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import gradio as gr |
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import py3Dmol |
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from Bio.PDB import * |
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import numpy as np |
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from Bio.PDB import PDBParser |
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import pandas as pd |
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import os, sys |
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print('importing...') |
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from run_gfn import run_gfn2 |
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print('done') |
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resid_hover = """function(atom,viewer) {{ |
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if(!atom.label) {{ |
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atom.label = viewer.addLabel('{0}:'+atom.atom+atom.serial, |
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{{position: atom, backgroundColor: 'mintcream', fontColor:'black'}}); |
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}} |
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}}""" |
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hover_func = """ |
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function(atom,viewer) { |
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if(!atom.label) { |
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atom.label = viewer.addLabel(atom.interaction, |
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{position: atom, backgroundColor: 'black', fontColor:'white'}); |
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} |
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}""" |
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unhover_func = """ |
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function(atom,viewer) { |
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if(atom.label) { |
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viewer.removeLabel(atom.label); |
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delete atom.label; |
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} |
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}""" |
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def predict(input_id, input_file): |
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input_f = open(input_file.name, "r").read() |
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input_format = input_file.name.split('.')[-1] |
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gfn2_output = run_gfn2(["filename","geom=cyclohexane."+input_format]) |
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print(gfn2_output) |
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charges = gfn2_output["fetchatomicprops"]["charges"] |
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view = py3Dmol.view(width=600, height=400) |
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view.setBackgroundColor('white') |
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view.addModel(input_f, input_format) |
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view.setStyle({'stick': {'colorscheme': {'prop': 'resi', 'C': 'turquoise'}}}) |
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view.zoomTo() |
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output = view._make_html().replace("'", '"') |
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print("got a view") |
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x = f"""<!DOCTYPE html><html> {output} </html>""" |
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return f"""<iframe style="width: 100%; height:420px" name="result" allow="midi; geolocation; microphone; camera; |
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display-capture; encrypted-media;" sandbox="allow-modals allow-forms |
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allow-scripts allow-same-origin allow-popups |
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allow-top-navigation-by-user-activation allow-downloads" allowfullscreen="" |
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allowpaymentrequest="" frameborder="0" srcdoc='{x}'></iframe>""" |
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print('run 1') |
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callback = gr.CSVLogger() |
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with gr.Blocks() as demo: |
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gr.Markdown("# Protein Adaptability Prediction") |
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inp = gr.Textbox(placeholder="PDB Code or upload file below", label="Input structure") |
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pdb_file = gr.File(label="PDB File Upload") |
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single_btn = gr.Button(label="Run") |
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with gr.Row(): |
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html = gr.HTML() |
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with gr.Row(): |
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dataframe = gr.Dataframe() |
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single_btn.click(fn=predict, inputs=[inp, pdb_file], outputs=[html]) |
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demo.launch(server_name="0.0.0.0", server_port=7860) |
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