Create app.py

app.py

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+import streamlit as st
+from typing import TypedDict, Annotated
+from langgraph.graph import StateGraph, START, END
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.checkpoint.sqlite import SqliteSaver
+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
+
+
+st.title("Checkpoints-Breakpoints")
+#st.caption("Demonstrating a step-by-step graph workflow with breakpoints and human control.")
+
+# Environment variables
+openai_api_key = os.getenv("OPENAI_API_KEY")
+tavily_api_key = os.getenv("TAVILY_API_KEY")
+
+if openai_api_key and tavily_api_key:
+    os.environ["OPENAI_API_KEY"] = openai_api_key
+    os.environ["TAVILY_API_KEY"] = tavily_api_key
+
+    # Define the State
+    class State(TypedDict):
+        messages: Annotated[list, add_messages]
+
+    # Instantiate ChatOpenAI and Tools
+    llm = ChatOpenAI(model="gpt-4o-mini")
+    tool = TavilySearchResults(max_results=2)
+    tools = [tool]
+    llm_with_tools = llm.bind_tools(tools)
+
+    # Define the agent function
+    def Agent(state: State):
+        return {"messages": [llm_with_tools.invoke(state["messages"])]}
+
+    # Memory Checkpoints
+    memory = MemorySaver()
+
+    # Build the Graph
+    graph = StateGraph(State)
+    tool_node = ToolNode(tools=[tool])
+    graph.add_node("Agent", Agent)
+    graph.add_node("tools", tool_node)
+
+    graph.add_conditional_edges("Agent", tools_condition)
+    graph.add_edge("tools", "Agent")
+    graph.set_entry_point("Agent")
+
+    app = graph.compile(checkpointer=memory, interrupt_before=["tools"])
+
+    # Display the graph structure
+    st.subheader("Graph Workflow")
+    st.image(app.get_graph().draw_mermaid_png(), caption="Graph Visualization", use_column_width=True)
+
+    # Input Section
+    st.subheader("Run the Workflow")
+    user_input = st.text_input("Enter a message to start the graph:", "Hello, I am from Kolkata")
+    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 = []
+        for event in app.stream(input_message, thread, stream_mode="values"):
+            latest_message = event["messages"][-1].pretty_print()
+            outputs.append(latest_message)
+            st.code(latest_message)
+
+        # Display all intermediate steps
+        st.subheader("Intermediate Outputs")
+        for i, output in enumerate(outputs, 1):
+            st.write(f"**Step {i}:**")
+            st.code(output)
+
+        # Snapshot State
+        st.subheader("Current State Snapshot")
+        snapshot = app.get_state(thread)
+        current_message = snapshot.values["messages"][-1]
+        st.code(current_message.pretty_print())
+
+        # Manual State Update
+        st.subheader("Manual Update")
+        tool_call_id = current_message.tool_calls[0]["id"]
+        manual_response = st.text_area("Manual Tool Response", "Enter response to continue...")
+        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.error("API keys are missing! Please set `OPENAI_API_KEY` and `TAVILY_API_KEY` in Hugging Face Spaces Secrets.")