Add app.py and .pt model

app.py

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+import gradio as gr
+import torch
+from ultralytics import YOLO
+
+torch.hub.download_url_to_file(
+    'https://external-content.duckduckgo.com/iu/?u=https%3A%2F%2Ftexashafts.com%2Fwp-content%2Fuploads%2F2016%2F04%2Fconstruction-worker.jpg', 'one.jpg')
+torch.hub.download_url_to_file(
+    'https://www.pearsonkoutcherlaw.com/wp-content/uploads/2020/06/Construction-Workers.jpg', 'two.jpg')
+torch.hub.download_url_to_file(
+    'https://nssgroup.com/wp-content/uploads/2019/02/Building-maintenance-blog.jpg', 'three.jpg')
+
+
+def handle_classify(image: gr.inputs.Image = None):
+    """This function performs YOLOv8 object detection on the given image.
+
+    Args:
+        image (gr.inputs.Image, optional): Input image to detect objects on. Defaults to None.
+    """
+
+    model_path = "racist2.0.pt"
+    model = YOLO(model_path)
+
+    results = model(image)
+    
+    result = results[0]
+    
+    top5 = [[result.names[class_index], result.probs.top5conf.tolist()[rank]]
+                    for class_index, rank in zip(result.probs.top5, range(5))]
+    
+    print(top5)
+    
+    return top5
+
+
+inputs = [
+    gr.inputs.Image(type="filepath", label="Input Image"),
+]
+
+
+outputs = gr.outputs.Text()
+
+title = "Racist model v2"
+
+
+examples = [['one.jpg'],
+            ['two.jpg'],
+            ['three.jpg']]
+
+yolo_app = gr.Interface(
+    fn=handle_classify,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    examples=examples,
+    cache_examples=True,
+)
+
+# Launch the Gradio interface in debug mode with queue enabled
+yolo_app.launch(debug=True, enable_queue=True)

racist2.0.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6c59a98befcac1c90e9500f01516a88ae728eb3cea2a0e13c20d1f42d30efd41
+size 112503345