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Update app.py

a61d522 over 4 years ago

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from fastai.vision.all import *
import gradio as gr


# Cargamos el learner
learn = load_learner('export.pkl')

# Definimos las etiquetas de nuestro modelo
labels = ['Negative','Neutral','Positive']


# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(text):
    pred,pred_idx,probs = learn.predict(text)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}
    
# Creamos la interfaz y la lanzamos. 
gr.Interface(fn=predict, inputs=gr.Textbox(), outputs=gr.outputs.Label(num_top_classes=3),examples=['Some hate humanity, but I love humanity so much.','I have this old 2006 BusinessWeek framed as a reminder. The “risky bet” that Wall Street disliked was AWS, which generated revenue of more than $62 billion last year.']).launch(share=False)

	from fastai.vision.all import *
	import gradio as gr


	# Cargamos el learner
	learn = load_learner('export.pkl')

	# Definimos las etiquetas de nuestro modelo
	labels = ['Negative','Neutral','Positive']


	# Definimos una función que se encarga de llevar a cabo las predicciones
	def predict(text):
	pred,pred_idx,probs = learn.predict(text)
	return {labels[i]: float(probs[i]) for i in range(len(labels))}

	# Creamos la interfaz y la lanzamos.
	gr.Interface(fn=predict, inputs=gr.Textbox(), outputs=gr.outputs.Label(num_top_classes=3),examples=['Some hate humanity, but I love humanity so much.','I have this old 2006 BusinessWeek framed as a reminder. The “risky bet” that Wall Street disliked was AWS, which generated revenue of more than $62 billion last year.']).launch(share=False)