Create app.py

app.py

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+import os
+import gradio as gr
+from ultralytics import YOLO
+from PIL import Image
+
+# Load YOLOv8 model
+model = YOLO('/content/drive/MyDrive/yolov8_models/best (4).pt')
+
+# Folder with test images
+test_images_folder = '/content/Instance_seg_teeth/Dataset/yolo_test_dataset/test/images'
+test_images = sorted(os.listdir(test_images_folder))
+
+# Prediction function
+def predict_image(image_path):
+    results = model(image_path)
+    img_array = results[0].plot(conf=False, labels=True, boxes=True)
+    return Image.fromarray(img_array)
+
+# Logic: use uploaded image if available, otherwise selected image
+def run_prediction(uploaded_image, selected_image):
+    if uploaded_image is not None:
+        return predict_image(uploaded_image)
+    elif selected_image is not None:
+        image_path = os.path.join(test_images_folder, selected_image)
+        return predict_image(image_path)
+    else:
+        return None
+
+# Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown("## 🦷 Dental Segmentation with YOLOv8")
+    gr.Markdown("Upload your own image or choose a test image from the list below.")
+
+    with gr.Column():
+        uploaded_image = gr.Image(label="Upload your image (optional)", type="filepath")
+        selected_image = gr.Dropdown(choices=test_images, label="...or select a test image")
+
+    gr.Markdown("### Prediction Result")
+    output_image = gr.Image(label="Predicted Image")
+
+    gr.Button("Run prediction").click(
+        fn=run_prediction,
+        inputs=[uploaded_image, selected_image],
+        outputs=output_image
+    )
+
+demo.launch()