Update app.py

app.py

@@ -1,11 +1,18 @@
+"""
+app.py
+------
+This is the main Streamlit application. It provides a clinician-friendly interface to input a clinical query,
+displays the generated answer, and visualizes key clinical concepts via an interactive knowledge graph.
+"""
+
 import subprocess
 import sys
+import os
 
-# Force reinstall pydantic v1 before any other imports to ensure compatibility with Chromadb.
+# Force reinstall pydantic v1 to ensure compatibility with Chromadb.
 subprocess.run([sys.executable, "-m", "pip", "install", "--force-reinstall", "pydantic==1.10.7"])
 
-import os
-# Set HF Hub timeout to 60 seconds.
+# Set the Hugging Face Hub timeout.
 os.environ["HF_HUB_TIMEOUT"] = "60"
 
 import streamlit as st
@@ -14,16 +21,20 @@ from backend import process_medical_query, docs_cache
 from visualization import create_medical_graph
 
 def main():
+    st.set_page_config(page_title="AI-Powered Medical Knowledge Graph Assistant",
+                       layout="wide")
     st.title("AI-Powered Medical Knowledge Graph Assistant")
     st.markdown(
-        "**Using BioGPT-Large-PubMedQA + PubMed + Chroma** for advanced retrieval-augmented generation."
+        """
+        **Using BioGPT-Large-PubMedQA + PubMed + Chroma** for advanced retrieval-augmented generation.
+        
+        Enter your clinical query below to retrieve and synthesize relevant medical literature.
+        """
     )
 
-    # Clinical query input – designed for clarity and ease-of-use.
     user_query = st.text_input("Enter biomedical/medical query", "Malaria and cough treatment")
     if st.button("Submit"):
         with st.spinner("Generating answer..."):
-            # Process the query using the streamlined backend.
             final_answer, sub_questions, initial_answer, critique = process_medical_query(user_query)
 
         st.subheader("AI Answer")