Update app.py

app.py

@@ -52,7 +52,7 @@ import line_cor
 import altair as alt
 #pytesseract.pytesseract.tesseract_cmd = r"./Tesseract-OCR/tesseract.exe"
 from PIL import Image
-@st.experimental_singleton
+
 def read_pdf(file):
 #     images=pdf2image.convert_from_path(file)
 #     # print(type(images))
@@ -86,7 +86,6 @@ def read_pdf(file):
 #     all_page_text += text + " " #page.extractText()
 #     return all_page_text
 st.title("NLP APPLICATION")
-@st.experimental_singleton
 #@st.cache_resource(experimental_allow_widgets=True)
 def text_analyzer(my_text):
 	nlp = spacy.load('en_core_web_sm')
@@ -94,12 +93,13 @@ def text_analyzer(my_text):
 	# tokens = [ token.text for token in docx]
 	allData = [('"Token":{},\n"Lemma":{}'.format(token.text,token.lemma_))for token in docx ]
 	return allData
-@st.experimental_singleton
 #@st.cache_resource(experimental_allow_widgets=True)
 def load_models():
     tokenizer = AutoTokenizer.from_pretrained('gpt2-large')
     model = GPT2LMHeadModel.from_pretrained('gpt2-large')
     return tokenizer, model
+tokenizer = AutoTokenizer.from_pretrained('t5-base')
+model = AutoModelWithLMHead.from_pretrained('t5-base', return_dict=True)
 # Function For Extracting Entities
 @st.experimental_singleton
 #@st.cache_resource(experimental_allow_widgets=True)
@@ -111,8 +111,6 @@ def entity_analyzer(my_text):
 	allData = ['"Token":{},\n"Entities":{}'.format(tokens,entities)]
 	return allData
 def main():
-    tokenizer = AutoTokenizer.from_pretrained('t5-base')
-    model = AutoModelWithLMHead.from_pretrained('t5-base', return_dict=True)
     """ NLP Based Application with Streamlit """
     st.markdown("""
     	#### Description