| import numpy as np | |
| import gradio as gr | |
| from PIL import Image | |
| import keras | |
| from huggingface_hub import from_pretrained_keras | |
| model = from_pretrained_keras("keras-io/lowlight-enhance-mirnet", compile=False) | |
| examples = ['examples/179.png', 'examples/493.png', 'examples/780.png'] | |
| def infer(original_image): | |
| image = keras.utils.img_to_array(original_image) | |
| image = image.astype("float32") / 255.0 | |
| image = np.expand_dims(image, axis=0) | |
| output = model.predict(image) | |
| output_image = output[0] * 255.0 | |
| output_image = output_image.clip(0, 255) | |
| output_image = output_image.reshape( | |
| (np.shape(output_image)[0], np.shape(output_image)[1], 3) | |
| ) | |
| output_image = np.uint32(output_image) | |
| return output_image | |
| iface = gr.Interface( | |
| fn=infer, | |
| inputs=gr.inputs.Image(type="pil"), | |
| outputs=gr.outputs.Image(type="pil", label="predicted"), | |
| title=title, | |
| description=description, | |
| examples=examples, | |
| enable_queue=True) | |
| iface.launch(debug=True) |