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# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
# %% auto 0
__all__ = ['tokenizer', 'device', 'model', 'CLASS_LABELS', 'sentence', 'label', 'examples', 'intf', 'classify_sentiment']
# %% ../app.ipynb 2
import gradio as gr
import torch
from layer import Model
# %% ../app.ipynb 3
from transformers import BertTokenizerFast
tokenizer = BertTokenizerFast.from_pretrained('bert-base-cased')
# %% ../app.ipynb 4
device = "cuda" if torch.cuda.is_available() else "cpu"
model = torch.load('./model.pt', map_location=torch.device('cpu')).to(device)
model.eval()
# %% ../app.ipynb 5
CLASS_LABELS = ['Negative', 'Positive']
# %% ../app.ipynb 6
def classify_sentiment(sentence):
tokens = tokenizer(sentence)
pred = model(torch.tensor([tokens['input_ids']]).to(device), [len(tokens)]).item()
return dict(zip(CLASS_LABELS, [1 - pred, pred]))
# %% ../app.ipynb 7
sentence = gr.inputs.Textbox()
label = gr.outputs.Label(label='sentiment')
examples = ["best movie I've ever seen", 'Worst movie ever.']
intf = gr.Interface(fn=classify_sentiment,
inputs=sentence,
outputs=label,
title='Sentiment analysis',
examples=examples)
intf.launch(inline=False)
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