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import gradio as gr
import spaces
from transformers import pipeline
from typing import List, Dict, Any
import torch
def merge_tokens(tokens: List[Dict[str, any]]) -> List[Dict[str, any]]:
merged_tokens = []
for token in tokens:
if merged_tokens and token['entity'].startswith('I-') and merged_tokens[-1]['entity'].endswith(token['entity'][2:]):
last_token = merged_tokens[-1]
last_token['word'] += token['word'].replace('##', '')
last_token['end'] = token['end']
last_token['score'] = (last_token['score'] + token['score']) / 2
else:
merged_tokens.append(token)
return merged_tokens
# Determine device
device = 0 if torch.cuda.is_available() else -1
# Initialize Model
get_completion = pipeline("ner", model="kazalbrur/BanglaMedNER", device=device)
@spaces.GPU(duration=120)
def ner(input: str) -> Dict[str, Any]:
try:
output = get_completion(input)
merged_tokens = merge_tokens(output)
return {"text": input, "entities": merged_tokens}
except Exception as e:
return {"text": input, "entities": [], "error": str(e)}
####### GRADIO APP #######
title = """<h1 id="title"> Bangla Bio-Medical Entity Recognition </h1>"""
description = """
- The model used for Recognizing entities [BERT-BASE-NER](https://huggingface.co/kazalbrur/BanglaMedNER).
"""
css = '''
h1#title {
text-align: center;
}
'''
theme = gr.themes.Soft()
demo = gr.Blocks(css=css, theme=theme)
with demo:
gr.Markdown(title)
gr.Markdown(description)
gr.Interface(
fn=ner,
inputs=[gr.Textbox(label="Enter Your Text to Find Entities", lines=10)],
outputs=[gr.HighlightedText(label="Text with entities")],
allow_flagging="never"
)
demo.launch()
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