| import gradio as gr |
| from tape import ProteinBertModel, ProteinBertConfig, TAPETokenizer |
| from tape.models import modeling_bert |
| import numpy as np |
| import torch |
|
|
| tokenizer = TAPETokenizer(vocab='iupac') |
| config=modeling_bert.ProteinBertConfig(num_hidden_layers=5,num_attention_heads=8,hidden_size=400) |
|
|
| bert_model = torch.load('models/bert.pt') |
| class_model=torch.load('models/class.pt') |
|
|
|
|
|
|
| def greet(name): |
| token_ids = torch.tensor([tokenizer.encode(name)]) |
| token_ids = token_ids |
| bert_output = bert_model(token_ids) |
| class_output=class_model(bert_output[1]) |
| cluster = torch.argmax(class_output, dim=1) + 1 |
| cluster=cluster.item() |
|
|
| return "cluster "+str(cluster) |
| demo = gr.Interface( |
| fn=greet, |
| |
| |
| inputs=gr.Textbox(lines=3, placeholder="Name Here...",label="my input"), |
| outputs="text", |
| ) |
| demo.launch(share=True) |
