app.py revert changes

app.py

@@ -3,15 +3,11 @@ import torch
 from ultralyticsplus import YOLO, render_result
 import os
 
-HF_TOKEN = os.getenv('HF_TOKEN')
-
 yolo_model = YOLO('eeshawn11/naruto_hand_seal_detection')
 yolo_model.overrides['max_det'] = 10
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 yolo_model.to(device)
 
-!wget -q https://github.com/gradio-app/gradio/raw/main/demo/blocks_inputs/lion.jpg
-
 def seal_detection(
     image,
     conf_threshold,
@@ -37,8 +33,6 @@ def clear():
     conf_update = gr.Slider.update(value=0.5)
     return image_update, conf_update, image_update
 
-callback = gr.HuggingFaceDatasetJSONSaver(hf_token=HF_TOKEN, dataset_name="crowdsourced_hand_seals", private=True)
-
 with gr.Blocks() as demo:
     gr.Markdown("# Naruto Hand Seal Detection with YOLOv8")
     with gr.Accordion("README", open=False):
@@ -51,7 +45,11 @@ with gr.Blocks() as demo:
                 Hand seals are an integral part of the Naruto universe, used by characters to activate powerful techniques. There are twelve basic seals, each named after an animal in the Chinese Zodiac, and different sequences of hand seals are required for different techniques.
     
                 As a fan of the series, I knew that accurately detecting and classifying hand seals would be a difficult but rewarding challenge, and I was excited to tackle it using my expertise in machine learning and computer vision. One key challenge to overcome would be the lack of a good dataset of labelled images for training, so I had to develop my own. Besides capturing images of myself performing the seals, I augmented my dataset with YouTube screenshots consisting of both real persons and anime characters performing the seals.
-    
+
+                <p style="text-align:center">
+                    <img src"https://huggingface.co/spaces/eeshawn11/naruto_hand_seals/blob/main/assets/Naruto_Hand_Seals_by_Megan.gif">
+                </p>
+
                 ### Problem Statement
     
                 The challenge was to develop a model that could accurately identify the hand seal being performed.
@@ -62,28 +60,13 @@ with gr.Blocks() as demo:
         
     with gr.Row():
         with gr.Column():
-            # inputs = [
             image = gr.Image(source="upload", type="pil", label="Image Upload", interactive=True),
             slider = gr.Slider(minimum=0.05, maximum=1.0, value=0.5, step=0.05, label="Confidence Threshold"),
-            # ]
             with gr.Row():
                 clear_form = gr.Button("Reset")
                 submit = gr.Button("Predict")
         with gr.Column():
             outputs = gr.Image(type="filepath", label="Output Image", interactive=False)
-            # flag = gr.Button("Flag")
-
-    with gr.Row():
-        gr.Examples(
-            examples=[os.path.join(os.path.dirname(__file__), "lion.jpg")],
-            inputs=image,
-            outputs=outputs,
-            fn=seal_detection,
-            run_on_click=True,
-            # preprocess=False,
-            # postprocess=False,
-            cache_examples=True,
-        )
 
     gr.Markdown(
         """
@@ -91,12 +74,9 @@ with gr.Blocks() as demo:
             Happy to connect on <a href="https://www.linkedin.com/in/shawn-sing/" target="_blank">LinkedIn</a> or visit my <a href="https://github.com/eeshawn11/" target="_blank">GitHub</a> to check out my other projects.
         """
         )
-    
-    # callback.setup([image, slider, outputs], "crowdsourced_hand_seals")
 
     clear_form.click(fn=clear, inputs=None, outputs=[image[0], slider[0], outputs], show_progress=False)
     submit.click(fn=seal_detection, inputs=[image[0], slider[0]], outputs=outputs)
-    # flag.click(lambda *args: callback.flag([image[0], slider[0], outputs]), inputs=[image[0], slider[0], outputs], outputs=None, preprocess=False)
 
 if __name__ == "__main__":
     demo.queue(api_open=False, max_size=10)