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e92408b 82625e4 e92408b ae0815f e92408b 82625e4 e92408b 82625e4 e92408b 82625e4 e92408b 82625e4 e92408b a6793fb 29488e0 a6793fb 82625e4 e92408b 82625e4 | 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 | # 1. Use an official Python base image
FROM python:3.10-slim
# 2. Install system dependencies
RUN apt-get update && apt-get install -y \
libgl1 \
libglib2.0-0 \
libgomp1 \
&& rm -rf /var/lib/apt/lists/*
# 3. Create non-root user required by Hugging Face Spaces
RUN useradd -m -u 1000 user
USER user
ENV HOME=/home/user \
PATH=/home/user/.local/bin:$PATH
# 4. Set working directory
WORKDIR $HOME/app
# 5. Copy and install requirements — install everything in one pass so the
# dependency resolver won't re-pull a CUDA torch when ultralytics is installed.
# --index-url sets PyTorch CPU wheel index as primary;
# --extra-index-url adds PyPI for all other packages.
COPY --chown=user requirements.txt .
RUN pip install --no-cache-dir \
--index-url https://download.pytorch.org/whl/cpu \
--extra-index-url https://pypi.org/simple/ \
torch torchvision \
-r requirements.txt
# 6. Pre-download backbone weights at BUILD time so they are baked into the
# image layer and never re-fetched at runtime.
# Weights are cached to $HOME/.cache/{torch,huggingface} inside the image.
RUN python - <<'EOF'
import torchvision.models as m
print("Downloading ResNet-18...")
m.resnet18(weights=m.ResNet18_Weights.DEFAULT)
print("Downloading MobileNetV3-Small...")
m.mobilenet_v3_small(weights=m.MobileNet_V3_Small_Weights.DEFAULT)
print("Downloading MobileViT-XXS...")
import timm
timm.create_model("mobilevit_xxs.cvnets_in1k", pretrained=True, num_classes=0)
print("All backbone weights cached.")
print("Downloading Depth Anything V2 Small...")
from transformers import pipeline as hf_pipeline
import torch
hf_pipeline("depth-estimation",
model="depth-anything/Depth-Anything-V2-Small-hf",
device="cpu", dtype=torch.float32)
print("Depth Anything V2 cached.")
EOF
# 7. Copy the rest of the project
COPY --chown=user . .
# 7. Expose Streamlit port
EXPOSE 7860
# 8. Run the app
CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]
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