Create app.py

app.py

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+import supervision as sv
+import gradio as gr
+from ultralytics import YOLO
+import sahi
+import numpy as np 
+
+
+
+# Images
+sahi.utils.file.download_from_url(
+    "https://transform.roboflow.com/bViBvBXkjUWzz4lYXwtoVTE2gpO2/210fe71d15bb416b0dfde415686da572/thumb.jpg",
+    "wh1.jpg",
+)
+sahi.utils.file.download_from_url(
+    "https://transform.roboflow.com/bViBvBXkjUWzz4lYXwtoVTE2gpO2/6731f1ac3e966e90ccc0057c86b42c74/thumb.jpg",
+    "wh2.jpg",
+)
+sahi.utils.file.download_from_url(
+    "https://transform.roboflow.com/bViBvBXkjUWzz4lYXwtoVTE2gpO2/ba9fc3cc24849c0408d5e2ddd4a4a4ed/thumb.jpg",
+    "wh3.jpg",
+)
+
+
+
+
+annotatorbbox = sv.BoxAnnotator()
+annotatormask=sv.MaskAnnotator()
+
+
+def yolov8_inference(
+    image: gr.inputs.Image = None,
+    model_name: gr.inputs.Dropdown = None,
+    image_size: gr.inputs.Slider = 640,
+    conf_threshold: gr.inputs.Slider = 0.25,
+    iou_threshold: gr.inputs.Slider = 0.45,
+):
+
+
+    image=image[:, :, ::-1].astype(np.uint8)
+    model = YOLO("/content/segment/train2/weights/best.pt")
+
+    results = model(image,imgsz=640)[0]
+    image=image[:, :, ::-1].astype(np.uint8)
+    detections = sv.Detections.from_yolov8(results)
+    annotated_image = annotatorbbox.annotate(scene=image, detections=detections)
+
+
+
+
+    return annotated_image
+
+image_input = gr.inputs.Image()  # Adjust the shape according to your requirements
+
+inputs = [
+    gr.inputs.Image(label="Input Image"),
+    gr.Slider(
+        minimum=0.0, maximum=1.0, value=0.25, 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 = "Ultralytics YOLOv8 Segmentation Demo"
+import os
+examples = [
+    ["wh1.jpg", 0.6, 0.45],
+    ["wh2.jpg", 0.25, 0.45],
+    ["wh3.jpg", 0.25, 0.45],
+]
+demo_app = gr.Interface(examples=examples,
+    fn=yolov8_inference,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    cache_examples=True,
+    theme="default",
+)
+demo_app.launch(debug=False, enable_queue=True)