Deploy 0cf602f4c2fecaf8c206bad7e3f4cc7b7642e444

Dockerfile

@@ -1,59 +1,95 @@
-# docqa — Document Data Extraction API
-#
-# Loads impira/layoutlm-invoices straight from the Hugging Face cache, so
-# there is no model artifact to host in this repository.
-#
-# Serves on 7860, the port this Space previously used for llama-server, so no
-# Space port setting changes are needed.
-
-FROM python:3.11-slim-bookworm
-
-# poppler-utils -> pdftotext/pdftoppm for the PDF text layer and rasterising
-# scans; tesseract-ocr -> OCR for images and scanned pages.
-RUN apt-get update && apt-get install -y --no-install-recommends \
-        poppler-utils \
-        tesseract-ocr \
-        tesseract-ocr-eng \
-        curl \
-        libgomp1 \
-    && rm -rf /var/lib/apt/lists/*
-
-WORKDIR /app
-
-COPY requirements.txt ./
-RUN pip install --no-cache-dir -r requirements.txt
-
-COPY docqa ./docqa
-ENV PYTHONPATH=/app/docqa
-
-# The model is pulled from the Hub at first start and cached under
-# /root/.cache/huggingface. Pre-fetching during build means a cold Space does
-# not serve requests until the weights are on disk.
-ARG MODEL_ID=impira/layoutlm-invoices
-RUN python -c "\
-from huggingface_hub import snapshot_download; \
-print(snapshot_download('${MODEL_ID}', \
-    allow_patterns=['*.json','*.txt','*.bin','*.safetensors','*.model'])); \
-" || echo 'prefetch failed; model will download at first start'
-
-ENV DOCX_MODEL_ID=impira/layoutlm-invoices \
-    DOCX_DEVICE=cpu \
-    DOCX_TORCH_THREADS=4 \
-    DOCX_BATCH_SIZE=8 \
-    DOCX_MAX_PAGES=3 \
-    DOCX_MAX_UPLOAD_MB=25 \
-    DOCX_CONFIDENCE_THRESHOLD=0.5 \
-    DOCX_REQUEST_TIMEOUT_S=60 \
-    DOCX_LOG_JSON=false \
-    HF_HOME=/app/.cache/huggingface \
-    PORT=7860
+# ============================================================================
+# ACTIVE: llama-server (Qwen3-0.6B Q8_0), OpenAI-compatible, port 7860
+# ============================================================================
+
+FROM ghcr.io/ggml-org/llama.cpp:server
+
+# pre-build: bake the official Q8_0 GGUF into the image (610 MB)
+RUN mkdir -p /models && \
+    curl -fL --retry 3 -o /models/Qwen3-0.6B-Q8_0.gguf \
+      "https://huggingface.co/Qwen/Qwen3-0.6B-GGUF/resolve/main/Qwen3-0.6B-Q8_0.gguf" && \
+    ls -lh /models
+
+# key lives in .env (llama-server reads the LLAMA_API_KEY env var natively)
+COPY .env /app/.env
 
 EXPOSE 7860
 
-# Warm the model first, then serve. The model loads before uvicorn binds, so a
-# passing health check means inference will actually work rather than the
-# process merely being alive.
-HEALTHCHECK --interval=30s --timeout=10s --start-period=600s --retries=6 \
+HEALTHCHECK --interval=30s --timeout=5s --start-period=120s --retries=6 \
   CMD curl -fsS http://127.0.0.1:7860/health || exit 1
 
