Update app.py

app.py

@@ -84,7 +84,7 @@ def read_pdf(file):
 #     text = pytesseract.image_to_string(image_name, lang="ben") if st.checkbox("Mark to see Bangla Image's Text") else pytesseract.image_to_string(image_name)
 #     all_page_text += text + " " #page.extractText()
 #     return all_page_text
-st.title("Streamlit NLP APP")
+st.title("NLP APPLICATION")
 @st.experimental_singleton
 def text_analyzer(my_text):
 	nlp = spacy.load('en_core_web_sm')
@@ -151,13 +151,13 @@ 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, lang="ben") if st.checkbox("Mark to see Bangla Image's Text") else pytesseract.image_to_string(img)
+            text = pytesseract.image_to_string(img) #pytesseract.image_to_string(img, lang="ben") if st.checkbox("Mark to see Bangla Image's Text") else 
             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, lang="ben") if st.checkbox("Mark to see Bangla Image's Text") else pytesseract.image_to_string(img)
+            text = pytesseract.image_to_string(img) #pytesseract.image_to_string(img, lang="ben") if st.checkbox("Mark to see Bangla Image's Text") else pytesseract.image_to_string(img)
             st.success(text)
         elif uploaded_photo==None and camera_photo==None:
     	#our_image=load_image("image.jpg")
@@ -185,7 +185,7 @@ def main():
         #     st.success(message)
         #     summary_result = summarize(text)
         #     st.success(summary_result)
-        if st.checkbox("Mark to better English Text Summarization!"):
+        if st.checkbox("Mark to English Text Summarization!"):
 			#st.title("Summarize Your Text for English only!")
             tokenizer = AutoTokenizer.from_pretrained('t5-base')
             model = AutoModelWithLMHead.from_pretrained('t5-base', return_dict=True)