Update app.py

app.py

@@ -10,21 +10,7 @@ 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"
-c2, c3 = st.columns([2,1])
-def change_photo_state():
-	st.session_state["photo"]="done"
-@st.cache
-def load_image(img):
-	im = Image.open(img)
-	return im
-activities = ["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)
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+device = torch.device("cuda" if torch.cuda.is_available() else "CPU")
 model.to(device)
 max_length = 16
 num_beams = 4
@@ -38,8 +24,21 @@ def predict_step(our_image):
    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
    preds = [pred.strip() for pred in preds]
    return preds
+#st.text("Build with Streamlit and OpenCV")
+if "photo" not in st.session_state:
+	st.session_state["photo"]="not done"
+c2, c3 = st.columns([2,1])
+def change_photo_state():
+	st.session_state["photo"]="done"
+@st.cache
+def load_image(img):
+	im = Image.open(img)
+	return im
+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)
 #st.subheader("Detection")
 if st.checkbox("Generate_Caption"):
+   our_image=None
    if st.session_state["photo"]=="done":
       if uploaded_photo:
          our_image= load_image(uploaded_photo)
@@ -47,8 +46,8 @@ if st.checkbox("Generate_Caption"):
          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.success(predict_step(our_image))
+elif st.checkbox("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/)")