Dockerfile

# Hugging Face Docker Space — runs as user 1000, app must listen on 7860.
FROM python:3.11-slim

RUN useradd -m -u 1000 user
USER user
ENV HOME=/home/user \
    PATH=/home/user/.local/bin:$PATH \
    HF_HOME=/home/user/.cache/huggingface
WORKDIR /home/user/app

COPY --chown=user requirements.txt .
RUN pip install --user --no-cache-dir -r requirements.txt

COPY --chown=user app.py .

# Bake the int8 ONNX model + tokenizer into the image (no cold pull on first
# request) and assert it loads, pools to 1024-dim, and emits unit-norm vectors.
RUN python -c "\
import numpy as np, onnxruntime as ort; \
from huggingface_hub import hf_hub_download; \
from transformers import AutoTokenizer; \
m='libryo-ai/BAAI-bge-m3-int8'; \
tok=AutoTokenizer.from_pretrained(m); \
sess=ort.InferenceSession(hf_hub_download(m,'model.onnx'),providers=['CPUExecutionProvider']); \
names={i.name for i in sess.get_inputs()}; \
e=tok(['ભાવ સમાચાર','mandi prices'],padding=True,truncation=True,max_length=512,return_tensors='np'); \
f={n:(e[n] if n in e else np.zeros_like(e['input_ids'])) for n in names}; \
h=sess.run(None,f)[0]; \
c=h[:,0]; c=c/np.linalg.norm(c,axis=1,keepdims=True); \
assert c.shape==(2,1024), c.shape; \
assert abs(float(np.linalg.norm(c[0]))-1.0)<1e-3; \
print('model ok', c.shape)"

EXPOSE 7860
CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]