import os
from typing import TypedDict, Annotated
from dotenv import load_dotenv
from langgraph.graph.message import add_messages
from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
from langgraph.prebuilt import ToolNode
from langgraph.graph import START, StateGraph
from langgraph.prebuilt import tools_condition
from langchain_openai import AzureChatOpenAI
from retriever import guest_info_tool
from tools import weather_info_tool, hub_stats_tool, news_search_tool

load_dotenv()


chat = AzureChatOpenAI(
    azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
    api_key=os.getenv("AZURE_OPENAI_API_KEY"),
    deployment_name=os.getenv("DEPLOYMENT_NAME"),
    openai_api_version=os.getenv("OPENAI_API_VERSION"),
    temperature=0.75,
    streaming=True,
    verbose=True
)

tools = [guest_info_tool, weather_info_tool, hub_stats_tool, news_search_tool]
chat_with_tools = chat.bind_tools(tools)


class AgentState(TypedDict):
    messages: Annotated[list[AnyMessage], add_messages]


def assistant(state: AgentState):
    return {
        "messages": [chat_with_tools.invoke(state["messages"])],
    }


# The graph
builder = StateGraph(AgentState)

# Define nodes: these do the work
builder.add_node("assistant", assistant)
builder.add_node("tools", ToolNode(tools))

# Define edges: these determine how the control flow moves
builder.add_edge(START, "assistant")
builder.add_conditional_edges(
    "assistant",
    # If the latest message requires a tool, route to tools
    # Otherwise, provide a direct response
    tools_condition,
)
builder.add_edge("tools", "assistant")

# Compile with debug mode for verbosity
alfred = builder.compile(debug=True)

messages = [HumanMessage(
    content="One of our guests is from Qwen. What can you tell me about their most recent popular AI model(search about it )?")]

print("🔍 Starting Agent Execution...")
print("="*50)

# Use stream instead of invoke to see step-by-step execution
final_messages = None
for step in alfred.stream({"messages": messages}):
    print(f"📍 Current Step: {list(step.keys())}")
    for node_name, node_output in step.items():
        print(f"🔧 Node '{node_name}' output:")
        if 'messages' in node_output:
            latest_message = node_output['messages'][-1]
            # Keep track of final messages
            final_messages = node_output['messages']
            print(f"   Type: {type(latest_message).__name__}")
            if hasattr(latest_message, 'content'):
                print(f"   Content: {latest_message.content[:200]}...")
            if hasattr(latest_message, 'tool_calls') and latest_message.tool_calls:
                print(f"   Tool Calls: {latest_message.tool_calls}")
        print("-" * 30)


print("\n"*3)
print("="*50)
print("🎩 Alfred's Final Response:")
if final_messages:
    print(final_messages[-1].content)