| # 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() | |