Update app.py

app.py

@@ -20,6 +20,7 @@ from langchain.document_loaders import TextLoader
 from langchain.memory import ConversationBufferMemory
 from langchain.agents import initialize_agent, Tool
 from langchain.agents.agent_types import AgentType
+from langchain.schema import messages_from_dict, messages_to_dict
 
 class AbbyyVantage:
     """
@@ -248,8 +249,7 @@ def setup_agent(file):
     agent_executor = initialize_agent(
         tools=tools,
         llm=ChatOpenAI(model="gpt-3.5-turbo"),
-        agent=AgentType.CONVERSATIONAL_REACT_DESCRIPTION,
-        #agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
+        agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,
         memory=memory,
         verbose=True
     )
@@ -285,9 +285,6 @@ def ask_question(message, history):
         "**💡 Advisor’s Practical Tip:**\n"
         "- Give actionable tips to help the user get the most from their policy.\n\n"
     
-        "**⚠️ Caveats and Exclusions:**\n"
-        "- Mention any exclusions, limitations, or waiting periods.\n\n"
-    
         "Your tone should be empathetic and clear—like a smart, helpful insurance advisor.\n"
         "Format everything using **Markdown**. Always include all four sections in the output.\n\n"
     
@@ -298,8 +295,6 @@ def ask_question(message, history):
         "**🧠 Simplified Explanation:**\n"
         "- ...\n\n"
         "**💡 Advisor’s Practical Tip:**\n"
-        "- ...\n\n"
-        "**⚠️ Caveats and Exclusions:**\n"
         "- ...\n"
         "---"
         "---\n"
@@ -307,7 +302,6 @@ def ask_question(message, history):
         "**📄 Policy Details:**\n- The policy offers 24-month waiting period for pre-existing conditions.\n\n"
         "**🧠 Simplified Explanation:**\n- You have to wait 2 years before claims related to past illnesses are covered.\n\n"
         "**💡 Advisor’s Practical Tip:**\n- Consider a top-up or rider that waives this waiting period.\n\n"
-        "**⚠️ Caveats and Exclusions:**\n- Diabetes and hypertension are included in pre-existing illnesses list.\n\n"
     )
 
 
@@ -315,8 +309,20 @@ def ask_question(message, history):
     prompt = f"{advisory_prompt}\nQuestion: {message}"
 
     try:
-        response = agent_executor.run(prompt)
-        return response
+        result = agent_executor.invoke({"input": prompt})
+        final_output = result.get("output", "No output.")
+        steps = result.get("intermediate_steps", [])
+
+        steps_md = "\n\n<details><summary>🧠 Agent Thought Process</summary>\n\n"
+        for i, (action, observation) in enumerate(steps, 1):
+            steps_md += f"**Step {i}**\n\n"
+            steps_md += f"- **Thought**: {action.log.strip()}\n"
+            steps_md += f"- **Tool Used**: `{action.tool}` with input `{action.tool_input}`\n"
+            steps_md += f"- **Observation**: {observation.strip()}\n\n"
+        steps_md += "</details>"
+
+        return final_output + "\n\n" + steps_md
+
     except Exception as e:
         return f"❌ Error: {str(e)}"