Spaces:
Sleeping
Sleeping
| import torch | |
| import gradio as gr | |
| from transformers import pipeline | |
| def food_not_food_classifier(text): | |
| # Set up text classification pipeline | |
| food_not_food_classifier = pipeline(task="text-classification", | |
| model="mrdbourke/learn_hf_food_not_food_text_classifier-distilbert-base-uncased", | |
| device="cuda" if torch.cuda.is_available() else "cpu", | |
| top_k=None) # return all possible scores (not just top-1) | |
| # Get outputs from pipeline (as a list of dicts) | |
| outputs = food_not_food_classifier(text)[0] | |
| # Format output for Gradio (e.g. {"label_1": probability_1, "label_2": probability_2}) | |
| output_dict = {} | |
| for item in outputs: | |
| output_dict[item["label"]] = item["score"] | |
| return output_dict | |
| description = """ | |
| A text classifier to determine if a sentence is about food or not food. | |
| TK - See source code: | |
| """ | |
| 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="🍗🚫🥑 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."]]) | |
| if __name__ == "__main__": | |
| demo.launch() | |