Update app.py

app.py

@@ -61,8 +61,8 @@ def train_model(df):
     train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)
     
     # Load your pre-trained model and tokenizer from Hugging Face
-    tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-multilingual-base")
-    model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base")
+    tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True)
+    model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True)
 
     # Add your training code here
     # This may involve tokenizing the data and feeding it into the model
@@ -71,8 +71,8 @@ def train_model(df):
 # Define the Gradio interface function
 def predict(input_text):
     # Load the model and tokenizer
-    tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-multilingual-base")
-    model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base")
+    tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True)
+    model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True)
     
     # Tokenize input and make predictions
     inputs = tokenizer(input_text, return_tensors="pt")
@@ -108,6 +108,3 @@ if __name__ == "__main__":
     else:
         print("Failed to build the Gradio interface. Please check the dataset and model.")
 
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-
-