app.py

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 ToolNode, 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)
tools = [tool]
llm_with_tools = llm.bind_tools(tools)

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

# Set up Graph
memory = MemorySaver()
graph = StateGraph(State)

# Add nodes
graph.add_node("Agent", Agent)
tool_node = ToolNode(tools=[tool])
graph.add_node("tools", tool_node)

# Add edges
graph.add_conditional_edges("Agent", tools_condition)
graph.add_edge("tools", "Agent")
graph.set_entry_point("Agent")

# Compile with Breakpoint
app = graph.compile(checkpointer=memory, interrupt_before=["tools"])

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

# Input Section
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 = []

    # Execute the workflow
    try:
        for event in app.stream(input_message, thread, stream_mode="values"):
            st.code(event["messages"][-1].content)
            outputs.append(event["messages"][-1].content)

        # 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 yet.")

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

        # Handle Tool Calls with Manual Input
        if hasattr(current_message, "tool_calls") and current_message.tool_calls:
            tool_call_id = current_message.tool_calls[0]["id"]
            st.warning("Execution paused before tool execution. Provide manual input to resume.")
            manual_response = st.text_area("Manual Tool Response", "Enter the tool's response here...")
            if st.button("Resume Execution"):
                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! Rerun the workflow to continue.")
                st.code(app.get_state(thread).values["messages"][-1].pretty_print())
        else:
            st.info("No tool calls detected at this step.")
    except Exception as e:
        st.error(f"Error during execution: {e}")