Update app.py

app.py

@@ -104,22 +104,12 @@ def set_example_url(example: list) -> dict:
 
 title = """<h1 id="title">License Plate Detection with YOLOS</h1>"""
 
-description = """
-YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN).
-The YOLOS model was fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](https://github.com/hustvl/YOLOS). 
-This model was further fine-tuned on the [Car license plate dataset]("https://www.kaggle.com/datasets/andrewmvd/car-plate-detection") from Kaggle. The dataset consists of 443 images of vehicle with annotations categorised as "Vehicle" and "Rego Plates". The model was trained for 200 epochs on a single GPU.
-Links to HuggingFace Models:
-- [nickmuchi/yolos-small-rego-plates-detection](https://huggingface.co/nickmuchi/yolos-small-rego-plates-detection)
-- [hustlv/yolos-small](https://huggingface.co/hustlv/yolos-small)  
-"""
+ 
 
 models = ["nickmuchi/yolos-small-finetuned-license-plate-detection","nickmuchi/detr-resnet50-license-plate-detection"]
 urls = ["https://drive.google.com/uc?id=1j9VZQ4NDS4gsubFf3m2qQoTMWLk552bQ","https://drive.google.com/uc?id=1p9wJIqRz3W50e2f_A0D8ftla8hoXz4T5"]
 images = [[path.as_posix()] for path in sorted(pathlib.Path('images').rglob('*.j*g'))]
-
-twitter_link = """
-[![](https://img.shields.io/twitter/follow/nickmuchi?label=@nickmuchi&style=social)](https://twitter.com/nickmuchi)
-"""
+ 
 
 css = '''
 h1#title {
@@ -129,9 +119,7 @@ h1#title {
 demo = gr.Blocks(css=css)
 
 with demo:
-    gr.Markdown(title)
-    gr.Markdown(description)
-    gr.Markdown(twitter_link)
+    gr.Markdown(title)  
     options = gr.Dropdown(choices=models,label='Object Detection Model',value=models[0],show_label=True)
     slider_input = gr.Slider(minimum=0.2,maximum=1,value=0.5,step=0.1,label='Prediction Threshold')
     
@@ -140,10 +128,10 @@ 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()
                     url_input.change(get_original_image, url_input, original_image)
                 with gr.Column():
-                    img_output_from_url = gr.Image(shape=(750,750))
+                    img_output_from_url = gr.Image()
                 
             with gr.Row():
                 example_url = gr.Examples(examples=urls,inputs=[url_input])
@@ -153,8 +141,8 @@ with demo:
      
         with gr.TabItem('Image Upload'):
             with gr.Row():
-                img_input = gr.Image(type='pil',shape=(750,750))
-                img_output_from_upload= gr.Image(shape=(750,750))
+                img_input = gr.Image(type='pil')
+                img_output_from_upload= gr.Image()
                 
             with gr.Row(): 
                 example_images = gr.Examples(examples=images,inputs=[img_input])                            
@@ -164,8 +152,8 @@ with demo:
             
         with gr.TabItem('WebCam'):
             with gr.Row():
-                web_input = gr.Image(source='webcam',type='pil',shape=(750,750),streaming=True)
-                img_output_from_webcam= gr.Image(shape=(750,750))
+                web_input = gr.Image(source='webcam',type='pil',streaming=True)
+                img_output_from_webcam= gr.Image()
 
             cam_but = gr.Button('Detect')