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



# repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
repo_id = "rasaenluis3/e3Modelo"

learner = from_pretrained_fastai(repo_id)
labels = ['0','1','2','3','4','5']

# Auxiliar
def catToValue(cat):
  if cat == '0':
    return 'sadness'
  elif cat == '1':
    return 'joy' # wonderhoy :)
  elif cat == '2':
    return 'love'
  elif cat == '3':
    return 'anger'
  elif cat == '4':
    return 'fear'
  elif cat == '5':
    return 'surprise'
  else:
    return str(cat)

# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(texto):
    print(texto)
    pred,pred_idx,probs = learner.predict(texto)
    si = {catToValue(labels[i]): float(probs[i]) for i in range(len(labels))}
    print(si)
    return si
    
# Creamos la interfaz y la lanzamos. 
gr.Interface(fn=predict, inputs=gr.inputs.Textbox(lines=3,label="Escríbeme in english please"), outputs=gr.outputs.Label(num_top_classes=3)).launch(share=False)