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| import os | |
| os.system("pip install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu") | |
| os.system("pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.12.0+cpu.html") | |
| import gradio as gr | |
| from glycowork.ml.processing import dataset_to_dataloader | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| from glycowork.motif.graph import glycan_to_nxGraph | |
| import networkx as nx | |
| import pydot | |
| # import pygraphviz as pgv | |
| class EnsembleModel(nn.Module): | |
| def __init__(self, models): | |
| super().__init__() | |
| self.models = models | |
| def forward(self, data): | |
| # Check if GPU available | |
| device = "cpu" | |
| if torch.cuda.is_available(): | |
| device = "cuda:0" | |
| # Prepare data | |
| x = data.labels.to(device) | |
| edge_index = data.edge_index.to(device) | |
| batch = data.batch.to(device) | |
| y_pred = [model(x,edge_index, batch).cpu().detach().numpy() for model in self.models] | |
| y_pred = np.mean(y_pred,axis=0)[0] | |
| return y_pred | |
| class_list=['Amoebozoa', 'Animalia', 'Bacteria', 'Bamfordvirae', 'Chromista', 'Euryarchaeota', 'Excavata', 'Fungi', 'Heunggongvirae', | |
| 'Orthornavirae', 'Pararnavirae', 'Plantae', 'Proteoarchaeota', 'Protista', 'Riboviria'] | |
| model1 = torch.load("model1.pt", map_location=torch.device('cpu')) | |
| model2 = torch.load("model2.pt", map_location=torch.device('cpu')) | |
| model3 = torch.load("model3.pt", map_location=torch.device('cpu')) | |
| model4 = torch.load("model4.pt", map_location=torch.device('cpu')) | |
| model5 = torch.load("model5.pt", map_location=torch.device('cpu')) | |
| model6 = torch.load("model6.pt", map_location=torch.device('cpu')) | |
| model7 = torch.load("model7.pt", map_location=torch.device('cpu')) | |
| def fn(glycan, model): | |
| # Draw graph | |
| #graph = glycan_to_nxGraph(glycan) | |
| #node_labels = nx.get_node_attributes(graph, 'string_labels') | |
| #labels = {i:node_labels[i] for i in range(len(graph.nodes))} | |
| #graph = nx.relabel_nodes(graph, labels) | |
| #graph = nx.drawing.nx_pydot.to_pydot(graph) | |
| #graph.set_prog("dot") | |
| #graph.write_png("graph.png") | |
| # write_dot(graph, "graph.dot") | |
| # graph=pgv.AGraph("graph.dot") | |
| # graph.layout(prog='dot') | |
| # graph.draw("graph.png") | |
| # Perform inference | |
| if model == "No data augmentation": | |
| model_pred = model1 | |
| model_pred.eval() | |
| elif model == "Classical Ensemble": | |
| model_pred = model3 | |
| model_pred.eval() | |
| elif model == "Bagging ensemble": | |
| model_pred = model4 | |
| model_pred.eval() | |
| elif model == "Random edge deletion": | |
| model_pred = model5 | |
| model_pred.eval() | |
| elif model == "Hierarchy substitution": | |
| model_pred = model6 | |
| model_pred.eval() | |
| elif model == "Adjusted class weights": | |
| model_pred = model7 | |
| model_pred.eval() | |
| else: | |
| model_pred = model2 | |
| model_pred.eval() | |
| glycan = [glycan] | |
| label = [0] | |
| data = next(iter(dataset_to_dataloader(glycan, label, batch_size=1))) | |
| if model in ["Ensemble", "Bootstrap ensemble"]: | |
| pred = model_pred(data) | |
| else: | |
| device = "cpu" | |
| x = data.labels | |
| edge_index = data.edge_index | |
| batch = data.batch | |
| x = x.to(device) | |
| edge_index = edge_index.to(device) | |
| batch = batch.to(device) | |
| pred = model_pred(x,edge_index, batch).cpu().detach().numpy()[0] | |
| pred = np.exp(pred)/sum(np.exp(pred)) # Softmax | |
| pred = [float(x) for x in pred] | |
| pred = {class_list[i]:pred[i] for i in range(15)} | |
| return pred | |
| demo = gr.Interface( | |
| fn=fn, | |
| inputs=[gr.Textbox(label="Glycan sequence", value="Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc"), gr.Radio(label="Model",choices=["No data augmentation", "Random node deletion", "Random edge deletion", "Ensemble", "Bootstrap ensemble", "Hierarchy substitution", "Adjusted class weights"])], | |
| outputs=[gr.Label(num_top_classes=15, label="Prediction")], | |
| allow_flagging="never", | |
| title="SweetNet demo", | |
| examples=[ | |
| ["D-Rha(b1-2)D-Rha(b1-2)Gal(b1-4)[Glc(b1-2)]GlcAOMe", "Random node deletion"], | |
| ["Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3)GalNAc", "No data augmentation"], | |
| ["Kdo(a2-4)[Kdo(a2-8)]Kdo(a2-4)Kdo", "Classical ensemble"], | |
| ["Galf(b1-6)Galf(b1-5)Galf(b1-6)Galf", "Bagging Ensemble"], | |
| ["GlcNAc(b1-2)Rha(a1-2)Rha(b1-3)Rha(a1-3)GlcNAc", "Random edge deletion"], | |
| ["Pse(b2-6)Glc(b1-6)Gal(b1-3)GalNAc(b1-3)[Glc(b1-6)]Gal(b1-3)GalNAc", "Adjusted class weights"], | |
| ] | |
| ) | |
| demo.launch(debug=True) |