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import gradio as gr
import pickle
categories = ['Telecaster', 'Stratocaster', 'Jazzmaster']
examples = ['stratocaster.jpt', 'telecaster.jpg', 'jazzmaster.jpg']
image = gr.inputs.Image(shape=(192, 192))
label = gr.outputs.Label()
# Load the trained model from the model.pkl file
with open("model.pkl", "rb") as f:
model = pickle.load(f)
def predict(image):
# image = cv2.resize(image, (224, 224))
# image = np.expand_dims(image, axis=0)
prediction, idx, probabilities = model.predict(image)
return dict(zip(categories, map(float, probabilities)))
iface = gr.Interface(fn=predict, inputs=image, outputs=label,
examples=examples, capture_session=True)
iface.launch()
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