app.py

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

# Reemplaza con tu propio repo_id
repo_id = "NatanGarMar/entregable3"

# Cargar el modelo preentrenado de Hugging Face
learner = from_pretrained_fastai(repo_id)
labels = learner.dls.vocab

# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(text):
    result = learner.predict(text)
    if len(result) == 3:
        pred, pred_idx, probs = result
        return {labels[i]: float(probs[i]) for i in range(len(labels))}
    elif len(result) == 2:
        pred, probs = result
        return {labels[i]: float(probs[i]) for i in range(len(labels))}
    else:
        return {"Error": "Unexpected output format from learner"}

# Creamos la interfaz y la lanzamos
gr.Interface(fn=predict, inputs=gr.inputs.Textbox(lines=2, placeholder="Finantial Sentence"), outputs=gr.outputs.Label(num_top_classes=3), examples=['text1.txt', 'text2.txt']).launch(share=False)