Update app.py

app.py

@@ -7,9 +7,6 @@ from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTok
 print("="*150)
 print("MODEL LOADED")
 st.title("img_captioning_app")
-model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
-tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 #st.text("Build with Streamlit and OpenCV")
 if "photo" not in st.session_state:
 	st.session_state["photo"]="not done"
@@ -28,6 +25,9 @@ activities = ["Caption","About"]
 choice = st.sidebar.selectbox("Select Activty",activities)
 uploaded_photo = c2.file_uploader("Upload Image",type=['jpg','png','jpeg'], on_change=change_photo_state)
 camera_photo = c2.camera_input("Take a photo", on_change=change_photo_state)
+model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
 if choice == 'Caption':
     #st.subheader("Detection") 
     if st.session_state["photo"]=="done":
@@ -54,5 +54,5 @@ if choice == 'Caption':
         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-site.streamlit.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/)")