meghnabjayakar commited on
Commit
a3d3554
·
verified ·
1 Parent(s): 4c11f97

updated model weights path

Browse files
Files changed (1) hide show
  1. app.py +4 -22
app.py CHANGED
@@ -6,31 +6,13 @@ import gradio as gr
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  from transformers import BertTokenizer, BertForSequenceClassification
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  import os
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- model_zip_path = "BioBERT_Model.zip"
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- tokenizer_zip_path = "BioBERT_Tokenizer.zip"
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- # Assume model and tokenizer are already loaded
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- def load_model_and_tokenizer():
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- if not os.path.exists('BioBERT_Model'):
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- with zipfile.ZipFile(model_zip_path, 'r') as zip_ref:
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- zip_ref.extractall('BioBERT_Model')
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- if not os.path.exists('BioBERT_Tokenizer'):
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- with zipfile.ZipFile(tokenizer_zip_path, 'r') as zip_ref:
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- zip_ref.extractall('BioBERT_Tokenizer')
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-
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- model_path = 'BioBERT_Model/content/BioBERT_Model'
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- tokenizer_path = 'BioBERT_Tokenizer/content/BioBERT_Tokenizer'
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-
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- model = BertForSequenceClassification.from_pretrained(model_path)
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- tokenizer = BertTokenizer.from_pretrained(tokenizer_path)
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-
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- return model, tokenizer
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-
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-
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- model, tokenizer = load_model_and_tokenizer()
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  device = "cpu"
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- model = model.to(device)
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  def predict_drug_target_interaction(sentence):
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  # Tokenize the input sentence
 
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  from transformers import BertTokenizer, BertForSequenceClassification
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  import os
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+ tokenizer = BertTokenizer.from_pretrained("dmis-lab/biobert-base-cased-v1.1")
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+ model = BertForSequenceClassification.from_pretrained("dmis-lab/biobert-base-cased-v1.1", num_labels=2)
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+ # loading the pretrained weights into the model
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+ model.load_state_dict(torch.load('Bio_BERT_model.pth'))
 
 
 
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  device = "cpu"
 
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  def predict_drug_target_interaction(sentence):
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  # Tokenize the input sentence