Update app.py

app.py

@@ -10,7 +10,6 @@ from ultralytics import YOLO
 import cv2
 from huggingface_hub import from_pretrained_keras
 
-
 class Config:
     ASSETS_DIR = './assets'
     MODELS_DIR = './models'
@@ -31,12 +30,17 @@ class ModelManager:
     def load_model(model_name: str):
         model_path = os.path.join(Config.MODELS_DIR, Config.MODELS[model_name])
         if model_name == "Dental X-Ray Segmentation":
-            return from_pretrained_keras("SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net")
+            try:
+                model = from_pretrained_keras("SerdarHelli/Segmentation-of-Teeth-in-Panoramic-X-ray-Image-Using-U-Net")
+            except:
+                model = tf.keras.models.load_model(model_path)
+            return model
         elif model_name == "Caries Detection":
             return YOLO(model_path)
         else:
             return load_model(model_path)
 
+
 class ImageProcessor:
 
     def process_image(self, image: Image.Image, model_name: str):
@@ -120,6 +124,7 @@ class ImageProcessor:
             img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
         return img
 
+
 class GradioInterface:
     def __init__(self):
         self.image_processor = ImageProcessor()
@@ -261,6 +266,16 @@ class GradioInterface:
         </div>
         """
 
+        header_html = f"""
+        <div class="app-header">
+            <h1 class="app-title">AI in Dentistry</h1>
+            <h2 class="app-subtitle"> Advancing Imaging and Clinical Transcription</h2>
+            <p class="app-description">
+                This application demonstrates the use of AI in dentistry for tasks such as classification, detection, and segmentation.
+            </p>
+        </div>
+        """
+
         js_func = """
         function refresh() {
             const url = new URL(window.location);
@@ -290,21 +305,10 @@ class GradioInterface:
                             choices=list(Config.MODELS.keys()),
                             value="Calculus and Caries Classification",
                         )
-                    examples_classification = gr.Examples(
-                        label="Classification Examples",
+                    examples = gr.Examples(
                         inputs=input_image,
                         examples=self.preloaded_examples["Calculus and Caries Classification"],
                     )
-                    examples_detection = gr.Examples(
-                        label="Caries Detection Examples",
-                        inputs=input_image,
-                        examples=self.preloaded_examples["Caries Detection"],
-                    )
-                    examples_segmentation = gr.Examples(
-                        label="Segmentation Examples",
-                        inputs=input_image,
-                        examples=self.preloaded_examples["Dental X-Ray Segmentation"],
-                    )
                 with gr.Column():
                     result = gr.Image(label="Result", elem_classes="image-preview")
                     run_button = gr.Button("Run", elem_classes="gr-button")
@@ -312,7 +316,7 @@ class GradioInterface:
             model_name.change(
                 fn=update_examples,
                 inputs=model_name,
-                outputs=[examples_classification.dataset, examples_detection.dataset, examples_segmentation.dataset],
+                outputs=examples.dataset,
             )
 
             run_button.click(
@@ -329,4 +333,5 @@ def main():
     demo.launch(share=False)
 
 if __name__ == "__main__":
-    main()
+    main()
+