trying to add thought process

agent.py

@@ -90,6 +90,12 @@ def read_message(state: AgentState) -> AgentState:
     # Just pass the messages through to the next node
     return {"messages": messages}
 
+def restart_required(state: AgentState) -> AgentState:
+    messages = state["messages"]
+    print(f"Processing question: {messages[-1].content if messages else ''}")
+    # Just pass the messages through to the next node
+    return {"messages": messages}
+
 # def tool_message(state: AgentState) -> AgentState:
 #     messages = state["messages"]
 #     prompt = f"""
@@ -159,6 +165,50 @@ def answer_message(state: AgentState) -> AgentState:
     messages.append(final_response)
     return {"messages": messages}
 
+def thought_process(state: AgentState) -> AgentState:
+    messages = state["messages"]
+    prompt = [SystemMessage(f"""
+    You are a GAIA question answering assistant.
+    Your task is to list down the steps and chain of thoughts to answer the question provided carefully.
+    Think carefully before answering the question. 
+    The steps provided should simplify the thinking of the another person and help them to derive the answer.
+    Include steps that require the use of specific tools or state the limitation that you're facing.  
+    """)]
+    messages = prompt + messages
+
+    # First pass: let model decide whether to call web_search
+    ai_msg = model_with_tools.invoke(messages)
+    messages.append(ai_msg)
+
+    # If the model didn't request any tools, its content is already the answer
+    tool_calls = getattr(ai_msg, "tool_calls", None) or []
+    if not tool_calls:
+        print(f"Final response: {ai_msg}")
+        return {"messages": messages}
+
+    # Execute requested tools and append their text output into the conversation
+    for tool_call in tool_calls:
+        name = tool_call["name"]
+        args = tool_call["args"]
+        tool = tools_by_name[name]
+        tool_result = tool.invoke(args)  # this is a plain string from web_search
+        messages.append(HumanMessage(content=f"Tool result ({name}):\n{tool_result}"))
+
+    # Second pass: force a plain-text final answer (no tool calls expected)
+    final_instruction = HumanMessage(
+        content=(
+            "Using the tool results above, provide the list of steps answer now. "
+            "Do not call any tools. Respond with only the chain of thought."
+        )
+    )
+    messages.append(final_instruction)
+
+    final_response = model.invoke(messages)
+    print(f"Final response: {final_response}")
+
+    # Return messages including the final AIMessage so BasicAgent reads .content
+    messages.append(final_response)
+    return {"thoughts": messages}
 
 
 def build_graph():
@@ -167,10 +217,12 @@ def build_graph():
     # Add nodes
     agent_graph.add_node("read_message", read_message)
     agent_graph.add_node("answer_message", answer_message)
+    agent_graph.add_node("thought_process", thought_process)
 
     # Add edges
     agent_graph.add_edge(START, "read_message")
     agent_graph.add_edge("read_message", "answer_message")
+    agent_graph.add_conditional_edges("answer_message", restart_required, {True:"thought_process", False:END})
 
     # Final edge
     agent_graph.add_edge("answer_message", END)

requirements.txt

@@ -1,4 +1,4 @@
-gradio
+gradio >=4.44.0
 requests
 langchain
 langchain-community
@@ -9,7 +9,7 @@ langchain-groq
 langchain-tavily
 langchain-chroma
 langgraph
-huggingface_hub
+huggingface_hub >=0.26.0
 supabase
 arxiv
 pymupdf