| 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="tanvircr7/learn_hf_food_not_food_text_classifier-distilbert-base-uncased", | |
| device="cuda" if torch.cuda.is_available() else "cpu", | |
| top_k=None) | |
| outputs = food_not_food_classifier(text)[0] | |
| 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. | |
| Fine-tuned from DistilBERT | |
| """ | |
| demo = gr.Interface(fn=food_not_food_classifier, | |
| inputs="text", | |
| outputs=gr.Label(num_top_classes=2), | |
| title="Food or Not Food Text Classifier", | |
| description=description, | |
| examples=[["I whipped up a fresh batch of code"],["A delicious photo of a plate"]] | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |