import streamlit as st
from typing import TypedDict, Annotated
from langgraph.graph import StateGraph
from langgraph.checkpoint.memory import MemorySaver
from langgraph.graph.message import add_messages
from langchain_openai import ChatOpenAI
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.messages import HumanMessage, ToolMessage, AIMessage
from langgraph.prebuilt import tools_condition
import os

# Streamlit UI Header
st.title("Checkpoints and Breakpoints")
st.caption("Demonstrating LangGraph workflow execution with interruptions and tool invocation.")

# Fetch API Keys
openai_api_key = os.getenv("OPENAI_API_KEY")
tavily_api_key = os.getenv("TAVILY_API_KEY")

if not openai_api_key or not tavily_api_key:
    st.error("API keys are missing! Set OPENAI_API_KEY and TAVILY_API_KEY in Hugging Face Spaces Secrets.")
    st.stop()

os.environ["OPENAI_API_KEY"] = openai_api_key
os.environ["TAVILY_API_KEY"] = tavily_api_key

# Define State Class
class State(TypedDict):
    messages: Annotated[list, add_messages]

# Initialize LLM and Tools
llm = ChatOpenAI(model="gpt-4o-mini")
tool = TavilySearchResults(max_results=2)
llm_with_tools = llm.bind_tools([tool])

# Agent Node
def Agent(state: State):
    st.sidebar.write("Agent Input State:", state["messages"])
    response = llm_with_tools.invoke(state["messages"])
    st.sidebar.write("Agent Response:", response)
    return {"messages": [response]}

# Tools Execution Node
def ExecuteTools(state: State):
    tool_calls = state["messages"][-1].tool_calls
    responses = []

    if tool_calls:
        for call in tool_calls:
            tool_name = call["name"]
            args = call["args"]
            st.sidebar.write("Tool Call Detected:", tool_name, args)

            if tool_name == "tavily_search_results_json":
                tool_response = tool.invoke({"query": args["query"]})
                st.sidebar.write("Tool Response:", tool_response)
                responses.append(ToolMessage(content=str(tool_response), tool_call_id=call["id"]))
    return {"messages": responses}

# Memory Checkpoint
memory = MemorySaver()

# Build the Graph
graph = StateGraph(State)
graph.add_node("Agent", Agent)
graph.add_node("ExecuteTools", ExecuteTools)

# Add Conditional Edge to Check for Tools
def custom_tools_condition(state: State):
    return "True" if state["messages"][-1].tool_calls else "False"

graph.add_conditional_edges("Agent", custom_tools_condition, {"True": "ExecuteTools", "False": "Agent"})
graph.add_edge("ExecuteTools", "Agent")
graph.set_entry_point("Agent")

# Compile the Graph
app = graph.compile(checkpointer=memory, interrupt_before=["ExecuteTools"])

# Display Graph Visualization
st.subheader("Graph Visualization")
st.image(app.get_graph().draw_mermaid_png(), caption="Workflow Graph", use_container_width=True)

# Run the Workflow
st.subheader("Run the Workflow")
user_input = st.text_input("Enter a message to start the graph:", "Search for the weather in Uttar Pradesh")
thread_id = st.text_input("Thread ID", "1")

if st.button("Execute Workflow"):
    thread = {"configurable": {"thread_id": thread_id}}
    input_message = {"messages": [HumanMessage(content=user_input)]}

    st.write("### Execution Outputs")
    outputs = []

    try:
        # Stream the graph execution
        for event in app.stream(input_message, thread, stream_mode="values"):
            output_message = event["messages"][-1]
            st.code(output_message.content)
            outputs.append(output_message.content)
            st.sidebar.write("Intermediate State:", event["messages"])

        # Display Intermediate Outputs
        if outputs:
            st.subheader("Intermediate Outputs")
            for idx, output in enumerate(outputs, start=1):
                st.write(f"**Step {idx}:**")
                st.code(output)
        else:
            st.warning("No outputs generated. Check the workflow or tool calls.")

        # Snapshot of Current State
        st.subheader("Current State Snapshot")
        snapshot = app.get_state(thread)
        current_message = snapshot.values["messages"][-1]
        st.code(current_message.pretty_print())

        # Manual Update for Interrupted State
        if hasattr(current_message, "tool_calls") and current_message.tool_calls:
            tool_call_id = current_message.tool_calls[0]["id"]
            manual_response = st.text_area("Manual Tool Response", "Enter response to continue execution...")
            if st.button("Update State"):
                new_messages = [
                    ToolMessage(content=manual_response, tool_call_id=tool_call_id),
                    AIMessage(content=manual_response),
                ]
                app.update_state(thread, {"messages": new_messages})
                st.success("State updated successfully!")
                st.code(app.get_state(thread).values["messages"][-1].pretty_print())
        else:
            st.warning("No tool calls detected to update the state.")
    except Exception as e:
        st.error(f"Error during execution: {e}")