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| from fastai.vision.all import * | |
| import gradio as gr | |
| # Cargamos el learner | |
| learn = load_learner('export-practica1.pkl') | |
| # Definimos las etiquetas de nuestro modelo | |
| labels = learn.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 = learn.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.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Label(num_top_classes=3),examples=['NORMAL2-IM-0081-0001.jpeg','person135_bacteria_646.jpeg']).launch(share=True) | |