app.py

import os
import logging
import logging.config
from typing import Any
from uuid import uuid4, UUID
import json
import sys

import gradio as gr
from dotenv import load_dotenv
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage, ToolMessage
from langgraph.types import RunnableConfig
from pydantic import BaseModel
from pathlib import Path

load_dotenv()

# Check Gradio version
print(f"Gradio version: {gr.__version__}")

# There are tools set here dependent on environment variables
from graph import graph, weak_model, search_enabled # noqa

FOLLOWUP_QUESTION_NUMBER = 3
TRIM_MESSAGE_LENGTH = 16  # Includes tool messages
USER_INPUT_MAX_LENGTH = 10000  # Characters

# We need the same secret for data persistance
# If you store sensitive data, you should store your secret in .env
BROWSER_STORAGE_SECRET = "itsnosecret"

try:
    with open('logging-config.json', 'r') as fh:
        config = json.load(fh)
    logging.config.dictConfig(config)
except FileNotFoundError:
    # Fallback logging configuration
    logging.basicConfig(level=logging.INFO)
    
logger = logging.getLogger(__name__)

def load_initial_greeting(filepath="greeting_prompt.txt") -> str:
    """
    Loads the initial greeting message from a specified text file.
    """
    try:
        with open(filepath, "r", encoding="utf-8") as f:
            return f.read().strip()
    except FileNotFoundError:
        logger.warning(f"Warning: Prompt file '{filepath}' not found.")
        return "Welcome to DIYO! I'm here to help you create amazing DIY projects. What would you like to build today?"

async def chat_fn(user_input: str, history: dict, input_graph_state: dict, uuid: UUID, prompt: str, search_enabled: bool, download_website_text_enabled: bool):
    """
    Args:
        user_input (str): The user's input message
        history (dict): The history of the conversation in gradio
        input_graph_state (dict): The current state of the graph. This includes tool call history
        uuid (UUID): The unique identifier for the current conversation. This can be used in conjunction with langgraph or for memory
        prompt (str): The system prompt
    Yields:
        str: The output message
        dict|Any: The final state of the graph
        bool|Any: Whether to trigger follow up questions
    """
    try:
        logger.info(f"Processing user input: {user_input[:100]}...")
        
        # Initialize input_graph_state if None
        if input_graph_state is None:
            input_graph_state = {}
            
        input_graph_state["tools_enabled"] = {
            "download_website_text": download_website_text_enabled,
            "tavily_search_results_json": search_enabled,
        }
        if prompt:
            input_graph_state["prompt"] = prompt

        if input_graph_state.get("awaiting_human_input"):
            input_graph_state["messages"].append(
                ToolMessage(
                    tool_call_id=input_graph_state.pop("human_assistance_tool_id"),
                    content=user_input
                )
            )
            input_graph_state["awaiting_human_input"] = False
        else:
            # New user message
            if "messages" not in input_graph_state:
                input_graph_state["messages"] = []
            input_graph_state["messages"].append(
                HumanMessage(user_input[:USER_INPUT_MAX_LENGTH])
            )
            input_graph_state["messages"] = input_graph_state["messages"][-TRIM_MESSAGE_LENGTH:]

        config = RunnableConfig(
            recursion_limit=20,
            run_name="user_chat",
            configurable={"thread_id": str(uuid)}
        )

        output: str = ""
        final_state: dict | Any = {}
        waiting_output_seq: list[str] = []

        async for stream_mode, chunk in graph.astream(
                    input_graph_state,
                    config=config,
                    stream_mode=["values", "messages"],
                ):
            if stream_mode == "values":
                final_state = chunk
                if chunk.get("messages") and len(chunk["messages"]) > 0:
                    last_message = chunk["messages"][-1]
                    if hasattr(last_message, "tool_calls") and last_message.tool_calls:
                        for msg_tool_call in last_message.tool_calls:
                            tool_name: str = msg_tool_call['name']

                            if tool_name == "tavily_search_results_json":
                                query = msg_tool_call['args']['query']
                                waiting_output_seq.append(f"🔍 Searching for '{query}'...")
                                yield "\n".join(waiting_output_seq), gr.skip(), gr.skip()

                            elif tool_name == "download_website_text":
                                url = msg_tool_call['args']['url']
                                waiting_output_seq.append(f"📥 Downloading text from '{url}'...")
                                yield "\n".join(waiting_output_seq), gr.skip(), gr.skip()

                            elif tool_name == "human_assistance":
                                query = msg_tool_call["args"]["query"]
                                waiting_output_seq.append(f"🤖: {query}")

                                # Save state to resume after user provides input
                                final_state["awaiting_human_input"] = True
                                final_state["human_assistance_tool_id"] = msg_tool_call["id"]

                                # Indicate that human input is needed
                                yield "\n".join(waiting_output_seq), final_state, True
                                return  # Pause execution, resume in next call

                            else:
                                waiting_output_seq.append(f"🔧 Running {tool_name}...")
                                yield "\n".join(waiting_output_seq), gr.skip(), gr.skip()

            elif stream_mode == "messages":
                msg, metadata = chunk
                # Check for the correct node name from your graph
                node_name = metadata.get('langgraph_node', '')
                if node_name in ["brainstorming_node", "prompt_planning_node", "generate_3d_node", "assistant_node"]:
                    current_chunk_text = ""
                    if isinstance(msg.content, str):
                        current_chunk_text = msg.content
                    elif isinstance(msg.content, list):
                        for block in msg.content:
                            if isinstance(block, dict) and block.get("type") == "text":
                                current_chunk_text += block.get("text", "")
                            elif isinstance(block, str):
                                current_chunk_text += block

                    if current_chunk_text:
                        output += current_chunk_text
                        yield output, gr.skip(), gr.skip()

