Update app.py

app.py

@@ -362,28 +362,28 @@ def clean_text(text,doc=False,plain_text=False,url=False):
         
 
   
-@st.cache(allow_output_mutation=True,suppress_st_warning=True)
+@st.experimental_singleton(suppress_st_warning=True)
 def get_spacy():
     nlp = en_core_web_lg.load()
     return nlp
     
-@st.cache(allow_output_mutation=True,suppress_st_warning=True)
+@st.experimental_singleton(suppress_st_warning=True)
 def facebook_model():
     
     summarizer = pipeline('summarization',model='facebook/bart-large-cnn')
     return summarizer
     
-@st.cache(allow_output_mutation=True,suppress_st_warning=True)
+@st.experimental_singleton(suppress_st_warning=True)
 def schleifer_model():
     
     summarizer = pipeline('summarization',model='sshleifer/distilbart-cnn-12-6')
     return summarizer
     
-@st.cache(allow_output_mutation=True,suppress_st_warning=True)
+@st.experimental_singleton(suppress_st_warning=True)
 def get_sentence_embedding_model():
     return SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
     
-@st.cache(allow_output_mutation=True,suppress_st_warning=True)
+@st.experimental_singleton(suppress_st_warning=True)
 def get_ner_pipeline():
     tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-large-finetuned-conll03-english")
     model = AutoModelForTokenClassification.from_pretrained("xlm-roberta-large-finetuned-conll03-english")