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from transformers import AutoTokenizer, AutoModel
import torch
import gradio as gr
# Load Bio_ClinicalBERT
tokenizer = AutoTokenizer.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
model = AutoModel.from_pretrained("emilyalsentzer/Bio_ClinicalBERT")
def embed_text(text):
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
with torch.no_grad():
outputs = model(**inputs)
# Mean pooling
embedding = outputs.last_hidden_state.mean(dim=1).squeeze().tolist()
return embedding
iface = gr.Interface(
fn=embed_text,
inputs=gr.Textbox(lines=5, label="Enter patient text"),
outputs="json",
title="Clinical Text Embedding API (Bio_ClinicalBERT)"
)
iface.launch()
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