issues_doc.md

Issue 1: redownloading model weights at every restart/ code upload

• I'm trying to make it so that pushing new code (editing app.py) and building again does not need to redownload the weights
• In a space like BoltzGen, all the installation happens in requirements.txt. downloading Boltzgen downloads the weights. This means that when I push app.py code, HF detects that requirements.txt did not change, it pulls the built image from cache and there is no need for redownload
• In my space, this is not the case, pip install rc-foundry is cached (from requirements.txt) but I need to run the command "foundry install rfd3 ligandmpnn rf3" to download the weights. I do the installation in the header of app.py. This means that the installation reruns every code push to app.py
• I considered fixing this using Docker instead of Gradio as I could create a Docker image with the weights downloaded but Docker is not compatible with ZeroGPU, needed for the hackathon
• I tried to use persistent storage using ./data to store the weights but persistent storage is only available at runtime not during build so I get a "Permission Denied" error.
• This problem is annoying for development but not really once the space is done and is being used as there will be no more code pushes.

Issue 2: not able to integrate gradio_molecule3D to visualize outputs

• I wanted to visualize the output of RFD3 in a widget in the HF space. I got it to work on a minimal example with a custom pdb file (not using RFD3)
• once I tried to integrate the 2, it wouldnt work because gradio_molecule3d and rc-foundry require conflicting versions of gradio: gradio_molecule3d requires <6 while rc-foundry requires 6.5.1.
• pinning gradio version manually did not work, gradio_Model3D does not support PDB and I couldnt get py3Dmol working