Update app.py

app.py

@@ -15,12 +15,6 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 
-# Sidebar navigation
-tab = st.sidebar.radio(
-    "Navigation",
-    ["📁 Manage Documents", "📝 Generate Templates", "🔍 Search Documents"]
-)
-
 def get_vector_store():
     """Lazy initialization of VectorStore to avoid circular imports."""
     global vector_store
@@ -28,68 +22,79 @@ def get_vector_store():
         vector_store = VectorStore()
     return vector_store
 
-# Tab 1: Manage Documents
-if tab == "📁 Manage Documents":
-    st.title("📁 Manage Documents")
-    uploaded_file = st.file_uploader("Upload Document", type=["pdf", "docx", "txt"])
+def main():
+    """Main function to handle app navigation and functionality."""
+    # Sidebar navigation
+    tab = st.sidebar.radio(
+        "Navigation",
+        ["📁 Manage Documents", "📝 Generate Templates", "🔍 Search Documents"]
+    )
 
-    if uploaded_file:
-        try:
-            with st.spinner("Processing document..."):
-                # Process document and extract text/chunks
-                text, chunks = doc_processor.process_document(uploaded_file)
-                st.success("Document processed successfully!")
+    # Tab 1: Manage Documents
+    if tab == "📁 Manage Documents":
+        st.title("📁 Manage Documents")
+        uploaded_file = st.file_uploader("Upload Document", type=["pdf", "docx", "txt"])
 
-                # Add to vector store
-                vector_store_instance = get_vector_store()
-                vector_store_instance.add_texts(
-                    texts=[chunk["text"] for chunk in chunks],
-                    metadatas=[{
-                        "text": chunk["text"],
-                        "chunk_id": chunk["chunk_id"],
-                        "filename": uploaded_file.name
-                    } for chunk in chunks]
-                )
-                st.success("Document added to vector store!")
+        if uploaded_file:
+            try:
+                with st.spinner("Processing document..."):
+                    # Process document and extract text/chunks
+                    text, chunks = doc_processor.process_document(uploaded_file)
+                    st.success("Document processed successfully!")
 
-                # Display document preview
-                with st.expander("Document Preview", expanded=False):
-                    st.text_area(
-                        "Content",
-                        value=text[:1000] + "..." if len(text) > 1000 else text,
-                        height=300,
-                        disabled=True
+                    # Add to vector store
+                    vector_store_instance = get_vector_store()
+                    vector_store_instance.add_texts(
+                        texts=[chunk["text"] for chunk in chunks],
+                        metadatas=[{
+                            "text": chunk["text"],
+                            "chunk_id": chunk["chunk_id"],
+                            "filename": uploaded_file.name
+                        } for chunk in chunks]
                     )
+                    st.success("Document added to vector store!")
 
-        except Exception as e:
-            st.error(f"Error processing document: {e}")
+                    # Display document preview
+                    with st.expander("Document Preview", expanded=False):
+                        st.text_area(
+                            "Content",
+                            value=text[:1000] + "..." if len(text) > 1000 else text,
+                            height=300,
+                            disabled=True
+                        )
 
-    # List processed documents
-    st.subheader("Processed Documents")
-    vector_store_instance = get_vector_store()
-    processed_docs = vector_store_instance.metadata
-    if processed_docs:
-        for idx, doc in enumerate(processed_docs):
-            st.markdown(f"{idx+1}. **{doc.get('filename', 'Unknown')}** - Chunk ID: {doc['chunk_id']}")
-    else:
-        st.info("No documents uploaded yet.")
+            except Exception as e:
+                st.error(f"Error processing document: {e}")
 
-# Tab 2: Generate Templates
-elif tab == "📝 Generate Templates":
-    st.title("📝 Generate Templates")
-    render_template_generator()
+        # List processed documents
+        st.subheader("Processed Documents")
+        vector_store_instance = get_vector_store()
+        processed_docs = vector_store_instance.metadata
+        if processed_docs:
+            for idx, doc in enumerate(processed_docs):
+                st.markdown(f"{idx+1}. **{doc.get('filename', 'Unknown')}** - Chunk ID: {doc['chunk_id']}")
+        else:
+            st.info("No documents uploaded yet.")
+
+    # Tab 2: Generate Templates
+    elif tab == "📝 Generate Templates":
+        st.title("📝 Generate Templates")
+        render_template_generator()
+
+    # Tab 3: Search Documents
+    elif tab == "🔍 Search Documents":
+        st.title("🔍 Search Documents")
+        query = st.text_input("Enter your query:")
+        if query:
+            with st.spinner("Searching for relevant chunks..."):
+                vector_store_instance = get_vector_store()
+                results = vector_store_instance.similarity_search(query)
+                if results:
+                    st.success("Found relevant chunks:")
+                    for result in results:
+                        st.markdown(f"- **Chunk:** {result['text'][:200]}... (Relevance: {result['distance']:.2f})")
+                else:
+                    st.warning("No relevant chunks found.")
 
-# Tab 3: Search Documents
-elif tab == "🔍 Search Documents":
-    st.title("🔍 Search Documents")
-    query = st.text_input("Enter your query:")
-    if query:
-        with st.spinner("Searching for relevant chunks..."):
-            vector_store_instance = get_vector_store()
-            results = vector_store_instance.similarity_search(query)
-            if results:
-                st.success("Found relevant chunks:")
-                for result in results:
-                    st.markdown(f"- **Chunk:** {result['text'][:200]}... (Relevance: {result['distance']:.2f})")
-            else:
-                st.warning("No relevant chunks found.")
+if __name__ == "__main__":
+    main()