| import gradio as gr | |
| import numpy as np | |
| from huggingface_hub import from_pretrained_keras | |
| model = from_pretrained_keras("mouaff25/tyre_quality_classifier") | |
| labels = ["defective", "good"] | |
| def classify_image(inp: np.ndarray): | |
| inp = inp.reshape((-1, 224, 224, 3)) | |
| prediction = model.predict(inp).flatten() | |
| confidences = { | |
| "defective": 1 - float(prediction[0]), | |
| "good": float(prediction[0]) | |
| } | |
| return confidences | |
| demo = gr.Interface(fn=classify_image, | |
| inputs=gr.Image(shape=(224, 224)), | |
| outputs=gr.Label(num_top_classes=2), | |
| examples=["./examples/defective.jpg", "./examples/good.jpg"], | |
| title="Tyre Quality Classifier", | |
| description="This model can distinguish between good and defective tyres. Upload an image of a tyre to see the model in action!") | |
| demo.launch() |