Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,9 +1,3 @@
-"""
-Enhanced Streamlit app for biomedical ontology retrieval on Hugging Face
-Supports multiple vocabularies with precomputed embeddings and t-SNE visualization
-Now supports multiple embedding models: BRIDGE, SapBERT, and OpenAI
-"""
-
 import streamlit as st
 import pandas as pd
 import numpy as np
@@ -34,7 +28,6 @@ MODELS_CONFIG = {
     "openai": {
         "display_name": "OpenAI",
         "type": "openai",
-        # name of the embeddings model to use (must match your account/model availability)
         "model": "text-embedding-3-large"
     }
 }
@@ -487,7 +480,7 @@ def main():
     # Info in sidebar
     st.sidebar.success(f"✓ Loaded {len(df_concepts)} concepts")
     st.sidebar.info(f"📊 Active vocabularies: {len(selected_vocabs)}")
-    st.sidebar.info(f"🤖 Model: {selected_model_name}\n💻 Device: {device}")
+    st.sidebar.info(f"Model: {selected_model_name}\n Device: {device}")
     
     # Display vocabulary statistics
     with st.sidebar.expander("📈 Vocabulary Statistics"):
@@ -499,7 +492,7 @@ def main():
     st.subheader("🔍 Query")
     user_input = st.text_area(
         "Enter clinical or biomedical text:",
-        placeholder="e.g., Migraine with aura",
+        placeholder="e.g. "Migraine with aura" or "ICD10 G43.1"",
         height=120,
         help="Enter any clinical description or biomedical concept to find similar codes"
     )