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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 = ["0","1","2","3"] | |
| # Definimos una función que se encarga de llevar a cabo las predicciones | |
| def predict(string): | |
| print(learn.predict(string)) | |
| pred,pred_idx,probs = learn.predict(string) | |
| 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=['it was so annoying to watch the president','I am so glad to see you'], title="Natural Language Model to classify the feelings of a message", description="This model has been trained with messages obtained from Twitter. Its purpose is to classify the possible feelings that a message might express. The labels obtained have the following meaning:\n 0: anger\n 1: joy\n 2: optimism\n 3: sadness").launch(share=False) |