Update app.py

app.py

@@ -5,7 +5,7 @@ import shap
 import requests
 from bs4 import BeautifulSoup
 from sklearn.metrics.pairwise import cosine_similarity
-from transformers import RobertaTokenizer,RobertaForSequenceClassification, pipeline
+from transformers import RobertaTokenizer,RobertaForSequenceClassification, pipeline,RobertaModel
 from IPython.core.display import HTML
 model_dir = 'temp'
 tokenizer = RobertaTokenizer.from_pretrained(model_dir)
@@ -64,8 +64,8 @@ def process_text(input_text):
             
             # Calculate embeddings using the model
                 with torch.no_grad():
-                    embedding1 = model(**encoding1).last_hidden_state.mean(dim=1)
-                    embedding2 = model(**encoding2).last_hidden_state.mean(dim=1)
+                    embedding1 = model1(**encoding1).last_hidden_state.mean(dim=1)
+                    embedding2 = model1(**encoding2).last_hidden_state.mean(dim=1)
             
             # Calculate cosine similarity between the input text and the article text embeddings
                 similarity = cosine_similarity(embedding1, embedding2)[0][0]