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from fastai.text.all import *
import gradio as gr
# Cargamos el learner
learn = load_learner('export (2).pkl')
# Definimos las etiquetas de nuestro modelo
labels = ['0','1','2','3', '4', '5']
# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(sentence):
pred,pred_idx,probs = learn.predict(sentence)
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.Textbox(lines=1), outputs=gr.outputs.Label(num_top_classes=3), examples=['I feel so happy today','i feel selfish and spoiled'], description='0 = sadness || 1 = joy || 2 = love || 3 = anger || 4 = fear || 5 = surprise').launch(share=False)
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