# Multi-agent sysadmin assistant using LangChain + LangGraph Supervisor
# Requires: `pip install langchain-openai langgraph langgraph-supervisor`

from __future__ import annotations

from supervisor import create_sysadmin_supervisor
from utils import print_step_info, explain_supervisor_pattern

if __name__ == "__main__":
    # Create the supervisor
    supervisor = create_sysadmin_supervisor()

    # Interactive conversation loop
    messages = []
    print("Welcome to the multi-agent sysadmin assistant!")
    print("Type your sysadmin question below. Type 'exit' to quit.")
    while True:
        user_input = input("\n📝 User: ")
        if user_input.strip().lower() == 'exit':
            print("Goodbye!")
            break
        messages.append({"role": "user", "content": user_input})
        query = {"messages": messages}

        print("\n=== Using invoke() method ===")
        result = supervisor.invoke(query)

        print("\n📊 FINAL RESULT:")
        print("-" * 40)
        print(result["messages"][-1].content)
        print("-" * 40)
        print(f"\n📈 Total messages exchanged: {len(result['messages'])}")

        # Add the assistant's reply to the conversation history
        messages.append({"role": "assistant", "content": result["messages"][-1].content})

        # Ask if the user wants to continue
        cont = input("\nWould you like to continue the conversation? (y/n): ")
        if cont.strip().lower() not in ('y', 'yes'):
            print("Session ended.")
            break

        print("\n=== Using stream() method for detailed step-by-step analysis ===")
        step_count = 0
        max_steps = 20  # Prevent infinite loops

        try:
            chunks_processed = []
            for chunk in supervisor.stream(query):
                step_count += 1
                chunks_processed.append(chunk)
                print_step_info(step_count, chunk)

                # Safety check to prevent infinite loops
                if step_count >= max_steps:
                    print(f"\n⚠️ Reached maximum steps ({max_steps}), stopping stream...")
                    break

            print(f"\n✅ Streaming completed successfully with {step_count} steps")
            print(f"📊 Total chunks processed: {len(chunks_processed)}")

            # Check if the last chunk contains a complete final response
            if chunks_processed:
                last_chunk = chunks_processed[-1]
                print(f"🔍 Last chunk keys: {list(last_chunk.keys()) if isinstance(last_chunk, dict) else type(last_chunk)}")

        except Exception as e:
            print(f"\n❌ Streaming error after {step_count} steps: {e}")
            print("💡 The invoke() method worked fine, so the supervisor itself is functional.")
            import traceback
            traceback.print_exc()