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