remove 3d visualization

app.py

@@ -10,7 +10,7 @@ from lightning.fabric import seed_everything
 from rfd3.engine import RFD3InferenceConfig, RFD3InferenceEngine
 from utils import download_weights
 from utils.pipelines import test_rfd3_from_notebook, unconditional_generation
-from gradio_molecule3d import Molecule3D
+#from gradio_molecule3d import Molecule3D
 
 
 download_weights()
@@ -56,8 +56,6 @@ with gr.Blocks(title="RFD3 Test") as demo:
     gen_btn = gr.Button("Run Unconditional Generation")
 
 
-
-
     # New visualize section
     with gr.Row():
         batch_dropdown = gr.Dropdown(
@@ -73,8 +71,8 @@ with gr.Blocks(title="RFD3 Test") as demo:
         visualize_btn = gr.Button("Visualize", visible=True)
         
     display_state = gr.Textbox(label="Selected Batch and Design", visible=True)
-    display_state.value = "Please Select a Batch and Design number to visualize."
-    viewer = Molecule3D(visible=True)
+    display_state.value = "Please Select a Batch and Design number to show sequence"
+    #viewer = Molecule3D(visible=True)
 
     def update_batch_choices(result):
         if result is None:
@@ -89,7 +87,6 @@ with gr.Blocks(title="RFD3 Test") as demo:
 
 
 
-
     def update_designs(batch, result):
         if batch is None:
             return gr.update(choices=[])
@@ -101,23 +98,25 @@ with gr.Blocks(title="RFD3 Test") as demo:
     batch_dropdown.change(update_designs, inputs=[batch_dropdown, gen_results], outputs=[design_dropdown])
     design_dropdown.change()
 
-    def visualize_selection(batch, design):
+    def visualize_selection(batch, design, result):
         if batch is None or design is None:
-            return gr.update(value="**Please select both batch and design.**", visible=True)
-        return gr.update(value=f"**Selected Batch: {batch}, Design: {design}**", visible=True)
-
-        
-
-    visualize_btn.click(visualize_selection, inputs=[batch_dropdown, design_dropdown], outputs=display_state)
+            return gr.update()
+        pdb_path= next(d["pdb"] for d in result if d["batch"] == int(batch) and d["design"] == int(design))
+        with open(pdb_path, 'r') as f:
+            pdb_str = f.read()
+        return gr.update(value=f"Selected Batch: {batch}, Design: {design}, saved at {pdb_str}:\n {pdb_str}", visible=True)
 
+    
+    visualize_btn.click(visualize_selection, inputs=[batch_dropdown, design_dropdown, gen_results], outputs=display_state)
 
-    def load_viewer(batch, design, result):
-        if batch is None or design is None:
-            return gr.update()
-        pdb_data = next(d["pdb"] for d in result if d["batch"] == int(batch) and d["design"] == int(design))
-        return gr.update(value=pdb_data, visible=True, reps=[{"style": "cartoon"}])  # Customize style
 
-    visualize_btn.click(load_viewer, inputs=[batch_dropdown, design_dropdown, gen_results], outputs=viewer)
+    #def load_viewer(batch, design, result):
+    #    if batch is None or design is None:
+    #        return gr.update()
+    #    pdb_data = next(d["pdb"] for d in result if d["batch"] == int(batch) and d["design"] == int(design))
+    #    return gr.update(value=pdb_data, visible=True, reps=[{"style": "cartoon"}])  # Customize style
+#
+    #visualize_btn.click(load_viewer, inputs=[batch_dropdown, design_dropdown, gen_results], outputs=viewer)
 
 if __name__ == "__main__":
     demo.launch()

issues_doc.md

@@ -1,6 +1,15 @@
+
+# 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 

utils/pipelines.py

@@ -5,7 +5,6 @@ import time
 import os
 import spaces
 import subprocess
-from Bio.PDB import MMCIFParser, PDBIO
 import gzip
 import gemmi