| # 1. Import required packages | |
| import torch | |
| import gradio as gr | |
| from typing import Dict | |
| from transformers import pipeline | |
| # 2. Define our function to use with our model. | |
| def food_not_food_classifier(text: str) -> Dict[str, float]: | |
| # 2. Setup food not food text classifier | |
| food_not_food_classifier_pipeline = pipeline(task="text-classification", | |
| model="adamNLP/learn_hf_food_not_food_text_classifier-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 | |
| # 3. get outputs from pipeline | |
| outputs = food_not_food_classifier_pipeline(text)[0] | |
| # 4. Format output for Gradio | |
| output_dict = {} | |
| for item in outputs: | |
| output_dict[item["label"]] = item["score"] | |
| return output_dict | |
| # 3. Create a Gradio interface -- we can use markdown text to create a description field | |
| 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) a [dataset of LLM generated food/not_food image captions](https://huggingface.co/datasets/mrdbourke/learn_hf_food_not_food_image_captions) | |
| """ | |
| ## create demo | |
| 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, but it seems to have syntax error"], | |
| ["A plate of waffles and bluberry syrup"]] | |
| ) | |
| # 4. Launch interface -- def Main function | |
| if __name__ == "__main__": | |
| demo.launch() | |