Create app.py
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app.py
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from ultralytics import YOLO
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import gradio as gr
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model = YOLO("./runs/detect/train18/weights/best.pt")
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def yolo_predict(image):
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"""Run YOLOv8 inference and return annotated image with results"""
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results = model(image)
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print(results)
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annotated_image = results[0].plot()
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# Get prediction details
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boxes = results[0].boxes
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prediction_details = []
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for box in boxes:
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class_id = int(box.cls[0].item())
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class_name = model.names[class_id]
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confidence = round(box.conf[0].item(), 2)
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coords = box.xyxy[0].tolist() # [x1, y1, x2, y2]
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prediction_details.append({
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"class": class_name,
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"confidence": confidence,
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"bbox": coords
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})
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return annotated_image
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# return annotated_image, prediction_details
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with gr.Blocks() as demo:
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gr.Markdown("# YOLOv8 Object Detection")
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gr.Markdown(
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"""
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This application uses a YOLOv8m model fine-tuned specifically to detect red blood
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cells, white blood cells, and platelets in images of blood cells. This version
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was trained using the `keremberke/blood-cell-object-detection` dataset on huggingface.com.
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"""
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)
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gr.Interface(
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fn=yolo_predict,
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inputs=gr.Image(label="Input Image",type="pil"),
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outputs=[
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gr.Image(label="Detected Objects"),
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# gr.JSON(label="Detection Details")
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],
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# title="YOLOv8 Object Detection",
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# # description="Upload an image to detect objects using YOLOv8",
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description='Select an example image below (none of which were included in model training or validation), or upload your own image. Then, click "Submit" to see the model in action.',
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examples=[
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"bloodcell-examples/image_0.jpg",
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"bloodcell-examples/image_1.jpg",
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"bloodcell-examples/image_2.jpg",
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"bloodcell-examples/image_3.jpg",
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"bloodcell-examples/image_4.jpg",
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],
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)
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demo.launch(
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show_error=True,
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height=900,
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width="80%",
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# width="100%",
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share=True,
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)
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