File size: 2,255 Bytes
97dc5ff |
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 51 52 53 54 55 56 57 58 59 60 61 62 63 |
# 1. Import the 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 set_device():
if torch.cuda.is_available():
device = torch.device("cuda")
elif torch.backends.mps.is_available() and torch.backends.mps.is_built():
device = torch.device("mps")
else:
device = torch.device("cpu")
return device
DEVICE = set_device()
# 1. Create a function to take a String input
def food_not_food_classifier(text: str) -> Dict[str, float]:
# Setup food not food text classifier
food_not_food_classifier_pipeline = pipeline(task="text-classification",
model="mdarefin/learn_hf_food_not_food_text_classifier-distilbert-base-uncased",
batch_size=32,
device=DEVICE,
top_k=None) # top_k = None => Return all possible labels
# Get the outputs from our pipeline
outputs = food_not_food_classifier_pipeline(text)[0]
# Format output from Gradio
output_dict = {}
for item in outputs:
output_dict[item["label"]] = item["score"]
return output_dict
# 3. Create a Gradio interface with details about our app
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/Adnan-edu/hugging_custom_ai_model).
"""
demo = gr.Interface(fn=food_not_food_classifier,
inputs="text",
outputs=gr.Label(num_top_classes=2), # show top 2 classes
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()
|