from fastai.text.all import *
import gradio as gr
from huggingface_hub import from_pretrained_fastai

# Cargamos el learner
repo_id = "ikerml/twitter_class"

learner = from_pretrained_fastai(repo_id)

# Definimos las etiquetas de nuestro modelo
labels = ['❤','😍','😂','💕','🔥','😊','😎','✨','💙','😘','📷','🇺🇸','☀','💜','😉','💯','😁','🎄','📸','😜']

example1 = "I like potatoe"
example2 = "My house is on fire"
example3 = "The other day, i eat a banana"

# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(text):
    pred,pred_idx,probs = learner.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.inputs.Textbox(), outputs=gr.outputs.Label(),examples=[example1,example2,example3]).launch(share=False)