embedding / app.py
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from sentence_transformers import SentenceTransformer
import gradio as gr
# Load a clinical sentence transformer
model = SentenceTransformer("pritamdeka/BioBERT-mnli-snli-scinli-scitail-mednli-stsb")
def embed_text(text):
embedding = model.encode(text, convert_to_numpy=True, normalize_embeddings=True).tolist()
return embedding
iface = gr.Interface(
fn=embed_text,
inputs=gr.Textbox(lines=5, label="Enter clinical text"),
outputs="json",
title="High-Accuracy Clinical Embeddings",
description="BioBERT fine-tuned for semantic similarity (STSB, MedNLI)"
)
iface.launch()