Update functions.py

functions.py

@@ -97,7 +97,7 @@ prompt = ChatPromptTemplate.from_messages(messages)
 
 ###################### Functions #######################################################################################
 
-@st.experimental_singleton(suppress_st_warning=True)
+@st.cache_resource
 def load_models():
 
     '''Load and cache all the models to be used'''
@@ -116,20 +116,20 @@ def load_models():
     
     return sent_pipe, sum_pipe, ner_pipe, cross_encoder, kg_model, kg_tokenizer, emb_tokenizer, sbert
 
-@st.experimental_singleton(suppress_st_warning=True)
+@st.cache_resource
 def load_asr_model(asr_model_name):
     asr_model = whisper.load_model(asr_model_name)
     
     return asr_model
 
-@st.experimental_singleton(suppress_st_warning=True)
+@st.cache_data
 def load_whisper_api(audio):
     file = open(audio, "rb")
     transcript = openai.Audio.translate("whisper-1", file)
 
     return transcript
 
-@st.experimental_singleton(suppress_st_warning=True)
+@st.cache_data
 def process_corpus(corpus, title, embedding_model, chunk_size=1000, overlap=50):
 
     '''Process text for Semantic Search'''
@@ -144,7 +144,7 @@ def process_corpus(corpus, title, embedding_model, chunk_size=1000, overlap=50):
 
     return docsearch
 
-@st.experimental_singleton(suppress_st_warning=True)
+@st.cache_data
 def chunk_and_preprocess_text(text,thresh=500):
     
     """Chunk text longer than n tokens for summarization"""
@@ -170,7 +170,7 @@ def chunk_and_preprocess_text(text,thresh=500):
     
     return chunks
     
-@st.experimental_singleton(suppress_st_warning=True)
+@st.cache_resource
 def gen_embeddings(embedding_model):
 
     '''Generate embeddings for given model'''
@@ -187,7 +187,7 @@ def gen_embeddings(embedding_model):
 
     return embeddings
     
-@st.experimental_memo(suppress_st_warning=True)
+@st.cache_data
 def embed_text(query,title,embedding_model,_docsearch):
     
     '''Embed text and generate semantic search scores'''
@@ -212,12 +212,12 @@ def embed_text(query,title,embedding_model,_docsearch):
 
     return answer
     
-@st.experimental_singleton(suppress_st_warning=True)
+@st.cache_resource
 def get_spacy():
     nlp = en_core_web_lg.load()
     return nlp
     
-@st.experimental_memo(suppress_st_warning=True)
+@st.cache_data
 def inference(link, upload, _asr_model):
     '''Convert Youtube video or Audio upload to text'''
     
@@ -307,7 +307,7 @@ def inference(link, upload, _asr_model):
         return results['text'], yt.title
     
       
-@st.experimental_memo(suppress_st_warning=True)
+@st.cache_data
 def sentiment_pipe(earnings_text):
     '''Determine the sentiment of the text'''
     
@@ -316,7 +316,7 @@ def sentiment_pipe(earnings_text):
     
     return earnings_sentiment, earnings_sentences    
 
-@st.experimental_memo(suppress_st_warning=True)
+@st.cache_data
 def summarize_text(text_to_summarize,max_len,min_len):
     '''Summarize text with HF model'''
     
@@ -329,7 +329,7 @@ def summarize_text(text_to_summarize,max_len,min_len):
      
     return summarized_text
      
-@st.experimental_memo(suppress_st_warning=True)
+@st.cache_data
 def clean_text(text):
     '''Clean all text'''
 
@@ -341,7 +341,7 @@ def clean_text(text):
     
     return text
        
-@st.experimental_memo(suppress_st_warning=True)
+@st.cache_data
 def chunk_long_text(text,threshold,window_size=3,stride=2):
     '''Preprocess text and chunk for sentiment analysis'''
     
@@ -378,7 +378,7 @@ def summary_downloader(raw_text):
 	href = f'<a href="data:file/txt;base64,{b64}" download="{new_filename}">Click to Download!!</a>'
 	st.markdown(href,unsafe_allow_html=True)
 
-@st.experimental_memo(suppress_st_warning=True) 	
+@st.cache_data 	
 def get_all_entities_per_sentence(text):
     doc = nlp(''.join(text))
 
@@ -401,12 +401,12 @@ def get_all_entities_per_sentence(text):
 
     return entities_all_sentences
  
-@st.experimental_memo(suppress_st_warning=True)    
+@st.cache_data 
 def get_all_entities(text):
     all_entities_per_sentence = get_all_entities_per_sentence(text)
     return list(itertools.chain.from_iterable(all_entities_per_sentence))
 
-@st.experimental_memo(suppress_st_warning=True)    
+@st.cache_data    
 def get_and_compare_entities(article_content,summary_output):
     
     all_entities_per_sentence = get_all_entities_per_sentence(article_content)
@@ -454,7 +454,7 @@ def get_and_compare_entities(article_content,summary_output):
 
     return matched_entities, unmatched_entities
 
-@st.experimental_memo(suppress_st_warning=True) 
+@st.cache_data 
 def highlight_entities(article_content,summary_output):
    
     markdown_start_red = "<mark class=\"entity\" style=\"background: rgb(238, 135, 135);\">"
@@ -506,6 +506,7 @@ def fin_ext(text):
 
 ## Knowledge Graphs code
 
+@st.cache_data 
 def extract_relations_from_model_output(text):
     relations = []
     relation, subject, relation, object_ = '', '', '', ''