| from huggingface_hub import from_pretrained_fastai | |
| import gradio as gr | |
| from fastai.vision.all import * | |
| # repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME" | |
| repo_id = "kevanme/Practica1" | |
| learner = from_pretrained_fastai(repo_id) | |
| labels = learner.dls.vocab | |
| # Definimos una función que se encarga de llevar a cabo las predicciones | |
| def predict(img): | |
| #img = PILImage.create(img) | |
| pred,pred_idx,probs = learner.predict(img) | |
| return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
| # Creamos la interfaz y la lanzamos. | |
| gr.Interface(fn=predict, inputs=gr.Image(type="pil", image_mode="RGBA"), outputs=gr.Label(num_top_classes=3),examples=['0000118560.jpg','1000827761.jpg']).launch(share=False) | |