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import os
import gradio as gr
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
HF_TOKEN = os.environ.get('HF_TOKEN')
model_checkpoint = "besijar/dspa_review_classification"
tokeniser = AutoTokenizer.from_pretrained(model_checkpoint, use_auth_token=HF_TOKEN)
model = TFAutoModelForSequenceClassification.from_pretrained(model_checkpoint, use_auth_token=HF_TOKEN)
example_review = "Tully's House Blend is the perfect K-Cup for me. Sure, I occasionally enjoy the special flavors.....Mocha, Italian roast, French vanilla, but my favorite 'go-to'coffee is House Blend. Wakes me up in the morning with it's coffee house full hearty taste."
def review_classify(review):
review = tokeniser.encode(review)
review = model.predict([review])
return int(review.logits.argmax())
iface = gr.Interface(review_classify,
title="Review Classification using DistilRoBERTa",
inputs=[gr.Text(label="Review")],
outputs=[gr.Number(label="Rating", precision=0)],
examples=[example_review])
iface.launch() |