updated app.py

app.py

@@ -2,47 +2,70 @@ import gradio as gr
 import torch
 from ultralyticsplus import YOLO, render_result
 
-device = 'cuda' if torch.cuda.is_available() else 'cpu'
 yolo_model = YOLO('eeshawn11/naruto_hand_seal_detection')
-yolo_model.conf = 0.50
-yolo_model.iou = 0.45
-yolo_model.overrides['max_det'] = 1
+yolo_model.overrides['max_det'] = 10
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
 yolo_model.to(device)
 
-def yolov8_inference(
-    image: gr.Image = None,
-    conf_threshold: gr.Slider = 0.50,
-    iou_threshold: gr.Slider = 0.45,
+def seal_detection(
+    image,
+    conf_threshold,
 ):
     """
-    YOLOv8 inference function
+    Object detection with YOLOv8 model, detecting basic Naruto hand seals.
+
     Args:
         image: Input image
-        model_path: Path to the model
         conf_threshold: Confidence threshold
-        iou_threshold: IOU threshold
     Returns:
         Rendered image
     """
-    results = yolo_model.predict(image)
+    results = yolo_model.predict(image, conf=conf_threshold)
     render = render_result(model=yolo_model, image=image, result=results[0])
     return render
-        
-
-inputs = [
-    gr.Image(source="upload", type="pil", label="Image Upload", interactive=True),
-    gr.Slider(minimum=0.0, maximum=1.0, value=0.5, step=0.05, label="Confidence Threshold"),
-    gr.Slider(minimum=0.0, maximum=1.0, value=0.45, step=0.05, label="IOU Threshold"),
-]
-
-outputs = gr.Image(type="filepath", label="Output Image")
-title = "Naruto Hand Seal Detection with YOLOv8"
-
-myapp = gr.Interface(
-    fn=yolov8_inference,
-    inputs=inputs,
-    outputs=outputs,
-    title=title,
-)
-myapp.queue()
-myapp.launch()
+
+def clear():
+    image_upload = gr.update(value=None)
+    conf_slider = gr.update(value=0.5)
+    return image_upload, conf_slider
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Naruto Hand Seal Detection with YOLOv8")
+    gr.Markdown(
+            """
+            ### Introduction
+
+            As a data science practitioner and fan of Japanese manga, I was eager to apply my skills to a project that combined my interests. I decided to develop a computer vision model that could detect hand seals from the **Naruto** anime. 
+            
+            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.
+
+            ### Problem Statement
+
+            The challenge was to develop a model that could accurately identify the hand seal being performed. 
+            
+            ### Methodology
+
+            In this project, I leveraged transfer learning from the [YOLOv8](https://github.com/ultralytics/ultralytics) model to customize an object detection model specifically for the hand seals.
+            
+            There were several challenges during the development process, including limited available datasets of labelled images for training, so I had to create my own through a mix of screenshots from YouTube videos, as well as capturing images of myself performing the seals.
+            """
+        )
+    with gr.Row():
+        with gr.Column():
+            inputs = [
+                    gr.Image(source="upload", type="pil", label="Image Upload", interactive=True),
+                    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")
+        outputs = gr.Image(type="filepath", label="Output Image", interactive=False)
+    gr.Markdown("Happy to connect on [LinkedIn](https://www.linkedin.com/in/shawn-sing/) or visit my [GitHub](https://github.com/eeshawn11/) to check out my other projects.")
+    
+    clear_form.click(fn=clear, inputs=None, outputs=inputs)
+    submit.click(fn=seal_detection, inputs=inputs, outputs=outputs)
+
+demo.queue(api_open=False, max_size=10)
+demo.launch()

requirements.txt

@@ -1,2 +1 @@
-ultralytics~=8.0.4
 ultralyticsplus==0.0.28