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)