Update app.py

app.py

@@ -72,8 +72,8 @@ def main():
     	""")                         
 	def change_photo_state():
 		st.session_state["photo"]="done"
-	st.subheader("Please, feed your image!")
-	message = st.text_input("Or, drop your text here, only English text!")
+	st.subheader("Please, feed your image below!")
+	message = st.text_input("Or, drop your text here!")
 	camera_photo = st.camera_input("Take a photo, Containing English or Bangla texts", on_change=change_photo_state)
 	uploaded_photo = st.file_uploader("Upload Image, Containing English or Bangla texts",type=['jpg','png','jpeg'], on_change=change_photo_state)
 	if "photo" not in st.session_state:
@@ -84,13 +84,13 @@ def main():
 			img = Image.open(uploaded_photo)
 			img = img.save("img.png")
 			img = cv2.imread("img.png")
-			text = pytesseract.image_to_string(img, lang="ben") if st.checkbox("Mark here to see in Bangla for Bangla Images only") else pytesseract.image_to_string(img)
+			text = 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)
 		if 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 here to see Bangla") else pytesseract.image_to_string(img)
+			text = 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)
 		if uploaded_photo==None and camera_photo==None:
 			#our_image=load_image("image.jpg")
@@ -120,7 +120,7 @@ def main():
 			#st.success(mess)
 			summary_result = summarize(text)
 			st.success(summary_result)
-		if st.checkbox("Mark here, Better Text Summarization for English only!"):
+		if st.checkbox("Mark to better 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)