gradio component for image classification added

classify_utils.py

@@ -0,0 +1,42 @@
+import gradio as gr
+import os
+from ultralytics import YOLO
+import requests
+import supervision as sv
+
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
+
+
+def clasify(image, radio_choice, slider_val):
+    print(radio_choice)
+    model = YOLO(radio_choice + '.pt')
+    result = model(image, verbose=False)[0]
+    classifications = sv.Classifications.from_ultralytics(result)
+    out_dic = {}
+    cls_out = classifications.get_top_k(4)
+    for idx in range(4):
+        cls_id = cls_out[0][idx]
+        cls_prob = cls_out[1][idx]
+        out_dic[labels[int(cls_id)]] = float(cls_prob)
+    return out_dic
+
+
+inputs_thresh = [
+    gr.inputs.Image(type="filepath", label="Input Image"),
+    gr.inputs.Radio(label="Classification Methods",
+                    choices=[
+                        "yolov8n-cls", "yolov8s-cls"
+                    ]),
+    gr.components.Slider(label="Class Probability Value",
+                         value=10, minimum=1, maximum=100, step=1),
+]
+
+classify_tab = gr.Interface(
+    clasify,
+    inputs=inputs_thresh,
+    outputs=gr.outputs.Label(num_top_classes=4),
+    title="supervision",
+    examples=[["dog.jpeg", "yolov8s-cls"], ["dog-2.jpeg", "yolov8n-cls"]],
+    description="Gradio based demo for <a href='https://github.com/roboflow/supervision' style='text-decoration: underline' target='_blank'>roboflow/supervision</a>, We write your reusable computer vision tools."
+)