Update app.py

app.py

@@ -113,6 +113,27 @@ for domain, system in rag_systems.items():
     status = "✅ Ready" if system else "❌ Failed (DB missing?)" 
     logger.info(f"   {domain}: {status}")
 
+def hydrate_history(raw_history_list: list) -> list:
+    """Converts a list of dicts from session back into LangChain Message objects."""
+    history = []
+    if not raw_history_list:
+        return history
+    for item in raw_history_list:
+        if item.get('type') == 'human':
+            history.append(HumanMessage(content=item.get('content', '')))
+        elif item.get('type') == 'ai':
+            history.append(AIMessage(content=item.get('content', '')))
+    return history
+
+def dehydrate_history(history_messages: list) -> list:
+    """Converts LangChain Message objects into a JSON-serializable list of dicts."""
+    raw_list = []
+    for msg in history_messages:
+        if isinstance(msg, HumanMessage):
+            raw_list.append({'type': 'human', 'content': msg.content})
+        elif isinstance(msg, AIMessage):
+            raw_list.append({'type': 'ai', 'content': msg.content})
+    return raw_list
 
 # --- FLASK ROUTES ---
 
@@ -149,9 +170,12 @@ def medical_page():
                                validation=validation,
                                source=source)
     
-    # POST Request Logic
+    # POST Request
     answer, thoughts, validation, source = "", "", "", ""
-    history = session.get('medical_history', [])
+    # 1. Get raw history (list of dicts) from session
+    raw_history_list = session.get('medical_history', [])
+    # 2. Hydrate it for the agent
+    history_for_agent = hydrate_history(raw_history_list)
     current_medical_document = session.get('current_medical_document', "")
     
     
@@ -180,11 +204,11 @@ def medical_page():
             swarm_answer = run_medical_swarm(current_medical_document, query)
             answer = markdown_bold_to_html(swarm_answer)
                 
-            history.append(HumanMessage(content=f"[Document Uploaded] Query: '{query}'"))
-            history.append(AIMessage(content=swarm_answer))
             thoughts = "Swarm analysis complete. The process is orchestrated and does not use the ReAct thought process. You can now ask follow-up questions."
             source= "Medical Swarm"
             validation = (True, "Swarm output generated.") # Swarm has its own validation logic
+            history_for_agent.append(HumanMessage(content=f"[Document Uploaded] Query: '{query}'"))
+            history_for_agent.append(AIMessage(content=answer))
             
         elif has_image :
             #Scenario 1 
@@ -224,8 +248,8 @@ def medical_page():
                 if not agent: raise Exception("Medical RAG system is not loaded.")
                 response_dict = agent.answer(enhanced_query, chat_history=history)
                 answer, thoughts, validation, source = parse_agent_response(response_dict)
-                history.append(HumanMessage(content=query))
-                history.append(AIMessage(content=answer))
+                history_for_agent.append(HumanMessage(content=query))
+                history_for_agent.append(AIMessage(content=answer))
             
             finally:
                 if os.path.exists(image_path):
@@ -255,8 +279,8 @@ def medical_page():
             response_dict = agent.answer(standalone_query, chat_history=history_for_agent)
             answer, thoughts, validation, source = parse_agent_response(response_dict)
 
-            history.append(HumanMessage(content=query))
-            history.append(AIMessage(content=answer))
+            history_for_agent.append(HumanMessage(content=query))
+            history_for_agent.append(AIMessage(content=answer))
 
         else:
             raise ValueError("No query or file provided.")
@@ -265,8 +289,9 @@ def medical_page():
         answer = f"An error occurred: {e}"
         thoughts = traceback.format_exc()
     
-    # Save updated history and LATEST RESPONSE DATA back to the session
-    session['medical_history'] = history
+    # 4. DEHYDRATE the full history back into dicts for session saving
+    session['medical_history'] = dehydrate_history(history_for_agent)
+    # This (latest_response) is ALREADY JSON-serializable, so it's fine.
     session['latest_medical_response'] = {
         'answer': answer, 
         'thoughts': thoughts,