Update app.py

app.py

@@ -52,9 +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
-#@st.cache_resource(experimental_allow_widgets=True)
-@st.cache_data
+@st.experimental_singleton
 def read_pdf(file):
 #     images=pdf2image.convert_from_path(file)
 #     # print(type(images))
@@ -88,9 +86,8 @@ def read_pdf(file):
 #     all_page_text += text + " " #page.extractText()
 #     return all_page_text
 st.title("NLP APPLICATION")
-#@st.experimental_singleton
+@st.experimental_singleton
 #@st.cache_resource(experimental_allow_widgets=True)
-@st.cache_data
 def text_analyzer(my_text):
 	nlp = spacy.load('en_core_web_sm')
 	docx = nlp(my_text)
@@ -104,9 +101,8 @@ def load_models():
     model = GPT2LMHeadModel.from_pretrained('gpt2-large')
     return tokenizer, model
 # Function For Extracting Entities
-#@st.experimental_singleton
+@st.experimental_singleton
 #@st.cache_resource(experimental_allow_widgets=True)
-@st.chache_data
 def entity_analyzer(my_text):
 	nlp = spacy.load('en_core_web_sm')
 	docx = nlp(my_text)
@@ -172,20 +168,16 @@ def main():
         #img = cv2.imread("scholarly_text.jpg")
             text = message
         if st.checkbox("Show Named Entities English/Bangla"):
-            st.cache_data.clear()
             entity_result = entity_analyzer(text)
             st.json(entity_result)
         
         if st.checkbox("Show Sentiment Analysis for English"):
-            st.cache_data.clear()
             blob = TextBlob(text)
             result_sentiment = blob.sentiment
             st.success(result_sentiment)
         if st.checkbox("Spell Corrections for English"):
-            st.cache_data.clear()
             st.success(TextBlob(text).correct())
         if st.checkbox("Text Generation"):
-            st.cache_data.clear()
             tokenizer, model = load_models()
             input_ids = tokenizer(text, return_tensors='pt').input_ids
             st.text("Using Hugging Face Transformer, Contrastive Search ..")
@@ -200,7 +192,6 @@ def main():
         #     st.success(summary_result)
         if st.checkbox("Mark to English Text Summarization!"):
 			#st.title("Summarize Your Text for English only!")
-            st.cache_data.clear()
             tokenizer = AutoTokenizer.from_pretrained('t5-base')
             model = AutoModelWithLMHead.from_pretrained('t5-base', return_dict=True)
 			#st.text("Using Google T5 Transformer ..")
@@ -212,7 +203,6 @@ def main():
             summary = tokenizer.decode(summary_ids[0])
             st.success(summary)
         if st.button("refresh"):
-            st.cache_data.clear()
             st.experimental_rerun()
 if __name__ == '__main__':
     main()