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Update app.py
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app.py
CHANGED
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@@ -2,10 +2,9 @@ import gradio as gr
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from ultralytics import YOLO
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from PIL import Image
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# We laden de modellen in een dictionary zodat ze maar één keer geladen worden
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models = {
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"CowCatcherV15"
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"CowCatcherV16 (Experimental)": YOLO('cowcatcherV16.pt')
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}
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description_text = """
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@@ -14,23 +13,18 @@ By analyzing live footage from your barn cameras, it automatically detects "moun
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"""
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def predict(img, model_name, conf_threshold):
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# Selecteer het juiste model uit de dictionary
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selected_model = models[model_name]
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# Voer de voorspelling uit met de gekozen confidence
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results = selected_model(img, conf=conf_threshold)
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res_plotted = results[0].plot()
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return Image.fromarray(res_plotted[:, :, ::-1])
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# De Interface
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demo = gr.Interface(
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fn=predict,
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inputs=[
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gr.Image(type="pil", label="Upload barn image"),
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gr.Dropdown(
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choices=list(models.keys()),
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value="
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label="Select Model"
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),
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gr.Slider(
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from ultralytics import YOLO
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from PIL import Image
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models = {
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"CowCatcherV15": YOLO('cowcatcherV15.pt'),
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"CowCatcherV16 (Experimental)": YOLO('cowcatcherV16.pt')
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}
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description_text = """
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"""
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def predict(img, model_name, conf_threshold):
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selected_model = models[model_name]
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results = selected_model(img, conf=conf_threshold)
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res_plotted = results[0].plot()
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return Image.fromarray(res_plotted[:, :, ::-1])
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demo = gr.Interface(
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fn=predict,
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inputs=[
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gr.Image(type="pil", label="Upload barn image"),
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gr.Dropdown(
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choices=list(models.keys()),
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value="CowCatcherV15",
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label="Select Model"
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),
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gr.Slider(
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