app.py

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]:
  # Set up food_not_food_classifer pipeline
  food_not_food_classifier_pipeline = pipeline(task="text-classification",
                                      model="StellaSoh/learn_hf_food_not_food_text_classifier-distilbert-base-uncased",
                                      batch_size=32,
                                      device = 0 if torch.cuda.is_available() else -1,
                                      top_k=None)       # top_k=None --> all possible labels

  # Get the output from the pipeline
  outputs = food_not_food_classifier_pipeline(text)[0]

  # Format output for Gradio
  output_dict = {}
  for item in outputs:
    output_dict[item["label"]] = item["score"]

  return output_dict

#3. Create a Gradio interface
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) on a [small dataset of food and not food text](https://huggingface.co/datasets/mrdbourke/learn_hf_food_not_food_image_captions).

See [source code](https://github.com/mrdbourke/learn-huggingface/blob/main/notebooks/hugging_face_text_classification_tutorial.ipynb).
"""

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."]])

# 4. Launch the interface
if __name__ == "__main__":
    demo.launch()