single thread conversational in-memory chatbot

conversational_bot.py

@@ -0,0 +1,75 @@
+import gradio as gr
+from langchain_ollama import ChatOllama
+from langgraph.graph import StateGraph, START, END
+from langgraph.graph.message import MessagesState
+from langgraph.checkpoint.memory import MemorySaver
+from langchain_core.messages import (
+    convert_to_openai_messages,
+    SystemMessage,
+    HumanMessage,
+)
+
+def create_conversation_graph():
+    """
+    Create a conversational graph with a memory saver.
+    """
+    memory = MemorySaver()
+
+    llm = ChatOllama(model="gemma3:4b", temperature=0)
+
+    sys_msg = SystemMessage(content="You are a helpful assistant tasked with performing arithmetic on a set of inputs.")
+
+    def assistant(state: MessagesState) -> MessagesState:
+        return {"messages": [llm.invoke([sys_msg] + state["messages"])]}
+
+    builder = StateGraph(MessagesState)
+
+    builder.add_node("assistant", assistant)
+    builder.add_edge(START, "assistant")
+    builder.add_edge("assistant", END)
+
+    graph = builder.compile(checkpointer=memory)
+    return graph
+
+
+def create_chat_interface():
+    """
+    Create and configure the chat interface with the conversation graph.
+    """
+    graph = create_conversation_graph()
+
+    # Specify a thread id
+    thread_id = "123"
+    config = {"configurable": {"thread_id": thread_id}}
+
+    def chat_with_assistant(message, history):
+        """
+        Chat with the assistant using the conversational graph.
+        """
+        # Create a MessagesState with a HumanMessage
+        messages_state = MessagesState(messages=[HumanMessage(content=message)])
+
+        # Invoke the graph with the properly formatted input
+        response = graph.invoke(messages_state, config)
+
+        # Extract the last message from the response's messages list
+        ai_message = response["messages"][-1]
+
+        # Return just the content of the AI message
+        return convert_to_openai_messages(ai_message)
+
+
+    demo = gr.ChatInterface(
+        fn=chat_with_assistant,
+        type="messages",
+        title="Conversational Bot",
+        description="Ask anything you want",
+        examples=["Hello", "What is your name?", "What is the weather in Tokyo?"],
+    )
+    return demo
+
+if __name__ == "__main__":
+    demo = create_chat_interface()
+    demo.launch()
+
+