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 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): if model == "No data augmentation": model = model1 model.eval() elif model == "Ensemble": model = model3 model.eval() else: model = model2 model.eval() glycan = [glycan] label = [0] data = next(iter(dataset_to_dataloader(glycan, label, batch_size=1))) device = "cpu" if torch.cuda.is_available(): device = "cuda:0" 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(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 f = fn(class_list) demo = gr.Interface( fn=f, inputs=[gr.Textbox(label="Glycan sequence"), gr.Radio(label="Model",choices=["No data augmentation", "Random node deletion"])], 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)