| # 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) | |
| # 3. Create a Gradio interface | |
| description = """ | |
| Un clasificador de texto que indica a qué asignatura se refiere la frase. | |
| Fine-tuned from [DistilBERT](https://huggingface.co/distilbert/distilbert/distilbert-base-multilingual-cased) on a [small dataset of food and not food text](https://huggingface.co/datasets/mrdbourke/learn_hf_food_not_food_image_captions). | |
| """ | |
| demo = gr.Interface( | |
| fn = spanish_sentence_classification_by_school_subject_pipeline, | |
| 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() | |