Update app.py

app.py

@@ -90,25 +90,12 @@ def set_example_image(example: list) -> dict:
 def set_example_url(example: list) -> dict:
     return gr.Textbox.update(value=example[0]), gr.Image.update(value=get_original_image(example[0]))
 
-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)  
-"""
+title = """<h1 id="title">License Plate Detection with YOLOS</h1>""" 
 
 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 {
   text-align: center;
@@ -117,9 +104,7 @@ 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')
     
@@ -146,7 +131,7 @@ with demo:
             
         with gr.TabItem('WebCam'):
             with gr.Row():
-                web_input = gr.Image(type='pil')
+                web_input = gr.Camera()
                 img_output_from_webcam = gr.Image()
             cam_but = gr.Button('Detect')