File size: 1,880 Bytes
b59fc2c 8c4590b 66c6f17 8c4590b b59fc2c 8c4590b b59fc2c 66c6f17 b59fc2c 8c4590b b59fc2c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 | # ── HuggingFace Spaces compatible Dockerfile ──────────────────────────────
# Port MUST be 7860 for HF Spaces.
# Runs as user 1000 (HF requirement).
FROM python:3.10-slim
# System deps
RUN apt-get update && apt-get install -y --no-install-recommends \
build-essential curl git \
&& rm -rf /var/lib/apt/lists/*
# Create non-root user (required by HF Spaces)
RUN useradd -m -u 1000 appuser
WORKDIR /app
# Install Python deps first (better layer caching)
COPY requirements_api.txt .
RUN pip install --no-cache-dir -r requirements_api.txt
# Copy application files
COPY . .
# Pin HuggingFace cache inside /app so the sentence-transformer model is
# downloaded once during `docker build` and baked into the image layer.
ENV HF_HOME=/app/.hf_cache
ENV TRANSFORMERS_CACHE=/app/.hf_cache/transformers
ENV SENTENCE_TRANSFORMERS_HOME=/app/.hf_cache/sentence_transformers
# Set ownership (includes .hf_cache written by the build step below)
RUN chown -R appuser:appuser /app
USER appuser
# Pre-build FAISS index + download the embedding model into /app/.hf_cache
RUN python src/build_faiss.py
# ── Offline mode ────────────────────────────────────────────────────────────
# Model is now cached in the image. Tell all HF libraries to NEVER call the
# network at runtime — prevents "Could not resolve host: huggingface.co" errors.
ENV TRANSFORMERS_OFFLINE=1
ENV HF_DATASETS_OFFLINE=1
ENV HF_HUB_OFFLINE=1
# Environment defaults (override via HF Space secrets)
ENV GROQ_API_KEY_1=""
ENV GROQ_API_KEY_2=""
ENV GROQ_API_KEY_3=""
ENV GROQ_MODEL="llama-3.3-70b-versatile"
ENV ALLOWED_ORIGINS="*"
ENV PORT=7860
# Expose HF Spaces port
EXPOSE 7860
CMD ["python", "api_server_fastapi.py"]
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