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# 1. Import the required libraries
import torch
import gradio as gr
from typing import Dict
from transformers import pipeline
# 2. Define our function to use with our model
spanish_sentence_classification_by_school_subject_pipeline = pipeline(task="text-classification",
model="tonicanada/learn_hf_spanish_sentence_classification_by_school_subject",
top_k=1,
device="cuda" if torch.cuda.is_available() else "cpu",
batch_size=32)
def classify_text(text):
# Usa el clasificador
result = spanish_sentence_classification_by_school_subject_pipeline(text)
# Extrae la etiqueta y la puntuación (score)
label = result[0][0]['label']
score = result[0][0]['score']
return {label: score} # Devuelve un diccionario con la etiqueta y la puntuación
# 3. Create a Gradio interface
description = """
Un clasificador de texto que indica a qué asignatura se refiere la frase.
Fine-tuned desde [DistilBERT](https://huggingface.co/distilbert/distilbert/distilbert-base-multilingual-cased) con un [pequeño dataset de frases acerca asignaturas escolares](https://huggingface.co/datasets/tonicanada/learn_hf_spanish_sentence_classification_by_school_subject).
"""
demo = gr.Interface(
fn = classify_text,
inputs = "text",
outputs=gr.Label(num_top_classes=10),
title="📚🔍 Clasificador de asignaturas",
description=description,
examples=[["Matemáticas: 5 al cuadrado es 25"],
["Geografía: París es la capital de Francia"]])
# 4. Launch the interface
if __name__ == "__main__":
demo.launch()
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