Update app.py

app.py

@@ -14,12 +14,16 @@ model = AutoModel.from_pretrained('dhhd255/parkinsons_pred0.1')
 # Move the model to the device
 model = model.to(device)
 
-# Use Streamlit to upload an image
+st.title("Parkinson's Disease Prediction")
+
 uploaded_file = st.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])
 if uploaded_file is not None:
+    col1, col2 = st.columns(2)
+
     # Load and resize the image
     image_size = (224, 224)
     new_image = Image.open(uploaded_file).convert('RGB').resize(image_size)
+    col1.image(new_image, use_column_width=True)
     new_image = np.array(new_image)
     new_image = torch.from_numpy(new_image).transpose(0, 2).float().unsqueeze(0)
 
@@ -40,6 +44,6 @@ if uploaded_file is not None:
         logits = feature_reducer(logits)
         predicted_class = torch.argmax(logits, dim=1).item()
         if(predicted_class == 0):
-            st.write('Predicted class: Parkinson\'s')
+            col2.subheader('Predicted class: Parkinson\'s')
         else:
-            st.write('Predicted class: Healthy')
+            col2.subheader('Predicted class: Healthy')