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# app.py
import gradio as gr
from gliner import GLiNER
import re
# Load GLiNER-BioMed Small model
model = GLiNER.from_pretrained("Ihor/gliner-biomed-bi-small-v1.0", trust_remote_code=True)
labels = ["Population", "Intervention", "Comparison", "Outcome"]
def parse_nbib(file):
content = file.read().decode("utf-8")
entries = re.findall(r'(?=PMID- .+?)(.*?)(?=(?:PMID- |\Z))', content, re.DOTALL)
refs = []
for entry in entries:
title = ""
abstract = ""
for line in entry.splitlines():
if line.startswith("TI - "):
title = line.replace("TI - ", "").strip()
elif line.startswith("AB - "):
abstract += " " + line.replace("AB - ", "").strip()
if title or abstract:
refs.append(f"{title}. {abstract.strip()}")
return refs
def extract_pico_from_nbib(file):
refs = parse_nbib(file)
results = []
for ref in refs:
entities = model.predict_entities(ref, labels, threshold=0.5)
results.append({"text": ref, "pico": entities})
return results
def extract_from_text(text):
entities = model.predict_entities(text, labels, threshold=0.5)
return {ent['label']: ent['text'] for ent in entities}
demo = gr.Interface(
fn=extract_from_text,
inputs=gr.Textbox(lines=10, placeholder="Paste biomedical abstract here..."),
outputs="json",
title="PICO Extractor (GLiNER-BioMed Small)",
description="Extract PICO (Population, Intervention, Comparison, Outcome) elements from biomedical text using the GLiNER-BioMed Small model."
)
nbib_demo = gr.Interface(
fn=extract_pico_from_nbib,
inputs=gr.File(file_types=[".nbib"]),
outputs="json",
title="PICO Extractor from NBIB",
description="Upload .nbib files to extract PICO elements using GLiNER-BioMed Small."
)
tabs = gr.TabbedInterface([demo, nbib_demo], ["From Text", "From NBIB File"])
if __name__ == "__main__":
tabs.launch()
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