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| import gradio as gr | |
| from transformers import pipeline | |
| # Load the pre-trained NER model | |
| model = pipeline("ner", model="/home/user/app/mendobert/", tokenizer="indolem/indobert-base-uncased") | |
| # basemodel = pipeline("ner", model="/home/user/app/base-model/", tokenizer="indolem/indobert-base-uncased") | |
| def text_analysis(text): | |
| doc = model(text) | |
| pos_tokens = [] | |
| for token in doc: | |
| pos_tokens.extend([(token.text, token.pos_), (" ", None)]) | |
| return pos_tokens, pos_count, html | |
| demo = gr.Interface( | |
| text_analysis, | |
| gr.Textbox(placeholder="Enter sentence here..."), | |
| ["highlight", "json", "html"], | |
| examples=[ | |
| ["Aspartylglucosaminuria (AGU) adalah gangguan metabolisme glikoprotein langka."], | |
| ["Mutasi germ - line dari gen BRCA1 membuat wanita cenderung mengalami kanker payudara dini dengan mengorbankan fungsi presumtif gen sebagai penekan tumor."], | |
| ], | |
| ) | |
| demo.launch() | |