File size: 1,823 Bytes
06e3f9d 45c2f6a 06e3f9d 45c2f6a 3932b32 06e3f9d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | # Import required packages
import torch
import gradio as gr
from typing import Dict
from transformers import pipeline
# Define a function to use the model
def food_not_food_classifier(text: str) -> Dict[str, float]:
# Set up food not food text classifier
food_not_food_classifier_pipeline = pipeline(task="text-classification",
model="karenwky/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)
# 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
# Create a Gradio interface with detilas about the app
description = """
A text classifier to determine whether a sentence pertains to food or not.
Fine-tuned from [DistilBERT](https://huggingface.co/distilbert/distilbert-base-uncased) on a [dataset](https://huggingface.co/datasets/mrdbourke/learn_hf_food_not_food_image_captions) of LLM-generated image captions categorizing food and non-food topics.
Follow-along [notebook](https://colab.research.google.com/github/karenwky/learn-hugging-face/blob/main/01_text-classification/learn_hugging_face_text_classification.ipynb).
"""
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=[["Today is a sunny day."],
["Pineapple fried rice."]]
)
# Launch the interface
if __name__ == "__main__":
demo.launch()
|