Update app.py

app.py

@@ -438,7 +438,17 @@ if st.session_state.get("app_started") and st.session_state.get("rag_pipeline_re
         with st.chat_message(chat_entry["role"]):
             st.markdown(chat_entry["content"])
             if "context" in chat_entry and chat_entry["context"]:
-                 with st.expander("Retrieved Context"): st.json(chat_entry["context"])
+                with st.expander("Retrieved Context"):
+                    try:
+                        # Attempt to parse as JSON only if it looks like a JSON string
+                        if isinstance(chat_entry["context"], str) and (chat_entry["context"].strip().startswith('{') or chat_entry["context"].strip().startswith('[')):
+                            st.json(json.loads(chat_entry["context"]))
+                        else:
+                            # Otherwise, display as plain text
+                            st.text(str(chat_entry["context"]))
+                    except (json.JSONDecodeError, TypeError):
+                        # Fallback for any other parsing errors
+                        st.text(str(chat_entry["context"]))
 
     user_query_manual = st.chat_input("Ask a question:")
     if user_query_manual:
@@ -461,10 +471,10 @@ if st.session_state.get("app_started") and st.session_state.get("rag_pipeline_re
             elif intent == "PRODUCT_INFO":
                 raw_context_data = get_product_info(user_query_manual, st.session_state.products_data)
                 context_for_llm = f"Product Information: {raw_context_data}"
-            elif intent == "GENERAL_POLICY_FAQ" or intent == "UNKNOWN": 
+            elif intent == "GENERAL_POLICY_FAQ" or intent == "UNKNOWN":
                 if st.session_state.faiss_index_pdfs and st.session_state.embedding_model:
                     k_val = 2 if intent == "GENERAL_POLICY_FAQ" else 1
-                    retrieved_chunks = search_faiss_index(st.session_state.faiss_index_pdfs, user_query_manual, 
+                    retrieved_chunks = search_faiss_index(st.session_state.faiss_index_pdfs, user_query_manual,
                                                           st.session_state.embedding_model, st.session_state.indexed_pdf_chunks, k=k_val)
                     if retrieved_chunks:
                         context_for_llm = "\n\n".join(retrieved_chunks)
@@ -475,17 +485,20 @@ if st.session_state.get("app_started") and st.session_state.get("rag_pipeline_re
                 else:
                     context_for_llm = "Policy/FAQ documents unavailable."
                     raw_context_data = {"message": "PDF index not ready."}
-            
+
             llm_response = generate_response_groq(st.session_state.groq_client, user_query_manual, context_for_llm)
             with st.chat_message("assistant"):
                 st.markdown(llm_response)
                 if raw_context_data:
                     with st.expander("Retrieved Context"):
+                        # This block is the one that needs the fix
                         try:
                             if isinstance(raw_context_data, str) and (raw_context_data.strip().startswith('{') or raw_context_data.strip().startswith('[')):
                                 st.json(json.loads(raw_context_data))
-                            else: st.json(raw_context_data) 
-                        except (json.JSONDecodeError, TypeError): st.text(str(raw_context_data))
+                            else:
+                                st.text(str(raw_context_data))
+                        except (json.JSONDecodeError, TypeError):
+                            st.text(str(raw_context_data))
             st.session_state.manual_chat_history.append({"role": "assistant", "content": llm_response, "context": raw_context_data})
 
 # --- Twilio Bot Polling Logic ---