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COPY pyproject.toml .
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RUN pip install --no-cache-dir . 2>/dev/null || pip install --no-cache-dir \
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"fastapi>=0.115.0" \
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"uvicorn[standard]>=0.32.0" \
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"xgboost>=2.1.0" \
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"numpy>=1.26.0" \
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"underthesea>=6.8.0" \
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"pydantic>=2.9.0" \
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"pydantic-settings>=2.5.0" \
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"httpx>=0.27.0"
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COPY app/ ./app/
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# Pre-download underthesea models at build time
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RUN python -c "from underthesea import word_tokenize; word_tokenize('test')" 2>/dev/null || true
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EXPOSE 8000
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HEALTHCHECK --interval=30s --timeout=5s --start-period=15s --retries=3 \
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CMD python -c "import httpx; httpx.get('http://localhost:8000/health').raise_for_status()"
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CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
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