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# PsyPredict — Backend Dockerfile for Hugging Face Spaces
#
# Architecture:
# - LLM inference via Groq API (no Ollama needed)
# - FastAPI app served by Uvicorn on port 7860 (HF Spaces standard port)
# - ML assets (Keras face model + CSV) downloaded at BUILD time via gdown
# - DistilBERT + Crisis classifier downloaded at BUILD time from HF Hub
# - HF_HUB_OFFLINE=1 at runtime so the container starts offline-capable
# ─────────────────────────────────────────────────────────────────────────────
FROM python:3.10-slim
WORKDIR /app
# ── 1. System dependencies ────────────────────────────────────────────────────
# libgl1 + libglib2.0-0: OpenCV headless needs these
RUN apt-get update && apt-get install -y --no-install-recommends \
libgl1 \
libglib2.0-0 \
curl \
ca-certificates \
&& rm -rf /var/lib/apt/lists/*
# ── 2. PyTorch CPU-only (separate layer — ~800MB, caches very well) ───────────
RUN pip install --no-cache-dir \
torch --index-url https://download.pytorch.org/whl/cpu
# ── 3. Install remaining Python dependencies ──────────────────────────────────
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# ── 4. Copy application source code ──────────────────────────────────────────
COPY . .
# ── 5. Download ML assets at BUILD time ──────────────────────────────────────
# Downloads:
# - app/ml_assets/emotion_model_trained.h5 (Keras CNN face model, ~4MB, Google Drive)
# - app/ml_assets/MEDICATION.csv (remedy database, Google Drive)
# - app/ml_assets/distilbert_model/ (DistilBERT emotion classifier, ~260MB, HF Hub)
# - app/ml_assets/crisis_model/ (MiniLM zero-shot classifier, ~130MB, HF Hub)
ENV HF_HUB_OFFLINE=0
RUN python download_models.py
# ── 6. Runtime environment ────────────────────────────────────────────────────
ENV PYTHONPATH=/app
ENV OLLAMA_TIMEOUT_S=30
ENV OLLAMA_RETRIES=3
ENV HF_HUB_OFFLINE=1
ENV LOG_LEVEL=INFO
ENV RATE_LIMIT=30/minute
# ── 7. Expose HF Spaces standard port ────────────────────────────────────────
EXPOSE 7860
# ── 8. Launch FastAPI directly (no Ollama needed) ────────────────────────────
CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "7860", "--workers", "1", "--log-level", "info"]
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