Add application file

.gitignore

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+.git/
+flagged/
+gradio_cached_examples/
+yolov8n.pt
+
+tmp/0630d4cd9e21cbb71aadde8af8d07063d477f477.jpg
+tmp/0766ddd55f53713c006822de3b7359aeedc3c10a.jpg
+tmp/14756463a54c6701f38d1fd048ee1ec2cad7bb0a.jpg
+tmp/1ac9d1a1c9d327b47369bcb2ed8db6d42d7890f7.jpg
+tmp/22a77defac5e6b1b677ece221b2da818fcf1c1d3.jpg
+tmp/29ad45df9b3cfc544ad11038c0c672b54575a4d4.jpg
+tmp/37cd6e1e0b05c969d020279d0168aac9574c8c80.jpg
+tmp/3e1bab1684e0fba56b99e848a5bac400d39dff0f.jpg
+tmp/4a14b3aeef71692ee3c64580a151dcf2651069e4.jpg
+tmp/4bed9cde4c606b5d719734d6ea431372201fc675.jpg
+tmp/517f7ba0fc8c575a0c949df08b14fa68208dce24.jpg
+tmp/5825095aa175acc396daf02e0840d53b513cfe26.jpg
+tmp/591b606b955809a97d187d8efac007f58002005f.jpg
+tmp/5b48a15348b36579a38a93fa9ceecaa8d469b063.jpg
+tmp/5b77964d8e3024a252657ca4a75cb3ebbaeac074.jpg
+tmp/660b91ce88146e2fa96db3d619d56145557794da.jpg
+tmp/7eb4d3a70a8ba68c7c56ecaf0b98a1bc86aac46c.jpg
+tmp/8cd6f1f3f9cc90e5e9adce08dd5d501f08861017.jpg
+tmp/8e11944dec02c6a20716ba85b86a326aefdaf2e6.jpg
+tmp/8f176a9e01c72c4462b96a8efb8c674a5d162dda.jpg
+tmp/933b16fb6edf53a30beb723f8fad7e9dee620b30.jpg
+tmp/98a97c135e28e50247c741ce7a1a6734132d03fc.jpg
+tmp/9ca463ac20ce86875f413fc67f1e2f7828a21221.jpg
+tmp/c2c6e668ecf2d31901bb8dda3a6f92ea8611631a.jpg
+tmp/fb210fab276dcb4a458dff6a91f7f39403959d50.jpg

app.py

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+import json
+import glob
+from collections import Counter
+
+import requests
+import gradio as gr
+from ultralyticsplus import YOLO, download_from_hub, render_result
+
+hf_model_ids = [
+    "chanelcolgate/chamdiemgianhang-vsk",
+    "chanelcolgate/chamdiemgianhang-vsk-v2",
+]
+
+image_paths = [
+    [image_path, "chanelcolgate/chamdiemgianhang-vsk-v2", 640, 0.25, 0.45]
+    for image_path in glob.glob("./tmp/*.jpg")
+]
+
+
+def detection_image(
+    image=None,
+    hf_model_id="chanelcolgate/chamdiemgianhang-vsk-v2",
+    image_size=640,
+    conf_threshold=0.25,
+    iou_threshold=0.45,
+):
+    model_path = download_from_hub(hf_model_id)
+    model = YOLO(model_path)
+    results = model(image, imgsz=image_size, conf=conf_threshold, iou=iou_threshold)
+    json_result = json.loads(results[0].tojson())
+    class_counts = Counter(detection["name"] for detection in json_result)
+
+    render = render_result(model=model, image=image, result=results[0])
+    return render, class_counts
+
+
+def detection_image_link(
+    image=None,
+    hf_model_id="chanelcolgate/chamdiemgianhang-vsk-v2",
+    image_size=640,
+    conf_threshold=0.25,
+    iou_threshold=0.45,
+):
+    model_path = download_from_hub(hf_model_id)
+    model = YOLO(model_path)
+    results = model(image, imgsz=image_size, conf=conf_threshold, iou=iou_threshold)
+    json_result = json.loads(results[0].tojson())
+    class_counts = Counter(detection["name"] for detection in json_result)
+
+    render = render_result(model=model, image=image, result=results[0])
+    return render, class_counts
+
+
+title = "Cham Diem Gian Hang VSK"
+
+interface = gr.Interface(
+    fn=detection_image,
+    inputs=[
+        gr.Image(type="pil"),
+        gr.Dropdown(hf_model_ids),
+        gr.Slider(minimum=320, maximum=1280, value=640, step=32, label="Image Size"),
+        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="pil"), gr.Textbox(show_label=False)],
+    title=title,
+    examples=image_paths,
+    cache_examples=True if image_paths else False,
+)
+
+interface_link = gr.Interface(
+    fn=detection_image,
+    inputs=[
+        gr.Textbox(label="Image Link"),
+        gr.Dropdown(hf_model_ids),
+        gr.Slider(minimum=320, maximum=1280, value=640, step=32, label="Image Size"),
+        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="pil"), gr.Textbox(show_label=False)],
+    title=title,
+)
+
+gr.TabbedInterface(
+    [interface, interface_link], tab_names=["Image inference", "Image link inference"]
+).queue().launch()

requirements.txt

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+requests==2.31.0
+gradio==4.26.0
+ultralyticsplus==0.0.29