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



# repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
repo_id = "igmarco/AWD_LSTM-text-classification"

learner = from_pretrained_fastai(repo_id)
labels = ['hate speech', 'offensive language', 'neither']

# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(txt):
    pred,pred_idx,probs = learner.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=gr.Textbox(lines=2, placeholder="Text Here..."), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=False)