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b2fac69 003b97f b2fac69 003b97f b2fac69 003b97f b2fac69 003b97f b2fac69 003b97f b2fac69 003b97f b2fac69 003b97f b2fac69 2209774 b2fac69 28735f7 b2fac69 003b97f b2fac69 003b97f 28735f7 003b97f b2fac69 28735f7 | 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 58 59 60 61 62 63 64 65 66 67 68 69 70 | # Inkference — HF Docker Space for the FULL corpus (all 6 books, ~923 pages).
#
# Differences from the Book-1 Dockerfile:
# * Bakes the PREBUILT DB + FAISS index (app/deploy/seed_data) — no boot re-seed,
# so cold start is instant even for 923 pages.
# * Page scans are NOT baked (1.3 GB). They're served by redirecting to a public
# HF dataset via the INKFERENCE_IMAGES_BASE_URL Space variable.
# * Live upload (Kraken + TrOCR + confidence + Groq correction) still works.
#
# HF builds the repo-ROOT Dockerfile, so the deploy script copies this to ./Dockerfile.
FROM python:3.11-slim
RUN apt-get update && apt-get install -y --no-install-recommends \
libgl1 libglib2.0-0 && rm -rf /var/lib/apt/lists/*
RUN useradd -m -u 1000 user
WORKDIR /app
# Full pipeline deps (Kraken + TrOCR + RAG + API) so live upload works.
COPY app/deploy/requirements-space.txt .
RUN pip install --no-cache-dir -r requirements-space.txt
COPY app/pyproject.toml ./
COPY app/src/ ./src/
COPY app/frontend/ ./frontend/
RUN pip install --no-cache-dir -e . --no-deps && \
mkdir -p /data && chown -R user:user /app /data
ENV INKFERENCE_DATA_ROOT=/data \
HF_HOME=/home/user/.cache/huggingface \
PORT=7860 \
INKFERENCE_LOG_LEVEL=INFO \
# recognition (live upload)
TROCR_MODEL_ID=microsoft/trocr-base-handwritten \
HTR_MAX_LONG_EDGE=1600 \
HTR_NUM_BEAMS=1 \
# post-correction via Groq (GROQ_API_KEY secret)
CORRECTION_ENABLED=true \
CORRECTION_BACKEND=api \
CORRECTION_API_BASE=https://api.groq.com/openai/v1 \
CORRECTION_API_MODEL=qwen/qwen3-32b \
# Ask-the-Archive: Groq -> Gemini fallback -> extractive
LLM_PROVIDER=groq \
LLM_MODEL=openai/gpt-oss-120b \
LLM_FALLBACK=gemini:gemini-2.5-flash-lite \
RAG_USE_CORRECTED=true \
# dataset holding the prebuilt corpus (seed_data/) AND the scans (book*/forster*/)
SEED_DATASET=sajitkun125/inkference-book-images
# REQUIRED Space secrets: GROQ_API_KEY, GEMINI_API_KEY
# REQUIRED Space variable (page scans): INKFERENCE_IMAGES_BASE_URL=
# https://huggingface.co/datasets/<user>/inkference-book-images/resolve/main
# (uploaded pages save absolute paths under /data and are served directly.)
USER user
# Pre-download embedding (RAG) + base recognition (upload) models for a fast first request.
RUN python -c "from sentence_transformers import SentenceTransformer as S; S('sentence-transformers/all-MiniLM-L6-v2')"
RUN python -c "from transformers import TrOCRProcessor, VisionEncoderDecoderModel as M; TrOCRProcessor.from_pretrained('microsoft/trocr-base-handwritten'); M.from_pretrained('microsoft/trocr-base-handwritten')"
# Fetch the prebuilt DB + FAISS index from the dataset. snapshot_download RESOLVES LFS
# to real bytes (unlike the Space's Docker build, which ships LFS as pointers). Only the
# seed_data/ folder is pulled — the 1.3 GB scans stay remote and stream via the CDN.
RUN python -c "import os; from huggingface_hub import snapshot_download; \
snapshot_download(os.environ['SEED_DATASET'], repo_type='dataset', \
allow_patterns=['seed_data/*'], local_dir='/app/hub')"
EXPOSE 7860
# Copy the prebuilt corpus into the (ephemeral) data root on boot, then serve. No re-seed.
CMD ["sh", "-c", "cp -rn /app/hub/seed_data/. /data/ 2>/dev/null || true; uvicorn inkference.api.main:app --host 0.0.0.0 --port ${PORT:-7860}"]
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