updating model caching

app.py

@@ -13,7 +13,6 @@ nltk.download('stopwords')
 nltk.download('brown')
 from nltk.tokenize import sent_tokenize
 nltk.download('wordnet')
-from gensim.models import KeyedVectors
 from nltk.corpus import wordnet
 import random
 
@@ -24,20 +23,13 @@ nlp = spacy.load("en_core_web_sm")
 user_agent = 'QGen/1.0 (channingfisher7@gmail.com)'
 wiki_wiki = wikipediaapi.Wikipedia(user_agent= user_agent,language='en')
 
+@st.cache(allow_output_mutation=True)
 def load_model():
     model_name = "DevBM/t5-large-squad"
     model = T5ForConditionalGeneration.from_pretrained(model_name)
     tokenizer = T5Tokenizer.from_pretrained(model_name)
     return model, tokenizer
 
-# Initialize session state for model and tokenizer
-if 'model' not in st.session_state:
-    st.session_state.model, st.session_state.tokenizer = load_model()
-
-# Use the model and tokenizer from session state
-model = st.session_state.model
-tokenizer = st.session_state.tokenizer
-
 # Function to extract keywords using combined techniques
 def extract_keywords(text):
     # Use RAKE