Update app.py

app.py

@@ -1,19 +1,18 @@
 import gradio as gr
 import torch
-from transformers import AutoTokenizer
+from transformers import AutoTokenizer, AutoModelForTokenClassification
 
-# Define model paths
-models_path = "./models"
-tokenizer_path = f"{models_path}/el_new_tokenizer.pt"
-lemmatizer_path = f"{models_path}/el_new_nocharlm_lemmatizer.pt"
-tagger_path = f"{models_path}/el_new_transformer_tagger.pt"
-parser_path = f"{models_path}/el_new_transformer_parser.pt"  # Updated parser model path
+# Define the Hugging Face repository paths for each model
+tokenizer_repo = "viv/UD_Greek-GUD"
+lemmatizer_repo = "viv/UD_Greek-GUD"  # Adjust this path if necessary
+tagger_repo = "viv/UD_Greek-GUD"  # Adjust this path if necessary
+parser_repo = "viv/UD_Greek-GUD"  # Adjust this path if necessary
 
-# Load models
-tokenizer = torch.load(tokenizer_path)  # Load tokenizer
-lemmatizer_model = torch.load(lemmatizer_path)  # Load lemmatizer
-tagger_model = torch.load(tagger_path)  # Load POS tagger
-parser_model = torch.load(parser_path)  # Load dependency parser
+# Load models using the Hugging Face model hub
+tokenizer = AutoTokenizer.from_pretrained(tokenizer_repo)
+lemmatizer_model = torch.load("models/el_new_nocharlm_lemmatizer.pt")
+tagger_model = torch.load("models/el_new_transformer_tagger.pt")
+parser_model = torch.load("models/el_new_transformer_parser.pt")
 
 # Prediction function
 def predict(text):