Adds Gradio app wrapper and Dockerfile

Dockerfile

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+ARG CUDA_IMAGE="12.1.1-devel-ubuntu22.04"
+FROM nvidia/cuda:${CUDA_IMAGE}
+
+# We need to set the host to 0.0.0.0 to allow outside access
+ENV HOST 0.0.0.0
+
+RUN apt-get update && apt-get upgrade -y \
+    && apt-get install -y git build-essential \
+    python3 python3-pip gcc wget \
+    ocl-icd-opencl-dev opencl-headers clinfo \
+    libclblast-dev libopenblas-dev \
+    && mkdir -p /etc/OpenCL/vendors && echo "libnvidia-opencl.so.1" > /etc/OpenCL/vendors/nvidia.icd
+RUN apt-get install git -y
+COPY . .
+
+# setting build related env vars
+ENV CUDA_DOCKER_ARCH=all
+ENV LLAMA_CUBLAS=1
+
+RUN useradd -m -u 1000 user
+# Switch to the "user" user
+USER user
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH \
+    PYTHONPATH=$HOME/app \
+	PYTHONUNBUFFERED=1 \
+	GRADIO_ALLOW_FLAGGING=never \
+	GRADIO_NUM_PORTS=1 \
+	GRADIO_SERVER_NAME=0.0.0.0 \
+	GRADIO_THEME=huggingface \
+	SYSTEM=spaces
+
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+# Install dependencies
+RUN python3 -m pip install --upgrade pip && \
+    python3 -m pip install pytest cmake \
+    scikit-build setuptools fastapi uvicorn sse-starlette \
+    pydantic-settings starlette-context gradio huggingface_hub hf_transfer
+RUN python3 -m pip install requests pydantic uvicorn starlette fastapi sse_starlette starlette_context pydantic_settings
+
+
+# Install llama-cpp-python (build with cuda)
+RUN CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install git+https://github.com/lukestanley/llama-cpp-python.git@expose_json_grammar_convert_function
+
+CMD ["python3", "app.py"]
+
+# Credit to Radamés Ajna <[email protected]> for the original Dockerfile

app.py

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+from os import system as run
+from subprocess import check_output
+
+import gradio as gr
+
+# Without a GPU, we need to re-install llama-cpp-python to avoid an error.
+# We use a shell command to detect if we have an NVIDIA GPU available:
+use_gpu = True
+try:
+    command = "nvidia-debugdump --list|grep Device"
+    output = str(check_output(command, shell=True).decode())
+    if "NVIDIA" in output and "ID" in output:
+        print("NVIDIA GPU detected.")
+except Exception as e:
+    print("No NVIDIA GPU detected, using CPU. GPU check result:", e)
+    use_gpu = False
+
+if use_gpu:
+    print("GPU detected, existing GPU focused llama-cpp-python should work.")
+else:
+    print("Avoiding error by re-installing non-GPU llama-cpp-python build because no GPU was detected.")
+    run('pip uninstall llama-cpp-python -y')
+    run('pip install git+https://github.com/lukestanley/llama-cpp-python.git@expose_json_grammar_convert_function --upgrade --no-cache-dir --force-reinstall')
+    print("llama-cpp-python re-installed, will now attempt to load.")
+
+# Now chill can import llama-cpp-python without an error:
+from chill import improvement_loop
+
+
+def greet(text):
+    return str(improvement_loop(text))
+
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo.launch(max_threads=1)