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| from fastai.text.all import * | |
| import gradio as gr | |
| # Cargamos el learner | |
| learn = load_learner('export.pkl') | |
| # Definimos las etiquetas de nuestro modelo | |
| labels = ["anger", "joy", "optimism", "sadness"] | |
| # Definimos una función que se encarga de llevar a cabo las predicciones | |
| def predict(txt): | |
| pred,pred_idx,probs = learn.predict(txt) | |
| return {labels[i]: float(probs[i]) for i in range(len(labels))} | |
| # Creamos la interfaz y la lanzamos. | |
| gr.Interface(fn=predict, inputs="text", outputs=gr.outputs.Label(),examples=["@user but your pussy was weak from what I heard so stfu up to me bitch . You got to threaten him that your pregnant .\n", "Worry is a down payment on a problem you may never have. Joyce Meyer. #motivation #leadership #worry\n", "My roommate: it's okay that we can't spell because we have autocorrect. #terrible #firstworldprobs\n", "it's pretty depressing when u hit pan on ur favourite highlighter\n", "Tiller and breezy should do a collab album. Rapping and singing prolly be fire\n"]).launch(share=False) | |