Update model path in app.py

app.py

@@ -3,7 +3,6 @@ import sys
 
 import gradio as gr
 import html
-from tqdm import tqdm
 import torch
 
 from transformers import MBartForConditionalGeneration, AutoTokenizer, AutoModel, AutoModelForQuestionAnswering, AutoModelForTokenClassification, pipeline
@@ -103,21 +102,21 @@ class CustomModel(nn.Module):
 phobert_sa = AutoModel.from_pretrained("vinai/phobert-base", output_hidden_states=True)
 phobert_sa_tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base")
 phobert_sa = CustomModel(phobert_sa)
-phobert_sa.load_state_dict(torch.load('sa_model\phobert_sentiment_analysis.pth', map_location=device))
+phobert_sa.load_state_dict(torch.load('phobert_sentiment_analysis.pth', map_location=device))
 phobert_sa.to(device)
 
 ## PhoBERTv2
 phobertv2_sa = AutoModel.from_pretrained("vinai/phobert-base-v2", output_hidden_states=True)
 phobertv2_sa_tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base-v2")
 phobertv2_sa = CustomModel(phobertv2_sa)
-phobertv2_sa.load_state_dict(torch.load('sa_model\phobertv2_sentiment_analysis.pth', map_location=device))
+phobertv2_sa.load_state_dict(torch.load('phobertv2_sentiment_analysis.pth', map_location=device))
 phobertv2_sa.to(device)
 
 ## Multilingual BERT
 m_bert_sa = AutoModel.from_pretrained("google-bert/bert-base-multilingual-cased", output_hidden_states=True)
 m_bert_sa_tokenizer = AutoTokenizer.from_pretrained("google-bert/bert-base-multilingual-cased")
 m_bert_sa = CustomModel(m_bert_sa)
-m_bert_sa.load_state_dict(torch.load('sa_model\\bert_model_sentiment_analysis.pth', map_location=device))
+m_bert_sa.load_state_dict(torch.load('bert_model_sentiment_analysis.pth', map_location=device))
 m_bert_sa.to(device)
 
 # Load Q&A model