app.py

import gradio as gr
from my_memory_logic import run_with_session_memory

def chat_interface_fn(message, history, session_id):
    """
    Multi-turn chat function for Gradio's ChatInterface.
    'session_id' is used to store conversation across turns.
    Deduplicates consecutive repeated Q&A pairs to avoid repetition.
    """
    # Initialize history if None
    if history is None:
        history = []
    
    # Ensure we're working with the correct history format
    # Gradio 3.x sends history as a list of tuples (user, assistant)
    if isinstance(history, list):
        if len(history) > 0 and isinstance(history[0], tuple):
            history = [
                {"role": "user" if i % 2 == 0 else "assistant", "content": msg}
                for tup in history
                for i, msg in enumerate([tup[0], tup[1]])
            ]
    
    # Get answer from the session-based memory pipeline
    try:
        answer = run_with_session_memory(message, session_id)
    except Exception as e:
        print(f"Error in run_with_session_memory: {str(e)}")
        answer = "I apologize, but I encountered an error processing your request."
    
    # Format for Gradio ChatInterface
    # Gradio expects a tuple of (new_chat_history, internal_history)
    new_history = history + [
        {"role": "user", "content": message},
        {"role": "assistant", "content": answer}
    ]
    
    # Convert history to format expected by Gradio
    chat_history = [(msg["content"], hist["content"]) 
                    for msg, hist in zip(new_history[::2], new_history[1::2])]
    
    return chat_history, new_history

# Custom CSS for chat interface
my_chat_css = """
.gradio-container {
    margin: auto;
}
.user .wrap {
    text-align: right !important;
}
.assistant .wrap {
    text-align: left !important;
}
"""

# Set up Gradio interface
with gr.Blocks(css=my_chat_css) as demo:
    gr.Markdown("### DailyWellnessAI (User on right, Assistant on left)")
    session_id_box = gr.Textbox(label="Session ID", value="abc123", interactive=True)
    
    chat_interface = gr.ChatInterface(
        fn=lambda msg, hist: chat_interface_fn(msg, hist, session_id_box.value),
        title="DailyWellnessAI (Session-based Memory)",
        description="Ask your questions. The session_id determines your stored memory."
    )

# Launch the Gradio interface with sharing enabled
demo.launch(share=True)