| from fastai.vision.all import * | |
| import gradio as gr | |
| from PIL import Image | |
| import numpy as np | |
| from fastai.vision.core import PILImage | |
| def is_cat(x): return x[0].isupper() | |
| learn = load_learner('model.pkl') | |
| categories = ('Dog','Cat') | |
| def classify_image(img): | |
| if isinstance(img, np.ndarray): | |
| img = Image.fromarray(img.astype('uint8'), 'RGB') | |
| if not isinstance(img, PILImage): | |
| img = PILImage.create(img) | |
| pred,idx,probs=learn.predict(img) | |
| return dict(zip(categories,map(float,probs))) | |
| image = gr.Image(width=192, height=192) | |
| label = gr.Label() | |
| examples = ['dog.jpg','cat.jpg','dog-cat.jpg'] | |
| intf= gr.Interface(fn=classify_image,inputs=image,outputs=label,examples=examples) | |
| intf.launch(inline=False) |