| import torch | |
| import gradio as gr | |
| from typing import Dict | |
| from transformers import pipeline | |
| def food_not_food_classifier(text:str) -> Dict[str, float]: | |
| food_not_food_classifier = pipeline( | |
| task = "text-classification", | |
| #model = "mrdbourke/learn_hf_food_not_food_text_classifier-distilbert-base-uncased", | |
| model = "YarnGuo/learn_hf_food_not_food_text_classifier-distilbert-base-uncased", | |
| device = "cuda" if torch.cuda.is_available() else "cpu", | |
| batch_size = 32, | |
| top_k = None | |
| ) | |
| #output is a list of dict | |
| outputs = food_not_food_classifier(text)[0] | |
| output_dict = {} | |
| for item in outputs: | |
| output_dict[item["label"]] = item["score"] | |
| return output_dict | |
| demo = gr.Interface( | |
| fn = food_not_food_classifier, | |
| inputs = "text", | |
| outputs = gr.Label(num_top_classes=2), | |
| title = "Food or Not Food Classifer", | |
| description = "A text classfier to say a senstence about food or not food", | |
| 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."]] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |