Update app.py

app.py

@@ -98,6 +98,7 @@ def read_pdf(file):
 #     return all_page_text
 st.title("NLP APPLICATION")
 #@st.cache_resource(experimental_allow_widgets=True)
+@st.experimental_singleton
 def text_analyzer(my_text):
 	nlp = spacy.load('en_core_web_sm')
 	docx = nlp(my_text)
@@ -168,14 +169,14 @@ def main():
             #ret,thresh1 = cv2.threshold(imge,120,255,cv2.THRESH_BINARY)
             # pytesseract image to string to get results
             #text = str(pytesseract.image_to_string(img, config='--psm 6',lang="ben")) if st.checkbox("Bangla") else str(pytesseract.image_to_string(thresh1, config='--psm 6'))
-            text =  pytesseract.image_to_string(img) if st.checkbox("Mark to see English Image's Text") else pytesseract.image_to_string(img, lang="ben")
-            st.success(text)
+            text =  pytesseract.image_to_string(img, lang="ben") if st.checkbox("Mark for Bangla Image's Text") else pytesseract.image_to_string(img)
+            #st.success(text)
         elif camera_photo:
             img = Image.open(camera_photo)
             img = img.save("img.png")
             img = cv2.imread("img.png")
-            text = pytesseract.image_to_string(img) if st.checkbox("Mark to see English Image's Text") else pytesseract.image_to_string(img, lang="ben")
-            st.success(text)
+            text = pytesseract.image_to_string(img) if st.checkbox("Mark for Bangla Image's Text") else pytesseract.image_to_string(img, lang="ben")
+            #st.success(text)
         elif uploaded_photo==None and camera_photo==None:
     	#our_image=load_image("image.jpg")
         #img = cv2.imread("scholarly_text.jpg")