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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 get_qm_atom_features(gfn2_output): |
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qm_atom_features = {} |
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atom_list = gfn2_output["fetchatomicprops"]["atmlist"] |
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charge = gfn2_output["fetchatomicprops"]["charges"] |
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pol = gfn2_output["fetchatomicprops"]["polarisabilities"] |
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qm_atom_features['atom type'] = atom_list |
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qm_atom_features['Charge'] = charge |
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qm_atom_features['Polarizability'] = pol |
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return qm_atom_features |
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def get_qm_mol_features(gfn2_output): |
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qm_mol_features = {} |
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qm_mol_features['Total Energy'] = gfn2_output["etotal"] |
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qm_mol_features['Total Polarizability'] = gfn2_output["totalpol"] |
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return qm_mol_features |
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def export_csv(d): |
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d.to_csv("qm_atom_features.csv") |
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return gr.File.update(value="qm_atom_features.csv", visible=True) |
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def get_basic_visualization(input_f,input_format): |
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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('output of html', output) |
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x = f"""<!DOCTYPE html><html> {output} </html>""" |
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visualization_html = f"""<iframe style="width: 100%; height:420px" data-ui="true" 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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return visualization_html |
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def add_spheres_feature_view(view, feature,xyz, viewnum, sizefactor, spec_color): |
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normalization = max(max(feature),abs(min(feature))) |
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for i in range(len(feature)): |
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if feature[i]<0: |
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color="#a0210f" |
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else: |
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color=spec_color |
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view.addSphere({'center':{ |
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'x':xyz[i][0], |
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'y':xyz[i][1], |
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'z':xyz[i][2]}, |
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'radius':abs(feature[i])/normalization*sizefactor,'color':color,'alpha':1.00}, viewer=viewnum) |
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return view |
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def add_densities(view, dens, color, viewnum): |
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view.addVolumetricData(dens, "cube", {'isoval': 0.01, 'color': color, 'opacity': 1.0}, viewer=viewnum) |
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return view |
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def get_feature_visualization(input_f,input_format, features, xyz): |
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view = py3Dmol.view(width=620, height=620, viewergrid=(2,2)) |
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view.setBackgroundColor('white') |
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view.addModel(input_f, input_format, viewer=(0,0)) |
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view.setStyle({'stick': {'colorscheme': {'prop': 'resi', 'C': '#cccccc'}}}, viewer=(0,0)) |
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print('features', features) |
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add_spheres_feature_view(view, features["fetchatomicprops"]["charges"], xyz, (0,1), 1.0, '#4c4e9e') |
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add_spheres_feature_view(view, features["fetchatomicprops"]["polarisabilities"], xyz, (1,0), 1.0, '#809BAC') |
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add_densities(view, open('dummy_struct_dens.cub', "r").read(), '#F7D7BE', (1,1)) |
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view.zoomTo(viewer=(0,0)) |
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output = view._make_html().replace("'", '"') |
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x = f"""<!DOCTYPE html><html> {output} </html>""" |
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visualization_html = f"""<iframe style="width: 100%; height:620px" 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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return visualization_html |
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def predict(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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with open('dummy_struct.'+input_format, "w") as oF: |
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oF.write(input_f) |
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input_f2 = open('dummy_struct.'+input_format, "r").read() |
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gfn2_input = ["filename","geom=dummy_struct."+input_format, 'calcdens=1'] |
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gfn2_output = run_gfn2(gfn2_input) |
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feature_visualization_html = get_feature_visualization(input_f,input_format, gfn2_output, gfn2_output['xyz']) |
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qm_atom_features = get_qm_atom_features(gfn2_output) |
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return feature_visualization_html, pd.DataFrame(qm_atom_features) |
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with gr.Blocks() as demo: |
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gr.Markdown("# QM property calculation") |
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with gr.Row(): |
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input_file = gr.File(label="Structure file for input") |
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charge = gr.Textbox(placeholder="Total charge", label="Give the total charge of the input molecule. (Default=0)") |
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single_btn = gr.Button(label="Run") |
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with gr.Row(): |
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basic_html = gr.HTML() |
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gr.HighlightedText(value=[("Positive Charge","Purple"),("Negative charge","red"),("Polarizability","Light blue"), ("Electronic Densities", "Beige")], color_map={"red":"#a0210f", "Light blue":"#809BAC", "Purple":"#4c4e9e", "Beige":"#F7D7BE"}) |
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with gr.Row(): |
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Dbutton = gr.Button("Download calculated atom features") |
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csv = gr.File(interactive=False, visible=False) |
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with gr.Row(): |
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df_atom_features = gr.Dataframe() |
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single_btn.click(fn=predict, inputs=[input_file], outputs=[basic_html, df_atom_features]) |
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Dbutton.click(export_csv, df_atom_features, csv) |
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demo.launch(server_name="0.0.0.0", server_port=7860) |
