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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 = "mrdbourke/learn_hf_food_not_food_text_classifier-distilbert-base-uncased",
model = "YarnGuo/learn_hf_food_not_food_text_classifier-distilbert-base-uncased",
device = "cuda" if torch.cuda.is_available() else "cpu",
batch_size = 32,
top_k = None
)
#output is a list of dict
outputs = food_not_food_classifier(text)[0]
output_dict = {}
for item in outputs:
output_dict[item["label"]] = item["score"]
return output_dict
demo = gr.Interface(
fn = food_not_food_classifier,
inputs = "text",
outputs = gr.Label(num_top_classes=2),
title = "Food or Not Food Classifer",
description = "A text classfier to say a senstence about food or not food",
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."]]
)
if __name__ == "__main__":
demo.launch()
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