Update app.py

app.py

@@ -21,7 +21,7 @@ print("RESNET MODEL LOADED")
 def load_image(img):
 	im = Image.open(img)
 	return im
-activities = ["Caption","About"]
+activities = ["About"]
 choice = st.sidebar.selectbox("Select Activty",activities)
 model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 uploaded_photo = c2.file_uploader("Upload Image",type=['jpg','png','jpeg'], on_change=change_photo_state)
@@ -42,17 +42,16 @@ def predict_step(our_image):
    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
    preds = [pred.strip() for pred in preds]
    return preds
-if choice == 'Caption':
-    #st.subheader("Detection") 
-    if st.session_state["photo"]=="done":
-        if uploaded_photo:
-            our_image= load_image(uploaded_photo)
-        elif camera_photo:
-            our_image= load_image(camera_photo)
-        elif uploaded_photo==None and camera_photo==None:
-            our_image= load_image('image.jpg')
-        st.success(predict_step(our_image))
-elif choice == 'About':
-	st.subheader("About Image Captioning App")
-	st.markdown("Built with Streamlit by [Soumen Sarker](https://soumen-sarker-personal-website.streamlit.app/)")
-	st.markdown("Demo applicaton of the following model [credit](https://huggingface.co/nlpconnect/vit-gpt2-image-captioning/)")
+#st.subheader("Detection") 
+if st.session_state["photo"]=="done":
+   if uploaded_photo:
+      our_image= load_image(uploaded_photo)
+   elif camera_photo:
+      our_image= load_image(camera_photo)
+   elif uploaded_photo==None and camera_photo==None:
+      our_image= load_image('image.jpg')
+   st.success(predict_step(our_image))
+if choice == 'About':
+   st.subheader("About Image Captioning App")
+   st.markdown("Built with Streamlit by [Soumen Sarker](https://soumen-sarker-personal-website.streamlit.app/)")
+   st.markdown("Demo applicaton of the following model [credit](https://huggingface.co/nlpconnect/vit-gpt2-image-captioning/)")