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| from PIL import Image | |
| import tensorflow | |
| import gradio as gr | |
| from tensorflow.keras.models import load_model | |
| model = load_model('model') | |
| def infer(img): | |
| cartoonGAN = model.signatures["serving_default"] | |
| img = np.array(img.convert("RGB")) | |
| img = np.expand_dims(img, 0).astype(np.float32) / 127.5 - 1 | |
| out = cartoonGAN(tf.constant(img))['output_1'] | |
| out = ((out.numpy().squeeze() + 1) * 127.5).astype(np.uint8) | |
| return out | |
| title = "CartoonGAN" | |
| description = "Gradio Demo for AnimeGanv2 Face Portrait. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below." | |
| #description = "Gradio Demo for AnimeGanv2 Face Portrait. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below. Please use a cropped portrait picture for best results similar to the examples below." | |
| iface = gr.Interface(infer, gr.inputs.Image(type="pil"), "image",title=title,description=description) | |
| iface.launch() | |