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

repo_id = "Igmata/TwitterFinancialSentiment"

learner = from_pretrained_fastai(repo_id)
labels = learner.dls.vocab[1]

# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(txt):
    pred, pred_idx, probs = learner.predict(txt)
    class_map = {0: 'bearish', 1: 'bullish', 2: 'neutral'}
    return {class_map[i]: float(probs[i]) for i in range(len(labels))}
    
# Creamos la interfaz y la lanzamos. 
gr.Interface(fn=predict, inputs=gr.Textbox(lines=5,placeholder="Escribe el tweet aquí"), outputs=gr.Label(num_top_classes=3)).launch(share=False)