| import torch | |
| import gradio as gr | |
| from transformers import pipeline | |
| from typing import Dict | |
| def food_not_food_classifier(text: str) -> Dict[str, float]: | |
| food_not_food_classifier_pipeline = pipeline(task="text-classification", | |
| model='Zakariya007/hf_food_not_food_distilbert_base_uncased', | |
| batch_size=32, | |
| device="cuda" if torch.cuda.is_available() else "cpu", | |
| top_k=None) # top_k=None => return all possible labels | |
| outputs = food_not_food_classifier_pipeline(text)[0] | |
| output_dict = {} | |
| for item in outputs: | |
| output_dict [item["label"] ] = item["score"] | |
| return output_dict | |
| description = """ | |
| This demo uses a fine-tuned Natural Language Processing (NLP) model to distinguish between descriptions of actual food and non-food related text. | |
| While many classifiers struggle with linguistic nuances, this model is designed to recognize the difference between a recipe and a metaphorical phrase (like "piece of cake"). | |
| """ | |
| demo = gr.Interface(fn=food_not_food_classifier, | |
| inputs="text", | |
| outputs=gr.Label(num_top_classes=2), # show top 2 classes (that's all we have) | |
| title=" 🍓 Is it Food?", | |
| 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. "], | |
| ['A delicious plate of spaghetti with meatballs'] | |
| ]) | |
| if __name__ == "__main__": | |
| demo.launch() | |