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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")
# os.system("apt-get install -y graphviz-dev")
# os.system("pip install pygraphviz")
import gradio as gr
from glycowork.ml.processing import dataset_to_dataloader
import numpy as np
import torch
import torch.nn as nn
from networkx.drawing.nx_agraph import write_dot
# import pygraphviz as pgv
# from glycowork.motif.graph import glycan_to_nxGraph
# import networkx as nx
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'))
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)
# 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 == "Ensemble":
model_pred = model3
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 == "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"), gr.Radio(label="Model",choices=["No data augmentation", "Random node deletion", "Ensemble"])],
outputs=[gr.Label(num_top_classes=15, label="Prediction")],
allow_flagging=False,
title="SweetNet demo",
examples=[["GlcOSN(a1-4)GlcA(b1-4)GlcOSN(a1-4)GlcAOS(b1-4)GlcOSN(a1-4)GlcOSN", "No data augmentation"],
["Man(a1-2)Man(a1-3)[Man(a1-3)Man(a1-6)]Man(b1-4)GlcNAc(b1-4)GlcNAc", "Random node deletion"],
["Man(a1-2)Man(a1-3)[Man(a1-6)]Man(a1-6)[Man(a1-2)Man(a1-2)Man(a1-3)]Man(b1-4)GlcNAc", "Ensemble"]]
)
demo.launch(debug=True) |