Fix uv Docker workflow: copy gsplat before sync, then rebuild

- Copy gsplat source before uv sync to resolve dependencies
- Use uv sync to install all dependencies including source gsplat
- Build optimized wheel and force-reinstall it for target GPU architecture
- This ensures GPU compatibility while using proper uv workflow

Dockerfile

@@ -42,28 +42,25 @@ ENV HOME=/home/user \
     GRADIO_SERVER_PORT=7860 \
     UV_CACHE_DIR=/home/user/.cache/uv
 
-# Copy dependency files for layer caching
+# Copy dependency files and gsplat source
 COPY --chown=user pyproject.docker.toml ./pyproject.toml
+COPY --chown=user gsplat/ ./gsplat/
 
 # Set CUDA compilation flags for HF Spaces GPU compatibility
 ENV TORCH_CUDA_ARCH_LIST="6.0;6.1;7.0;7.5;8.0;8.6;8.9;9.0" \
     FORCE_CUDA=1
 
-# Install dependencies without the project first (for better caching)
+# Create virtual environment and install dependencies
 RUN --mount=type=cache,target=/tmp/uv-cache,sharing=locked,uid=1000,gid=1000 \
-    UV_CACHE_DIR=/tmp/uv-cache uv sync --no-install-project --no-dev
+    UV_CACHE_DIR=/tmp/uv-cache uv sync --no-dev
 
-# Copy gsplat source and build wheel for target architecture
-COPY --chown=user gsplat/ ./gsplat/
+# Build gsplat wheel for target architecture and reinstall it
 RUN cd gsplat && \
     uv run python setup.py bdist_wheel && \
     mkdir -p ../wheels && \
     cp dist/*.whl ../wheels/ && \
-    cd ..
-
-# Install the project with the fresh-built wheel
-RUN --mount=type=cache,target=/tmp/uv-cache,sharing=locked,uid=1000,gid=1000 \
-    UV_CACHE_DIR=/tmp/uv-cache uv sync --no-dev
+    cd .. && \
+    uv pip install wheels/gsplat-0.1.0-cp310-cp310-linux_x86_64.whl --force-reinstall
 
 # Copy the rest of the application
 COPY --chown=user . .