A simple "Be My Eyes" web app with a llama.cpp/llava backend created in about an hour using ChatGPT, Copilot, and some minor help from me, @lxe. It describes what it sees using SkunkworksAI BakLLaVA-1 model via llama.cpp and narrates the text using Web Speech API.
Inspired by Fuzzy-Search/realtime-bakllava.
You will need a machine with about ~5 GB of RAM/VRAM for the q4_k version.
(Optional) Install the CUDA toolkit:
sudo apt install nvidia-cuda-toolkitBuild llama.cpp (build instructions for various platforms at llama.cpp build):
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
mkdir build
cd build
cmake .. -DLLAMA_CUBLAS=ON # Remove the flag if CUDA is unavailable
cmake --build . --config ReleaseDownload the models from ggml_bakllava-1:
wget https://huggingface.co/mys/ggml_bakllava-1/resolve/main/mmproj-model-f16.gguf
wget https://huggingface.co/mys/ggml_bakllava-1/resolve/main/ggml-model-q4_k.gguf # Choose another quant if preferredStart the server (server options detailed here):
./bin/server -m ggml-model-q4_k.gguf --mmproj mmproj-model-f16.gguf -ngl 35 -ts 100,0 # For GPU-only, single GPU
# ./bin/server -m ggml-model-q4_k.gguf --mmproj mmproj-model-f16.gguf # For CPUClone and set up the environment:
git clone https://github.com/lxe/llavavision
cd llavavision
python3 -m venv venv
. ./venv/bin/activate
pip install -r requirements.txtCreate dummy certificates and start the server. HTTPS is required for mobile video functionality:
openssl req -newkey rsa:4096 -x509 -sha256 -days 365 -nodes -out cert.pem -keyout key.pem
flask run --host=0.0.0.0 --key key.pem --cert cert.pem --debugAccess https://your-machine-ip:5000 from your mobile device. Optionally, start a local tunnel with ngrok or localtunnel:
npx localtunnel --local-https --allow-invalid-cert --port 5000
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