| import gradio as gr |
| from fastai.vision.all import * |
| import skimage |
|
|
| learn = load_learner('curlyhairmodel0.3.pkl') |
|
|
| categories = ('Curly', 'Straight') |
|
|
| def predict(img): |
| img = PILImage.create(img) |
| pred,pred_idx,probs = learn.predict(img) |
| return dict(zip(categories, map(float,probs))) |
|
|
| title = "Hair Type Classifier" |
| description = "A hair type classifier trained on the Oxford Pets dataset with fastai." |
| examples = [['c1.jpeg'],['c2.jpeg'],['c3.jpeg'], |
| ['c4.jpeg'],['c5.jpeg'],['c6.jpeg'], |
| ['c7.jpeg'],['c9.jpeg'],['c10.jpeg'], |
| ['c11.jpeg'],['c12.jpeg'], |
| ['s2.jpeg'],['s4.jpeg'],['s5.jpeg'], |
| ['s6.jpeg'],['s7.jpeg'],['s12.jpeg'], |
| ['sc1.jpeg'],['sc2.jpeg'],['sc3.jpeg'], |
| ['sc4.jpeg'],['sc5.jpeg'],['sc6.jpeg'] |
| ] |
| interpretation='default' |
| enable_queue=True |
|
|
| gr.Interface( |
| fn=predict, |
| inputs=gr.inputs.Image(shape=(512, 512)), |
| outputs=gr.outputs.Label(), |
| title=title, |
| description=description, |
| examples=examples, |
| interpretation=interpretation, |
| enable_queue=enable_queue).launch() |
