Update app.py

app.py

@@ -22,35 +22,32 @@ print("RESNET MODEL LOADED")
 def load_image(img):
 	im = Image.open(img)
 	return im
-activities = ["Generate ","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)
-if choice == 'Generate':
-    st.subheader("Generate") 
-    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')
-        device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-        model.to(device)
-        max_length = 16
-        num_beams = 4
-        gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
-        def predict_step(our_image):
-            if our_image.mode != "RGB":
-                our_image = our_image.convert(mode="RGB")
-            pixel_values = feature_extractor(images=our_image, return_tensors="pt").pixel_values
-            pixel_values = pixel_values.to(device)
-            output_ids = model.generate(pixel_values, **gen_kwargs)
-            preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
-            preds = [pred.strip() for pred in preds]
-            return preds
-        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("Demo applicaton of the following model [credit](https://huggingface.co/nlpconnect/vit-gpt2-image-captioning/)")
+st.subheader("Caption is below!") 
+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')
+   device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+   model.to(device)
+   max_length = 16
+   num_beams = 4
+   gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
+   def predict_step(our_image):
+       if our_image.mode != "RGB":
+          our_image = our_image.convert(mode="RGB")
+       pixel_values = feature_extractor(images=our_image, return_tensors="pt").pixel_values
+       pixel_values = pixel_values.to(device)
+       output_ids = model.generate(pixel_values, **gen_kwargs)
+       preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+       preds = [pred.strip() for pred in preds]
+       return preds
+   st.success(predict_step(our_image))
+if st.checkbox('About'):
+   st.subheader("About Image Captioning App")
+   st.markdown("Built with Streamlit by [Soumen Sarker](https://soumen-sarker-personal-site.streamlit.app/)")
+   st.markdown("Demo applicaton of the following model [credit](https://huggingface.co/nlpconnect/vit-gpt2-image-captioning/)")