-CMD ["sh", "-c", "python -c \"from docxextract.engine import get_engine; from docxextract.config import get_settings; s=get_settings(); get_engine(s).warmup(); print('model warm')\" && exec uvicorn docxextract.api:app --host 0.0.0.0 --port ${PORT} --workers 1 --timeout-keep-alive 65"]
+# source .env unless the key is already set (Space secret / --env-file win)
+ENTRYPOINT ["/bin/sh", "-c", "if [ -z \"$LLAMA_API_KEY\" ]; then set -a; . /app/.env; set +a; fi; exec /app/llama-server \"$@\"", "--"]
+
+# -c is TOTAL across slots, so 32768 / 2 slots = 16384 ctx per request.
+# Raise -c to 65536 if you want a full 32k per concurrent request.
+CMD ["-m", "/models/Qwen3-0.6B-Q8_0.gguf", \
+     "--host", "0.0.0.0", \
+     "--port", "7860", \
+     "-c", "32768", \
+     "-np", "2", \
+     "-t", "2", \
+     "-tb", "4", \
+     "-ctk", "q8_0", \
+     "-ctv", "q8_0", \
+     "--jinja", \
+     "--alias", "Qwen3-0.6B"]
+
+# ============================================================================
+# COMMENTED OUT: docqa — Document Data Extraction API (Python + FastAPI)
+#
+# Kept for later, not built. To restore it, delete the FROM/ENTRYPOINT/CMD above
+# and uncomment the block below. Its own README is in docqa/.
+# ============================================================================
+#
+# FROM python:3.11-slim-bookworm
+#
+# # poppler-utils -> pdftotext/pdftoppm for the PDF text layer and rasterising
+# # scans; tesseract-ocr -> OCR for images and scanned pages.
+# RUN apt-get update && apt-get install -y --no-install-recommends \
+#         poppler-utils \
+#         tesseract-ocr \
+#         tesseract-ocr-eng \
+#         curl \
+#         libgomp1 \
+#     && rm -rf /var/lib/apt/lists/*
+#
+# WORKDIR /app
+#
+# COPY requirements.txt ./
+# RUN pip install --no-cache-dir -r requirements.txt
+#
+# COPY docqa ./docqa
+# ENV PYTHONPATH=/app/docqa
+#
+# # The model is pulled from the Hub at first start and cached under
+# # /root/.cache/huggingface. Pre-fetching during build means a cold Space does
+# # not serve requests until the weights are on disk.
+# ARG MODEL_ID=impira/layoutlm-invoices
+# RUN python -c "\
+# from huggingface_hub import snapshot_download; \
+# print(snapshot_download('${MODEL_ID}', \
+#     allow_patterns=['*.json','*.txt','*.bin','*.safetensors','*.model'])); \
+# " || echo 'prefetch failed; model will download at first start'
+#
+# ENV DOCX_MODEL_ID=impira/layoutlm-invoices \
+#     DOCX_DEVICE=cpu \
+#     DOCX_TORCH_THREADS=4 \
+#     DOCX_BATCH_SIZE=8 \
+#     DOCX_MAX_PAGES=3 \
+#     DOCX_MAX_UPLOAD_MB=25 \
+#     DOCX_CONFIDENCE_THRESHOLD=0.5 \
+#     DOCX_REQUEST_TIMEOUT_S=60 \
+#     DOCX_LOG_JSON=false \
+#     HF_HOME=/app/.cache/huggingface \
+#     PORT=7860
+#
+# EXPOSE 7860
+#
+# # Warm the model first, then serve. The model loads before uvicorn binds, so a
+# # passing health check means inference will actually work rather than the
+# # process merely being alive.
+# HEALTHCHECK --interval=30s --timeout=10s --start-period=600s --retries=6 \
+#   CMD curl -fsS http://127.0.0.1:7860/health || exit 1
+#
+# CMD ["sh", "-c", "python -c \"from docxextract.engine import get_engine; from docxextract.config import get_settings; s=get_settings(); get_engine(s).warmup(); print('model warm')\" && exec uvicorn docxextract.api:app --host 0.0.0.0 --port ${PORT} --workers 1 --timeout-keep-alive 65"]

README.md

@@ -1,10 +1,9 @@
 ---
-title: Document Data Extraction
-emoji: 🧾
-colorFrom: blue
-colorTo: indigo
+title: OpenAI Chat
+emoji: 🤖
+colorFrom: purple
+colorTo: green
 sdk: docker
 pinned: false
 app_port: 7860
-license: mit
 ---