Update app.py

app.py

@@ -121,7 +121,6 @@ nlp.add_pipe('sentencizer')
 spacy_stop_words = spacy.lang.nb.stop_words.STOP_WORDS
 
 summarization_model = AutoModel.from_pretrained("NbAiLab/nb-bert-large")
-summarization_tokenizer = AutoTokenizer.from_pretrained("NbAiLab/nb-bert-large") # <--not sure if this is needed..is not the tokenizer already part of this model..?
 # pipe = pipeline("fill-mask", model="NbAiLab/nb-bert-large")
 
 @spaces.GPU()
@@ -150,10 +149,10 @@ def summarize_text(text, file_upload):
 # ----add same if/elif logic as above here----
 #
     preprocessed_text = preprocess_text(text)
-    inputs = summarization_tokenizer(preprocessed_text, max_length=1024, return_tensors="pt", truncation=True)
+    inputs = summarization_model(preprocessed_text, max_length=1024, return_tensors="pt", truncation=True)
     inputs = inputs.to(device)
     summary_ids = summarization_model.generate(inputs.input_ids, num_beams=5, max_length=150, early_stopping=True)
-    return summarization_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summarization_model.decode(summary_ids[0], skip_special_tokens=True)
 
 @spaces.GPU()
 def build_similarity_matrix(sentences):
@@ -295,7 +294,7 @@ with iface:
             summarize_uploaded_button_graph.click(fn=graph_based_summary(file_upload), inputs=[text_input_graph], outputs=[summary_output_graph])
 
         with gr.TabItem("Summary | LexRank"):
-          with gr.Blocks():
+            with gr.Blocks():
             text_output = gr.Textbox(label="Transcription Output")
             text_input_lex = gr.Textbox(label="Input Text", placeholder="txt2summarize")
             summary_output_lex = gr.Textbox(label="LexRank | cosine similarity")