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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/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) |