Update app.py

app.py

@@ -16,12 +16,11 @@ def lex_rank(in_text, threshold=None , model_in = 'KBLab/sentence-bert-swedish-c
 
     model = SentenceTransformer(model_in)
     #Split the document into sentences
-    sentences = nltk.sent_tokenize(document, language=language)
-    print("1")
+    sentences = nltk.sent_tokenize(in_text, language=language)
+    
     #Compute the sentence embeddings
     embeddings = model.encode(sentences, convert_to_tensor=True)
     cos_scores = util.cos_sim(embeddings, embeddings).cpu().numpy()
-    print("2")
 
     #Compute the centrality for each sentence
     centrality_scores = degree_centrality_scores(cos_scores, threshold=threshold)