app.py

# app.py (or your main Gradio script)
import gradio as gr
from backend import chatbot
from langchain_core.messages import HumanMessage, BaseMessage, AIMessage

def respond_stream(message, history):
    # Convert history to LangChain format
    messages = []
    for human, ai in history:
        messages.append(HumanMessage(content=human))
        messages.append(ai)
    
    # Add the current message
    messages.append(HumanMessage(content=message))
    
    # Stream the response
    config = {"configurable": {"thread_id": "1"}}
    full_response = ""
    
    # Stream mode 'messages' returns tuples of (message, metadata)
    for chunk, metadata in chatbot.stream(
        {"messages": messages}, 
        config=config, 
        stream_mode="messages"
    ):
        # Extract content from the chunk
        if hasattr(chunk, 'content'):
            full_response += chunk.content
            yield full_response
        elif isinstance(chunk, dict) and 'content' in chunk:
            full_response += chunk['content']
            yield full_response


def main():
    # Create the Gradio ChatInterface using the streaming function
    demo = gr.ChatInterface(
        fn=respond_stream,  # Use the streaming function
        # Note: The 'type' parameter for ChatInterface history format is usually handled internally
        # based on the function signature or defaults. Specifying it might be unnecessary or outdated.
        title="Streaming ChatBot",
        description="Chatbot using langgraph backend with streaming output.",
        examples=[["Hello"], ["How are you?"], ["Tell me a joke."]] # Add examples
    )
    # Launch with share=True to get a public link if needed
    demo.launch(share=True) 

if __name__ == "__main__":
    main()