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from transformers import pipeline
import gradio as gr
from gradio.mix import Parallel
pretrained_sentiment = "w11wo/indonesian-roberta-base-sentiment-classifier"
pretrained_ner = "cahya/bert-base-indonesian-NER"
sentiment_pipeline = pipeline(
"sentiment-analysis",
model=pretrained_sentiment,
tokenizer=pretrained_sentiment,
return_all_scores=True
)
ner_pipeline = pipeline(
"ner",
model=pretrained_ner,
tokenizer=pretrained_ner
)
examples = [
"Masyarakat sangat kecewa dengan tragedi Kanjuruhan",
"Jokowi mengutuk kepolisian atas kerusuhan yang terjadi di Malang"
]
def sentiment_analysis(text):
output = sentiment_pipeline(text)
return {elm["label"]: elm["score"] for elm in output[0]}
def ner(text):
output = ner_pipeline(text)
return {"text": text, "entities": output}
sentiment_demo = gr.Interface(
fn=sentiment_analysis,
inputs=gr.Textbox(placeholder="Enter sentence here..."),
outputs="label",
interpretation="default",
examples=examples)
ner_demo = gr.Interface(
ner,
gr.Textbox(placeholder="Enter sentence here..."),
gr.HighlightedText(),
examples=examples)
if __name__ == "__main__":
Parallel(sentiment_demo, ner_demo,
inputs=gr.Textbox(placeholder="Enter sentence here..."),
examples=examples).launch() |