Update app.py

app.py

@@ -61,7 +61,9 @@ def set_example_url(example: list) -> dict:
 
 title = """<h1 id="title">Plant Health Classification with ViT</h1>"""
 
-gr.Image('images/Healthy.png',label = 'Healthy Plant'), gr.Image('images/sickie.png',label = 'Infected Plant')
+gr.Image('images/Healthy.png',label = 'Healthy Plant') 
+
+gr.Image('images/sickie.png',label = 'Infected Plant')
 
 description = """
 This Plant Health classifier app was built to detect the health of plants using images of leaves by fine-tuning a Vision Transformer (ViT) [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the [Beans](https://huggingface.co/datasets/beans) dataset.
@@ -70,9 +72,9 @@ The finetuned model has an accuracy of 98.4% on the test (unseen) dataset and 10
 How to use the app:
 - Upload an image via 3 options, uploading the image from local device, using a URL (image from the web) or a webcam
 - The app will take a few seconds to generate a prediction with the following labels:
-  - *'angular_leaf_spot'* 
-  - *'bean_rust'*
-  - *'healthy'*
+  - *angular_leaf_spot* 
+  - *bean_rust*
+  - *healthy*
 - Feel free to click the image examples as well.  
 """
 urls = ["https://www.healthbenefitstimes.com/green-beans/","https://huggingface.co/nateraw/vit-base-beans/resolve/main/angular_leaf_spot.jpeg", "https://huggingface.co/nateraw/vit-base-beans/resolve/main/bean_rust.jpeg"]
@@ -98,7 +100,7 @@ with demo:
         with gr.TabItem('Image Upload'):
             with gr.Row():
                 with gr.Column():
-                  img_input = gr.Image(type='pil',shape=(750,750))
+                  img_input = gr.Image(type='pil',shape=(450,450))
                   label_from_upload= gr.Label(num_top_classes=3)
                 
             with gr.Row(): 
@@ -111,7 +113,7 @@ with demo:
             with gr.Row():
                 with gr.Column():
                     url_input = gr.Textbox(lines=2,label='Enter valid image URL here..')
-                    original_image = gr.Image(shape=(750,750))
+                    original_image = gr.Image(shape=(450,450))
                     url_input.change(get_original_image, url_input, original_image)
                 with gr.Column():
                     label_from_url = gr.Label(num_top_classes=3)