| import gradio as gr | |
| from fastai.vision.all import * | |
| import skimage | |
| import pathlib | |
| plt = platform.system() | |
| if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath | |
| learn = load_learner('export.pkl') | |
| labels = learn.dls.vocab | |
| def predict(img): | |
| img = PILImage.create(img) | |
| pred,pred_idx,probs = learn.predict(img) | |
| return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
| title = "Face condition Analyzer" | |
| description = "A face condition detector trained on the custom dataset with fastai. Created using Gradio and HuggingFace Spaces." | |
| examples = [['harmonal_acne.jpg'],['forehead_wrinkles.jpg'],['oily_skin.jpg']] | |
| enable_queue=True | |
| gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=3),title=title, | |
| description=description,examples=examples,enable_queue=enable_queue).launch(share=True) |