Update app.py

app.py

@@ -105,10 +105,10 @@ def text_analyzer(my_text):
 	allData = [('"Token":{},\n"Lemma":{}'.format(token.text,token.lemma_))for token in docx ]
 	return allData
 #@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
+# def load_models():
+#     tokenizer = AutoTokenizer.from_pretrained('gpt2-large')
+#     model = GPT2LMHeadModel.from_pretrained('gpt2-large')
+#     return tokenizer, model
 
 # Function For Extracting Entities
 @st.experimental_singleton
@@ -167,14 +167,14 @@ def main():
             # convert the image to black and white for better OCR
             #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 = 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, lan="ben")
             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) if st.checkbox("Mark to see English Image's Text") else pytesseract.image_to_string(img, lan="ben")
             st.success(text)
         elif uploaded_photo==None and camera_photo==None:
     	#our_image=load_image("image.jpg")