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# 1. Import the required packages
import torch
import gradio as gr
from typing import Dict
from transformers import pipeline
# 2. Define function to use our model on given text
def food_not_food_classifier(text: str) -> Dict[str, float]:
"""
Takes an input string of text and classifies it into food/not_food in the form of a dictionary.
"""
# 2. Setup the pipeline to use the local model (or Hugging Face model path)
food_not_food_classifier = pipeline(task='text-classification',
model = "shivajimallela/learn_hf_food_not_food_classifier-ditsilbert-base-uncased",
device= "cuda" if torch.cuda.is_available() else "cpu",
top_k = None)
# 3. Get outputs from pipeline (as a list of dicts)
outputs = food_not_food_classifier(text)[0]
# print(food_not_food_classifier(text))
# 4. Format output for Gradio (e.g. {"label_1": probability_1, "label_2": probability_2})
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/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),
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()
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