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| import gradio as gr | |
| from transformers import pipeline | |
| # Load your fine-tuned SpanBERT ADR model (replace with your actual model name) | |
| ner_pipeline = pipeline( | |
| "token-classification", | |
| model="hazarri/fine_tuned_spanbert", # make sure this matches your uploaded model repo name | |
| aggregation_strategy="simple" | |
| ) | |
| def extract_adrs(text): | |
| results = ner_pipeline(text) | |
| if not results: | |
| return "No ADR entities found." | |
| formatted = [] | |
| for r in results: | |
| formatted.append(f"{r['word']} ({r['entity_group']}, score={r['score']:.2f})") | |
| return "\n".join(formatted) | |
| demo = gr.Interface( | |
| fn=extract_adrs, | |
| inputs=gr.Textbox(lines=5, placeholder="Enter a medical review..."), | |
| outputs="text", | |
| title="SpanBERT ADR Extraction API", | |
| description="Extracts adverse drug reactions (ADRs) from patient reviews using a fine-tuned SpanBERT model." | |
| ) | |
| demo.launch() | |