Update app.py

app.py

@@ -9,12 +9,14 @@ from sentence_transformers import SentenceTransformer
 
 sentence_transformer_model = SentenceTransformer("sentence-transformers/all-roberta-large-v1")
 
+
 nltk.download('punkt')
 with open('testbook.json') as f:
     test_book = json.load(f)
 
 tokenizer = AutoTokenizer.from_pretrained("UNIST-Eunchan/bart-dnc-booksum")
 
+
 def load_model(model_name):
     model = AutoModelForSeq2SeqLM.from_pretrained("UNIST-Eunchan/bart-dnc-booksum")
     return model
@@ -38,6 +40,7 @@ def infer(input_ids, max_length, temperature, top_k, top_p):
     return output_sequences
 
 
+@st.cache_data
 def chunking(book_text):
     sentences = sent_tokenize(book_text)
     segments = [] 
@@ -82,14 +85,14 @@ def chunking(book_text):
 
 '''
 '''
-
+book_index = 0
 _book = test_book[book_index]['book']
 
 #prompts
 st.title("Book Summarization 📚")
 st.write("The almighty king of text generation, GPT-2 comes in four available sizes, only three of which have been publicly made available. Feared for its fake news generation capabilities, it currently stands as the most syntactically coherent model. A direct successor to the original GPT, it reinforces the already established pre-training/fine-tuning killer duo. From the paper: Language Models are Unsupervised Multitask Learners by Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever.")
 
-book_index = st.sidebar.slider("Select Book Example", value = 0,min_value = 0, max_value=4)
+#book_index = st.sidebar.slider("Select Book Example", value = 0,min_value = 0, max_value=4)
 sent = st.text_area("Text", _book[:512], height = 550)
 max_length = st.sidebar.slider("Max Length", value = 512,min_value = 10, max_value=1024)
 temperature = st.sidebar.slider("Temperature", value = 1.0, min_value = 0.0, max_value=1.0, step=0.05)