Upload 3 files

app.py

@@ -1,29 +1,44 @@
-import gradio as gr
+ import gradio as gr
+
+
 from tape import ProteinBertModel, ProteinBertConfig, TAPETokenizer  # type: ignore
 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')
+bert_model = torch.load('models/transformer1500_95p_500.pt')
+class_model=torch.load('models/down_model_500_kfold1.pt')
+
+bert_model=bert_model.module
+bert_model=bert_model.to('cpu')
+bert_model=bert_model.eval()
 
 
 
 def greet(name):
 
-    #translation_table = str.maketrans("", "", " \t\n\r\f\v")
-    name = name.replace(" ", "").replace("\n", "").replace("\t", "")
+
+
+    translation_table = str.maketrans("", "", " \t\n\r\f\v")
+    name = name.translate(translation_table)
     token_ids = torch.tensor([tokenizer.encode(name)])
- 
+    token_ids = token_ids
     bert_output = bert_model(token_ids)
     class_output=class_model(bert_output[1])
+    class_output = torch.softmax(class_output, dim=1)
     cluster = torch.argmax(class_output, dim=1) + 1
     cluster=cluster.item()
 
     return "cluster "+str(cluster)
+
+
+
+
+
 demo = gr.Interface(
     fn=greet,
     # 自定义输入框

down_model_500_kfold1.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:54ffee6f21ff8d0c743fbf8cad638ed609030e79a6359ec115005c98f0cef9c7
+size 2491739

transformer1500_95p_500.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:77877d4c8f835aacae45e311d5ecfdc180270a22bf7206a33b4a358fbdcb5d6d
+size 80270463