| # 1. Importamos las librerías necesarias | |
| import torch | |
| import gradio as gr | |
| from typing import Dict | |
| from transformers import pipeline | |
| # 2. Deifnimos nuestra función para usar el modelo | |
| food_not_food_classifier = pipeline(task="text-classification", | |
| model="tonicanada/learn_hf_food_not_food_text_classifier-distilbert-base-uncased", | |
| top_k=1, | |
| device="cuda" if torch.cuda.is_available() else "cpu", | |
| batch_size=32) | |
| def classify_text(text): | |
| # Usa el clasificador | |
| result = food_not_food_classifier(text) | |
| # Ahora accedemos al primer diccionario en la primera lista | |
| return result[0][0]['label'], result[0][0]['score'] | |
| # 3. Create a Gradio interface | |
| description = """ | |
| A text classifier to determine if a sentence is about food or not food. | |
| Fine-tuned from [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) on a [small dataset of food and not food text](https://huggingface.co/datasets/mrdbourke/learn_hf_food_not_food_image_captions). | |
| See [source code](https://github.com/mrdbourke/learn-huggingface/blob/main/notebooks/hugging_face_text_classification_tutorial.ipynb). | |
| """ | |
| demo = gr.Interface( | |
| fn = classify_text, | |
| inputs = "text", | |
| outputs=[gr.Label(num_top_classes=2), gr.Textbox()], | |
| title="🍗🚫🥑 Food or Not Food Text Classifier", | |
| description=description, | |
| examples=[["I whipped up a fresh batch of code, but it seems to have a syntax error."], | |
| ["A delicious photo of a plate of scrambled eggs, bacon and toast."]]) | |
| # 4. Launch the interface | |
| if __name__ == "__main__": | |
| demo.launch() | |