first space

app.py

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+
+import gradio as gr
+import yolov5
+from PIL import Image
+
+app_title = "License Plate Object Detection"
+models_ids = ['hermanshid/yolo-aksara-jawa']
+article = f"<p style='text-align: center'> <a href='https://huggingface.co/{models_ids[-1]}'>model</a> | <a href='https://huggingface.co/keremberke/license-plate-object-detection'>dataset</a> | <a href='https://github.com/keremberke/awesome-yolov5-models'>awesome-yolov5-models</a> </p>"
+
+current_model_id = models_ids[-1]
+model = yolov5.load(current_model_id)
+
+examples = [['test_images/hanacaraka.jpg', 0.25, 'hermanshid/yolo-aksara-jawa']]
+
+
+def predict(image, threshold=0.25, model_id=None):
+    # update model if required
+    global current_model_id
+    global model
+    if model_id != current_model_id:
+        model = yolov5.load(model_id)
+        current_model_id = model_id
+
+    input_size = 640
+
+    # perform inference
+    model.conf = threshold
+    results = model(image, size=input_size)
+    numpy_image = results.render()[0]
+    output_image = Image.fromarray(numpy_image)
+    return output_image
+
+
+gr.Interface(
+    title=app_title,
+    description="Created by 'keremberke'",
+    article=article,
+    fn=predict,
+    inputs=[
+        gr.Image(type="pil"),
+        gr.Slider(maximum=1, step=0.01, value=0.25),
+        gr.Dropdown(models_ids, value=models_ids[-1]),
+    ],
+    outputs=gr.Image(type="pil"),
+    examples=examples,
+    cache_examples=True if examples else False,
+).launch(enable_queue=True)

requirements.txt

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+yolov5==7.0.5
+gradio==3.15.0
+torch
+huggingface-hub