        # Final yield with complete response
        yield output + " ", dict(final_state), True
        
    except Exception as e:
        logger.exception("Exception occurred in chat_fn")
        user_error_message = "There was an error processing your request. Please try again."
        yield user_error_message, gr.skip(), False

def clear():
    """Clear the current conversation state"""
    return dict(), uuid4()

class FollowupQuestions(BaseModel):
    """Model for langchain to use for structured output for followup questions"""
    questions: list[str]

async def populate_followup_questions(end_of_chat_response: bool, messages: dict[str, str], uuid: UUID):
    """
    This function gets called a lot due to the asynchronous nature of streaming
    Only populate followup questions if streaming has completed and the message is coming from the assistant
    """
    if not end_of_chat_response or not messages or len(messages) == 0:
        return *[gr.skip() for _ in range(FOLLOWUP_QUESTION_NUMBER)], False
    
    # Check if the last message is from assistant
    if messages[-1]["role"] != "assistant":
        return *[gr.skip() for _ in range(FOLLOWUP_QUESTION_NUMBER)], False
        
    try:
        config = RunnableConfig(
            run_name="populate_followup_questions",
            configurable={"thread_id": str(uuid)}
        )
        weak_model_with_config = weak_model.with_config(config)
        follow_up_questions = await weak_model_with_config.with_structured_output(FollowupQuestions).ainvoke([
            ("system", f"suggest {FOLLOWUP_QUESTION_NUMBER} followup questions for the user to ask the assistant. Refrain from asking personal questions."),
            *messages,
        ])
        
        if len(follow_up_questions.questions) != FOLLOWUP_QUESTION_NUMBER:
            logger.warning("Invalid number of followup questions generated")
            return *[gr.Button(visible=False) for _ in range(FOLLOWUP_QUESTION_NUMBER)], False
            
        buttons = []
        for i in range(FOLLOWUP_QUESTION_NUMBER):
            buttons.append(
                gr.Button(follow_up_questions.questions[i], visible=True, elem_classes="chat-tab"),
            )
        return *buttons, False
        
    except Exception as e:
        logger.error(f"Error generating followup questions: {e}")
        return *[gr.Button(visible=False) for _ in range(FOLLOWUP_QUESTION_NUMBER)], False

async def summarize_chat(end_of_chat_response: bool, messages: dict, sidebar_summaries: dict, uuid: UUID):
    """Summarize chat for tab names"""
    should_return = (
        not end_of_chat_response or
        not messages or
        len(messages) == 0 or
        messages[-1]["role"] != "assistant" or
        isinstance(sidebar_summaries, type(lambda x: x)) or
        uuid in sidebar_summaries
    )
    if should_return:
        return gr.skip(), gr.skip()

    # Filter valid messages
    filtered_messages = []
    for msg in messages:
        if isinstance(msg, dict) and msg.get("content") and msg["content"].strip():
            filtered_messages.append(msg)
    
    # If we don't have any valid messages after filtering, provide a default summary
    if not filtered_messages:
        if uuid not in sidebar_summaries:
            sidebar_summaries[uuid] = "New Chat"
        return sidebar_summaries, False

    try:
        config = RunnableConfig(
            run_name="summarize_chat",
            configurable={"thread_id": str(uuid)}
        )
        weak_model_with_config = weak_model.with_config(config)
        summary_response = await weak_model_with_config.ainvoke([
            ("system", "summarize this chat in 7 tokens or less. Refrain from using periods"),
            *filtered_messages,
        ])
        
        if uuid not in sidebar_summaries:
            sidebar_summaries[uuid] = summary_response.content[:50]  # Limit length
            
    except Exception as e:
        logger.error(f"Error summarizing chat: {e}")
        if uuid not in sidebar_summaries:
            sidebar_summaries[uuid] = "Chat Session"
    
    return sidebar_summaries, False

async def new_tab(uuid, gradio_graph, messages, tabs, prompt, sidebar_summaries):
    """Create a new chat tab"""
    new_uuid = uuid4()
    new_graph = {}
    
    # Save current tab if it has content
    if messages and len(messages) > 0:
        if uuid not in sidebar_summaries:
            sidebar_summaries, _ = await summarize_chat(True, messages, sidebar_summaries, uuid)
        tabs[uuid] = {
            "graph": gradio_graph,
            "messages": messages,
            "prompt": prompt,
        }
    
    # Clear suggestion buttons
    suggestion_buttons = [gr.Button(visible=False) for _ in range(FOLLOWUP_QUESTION_NUMBER)]
    
    # Load initial greeting for new chat
    greeting_text = load_initial_greeting()
    new_chat_messages_for_display = [{"role": "assistant", "content": greeting_text}]
    
    new_prompt = prompt if prompt else "You are a helpful DIY assistant."
    
    return new_uuid, new_graph, new_chat_messages_for_display, tabs, new_prompt, sidebar_summaries, *suggestion_buttons

def switch_tab(selected_uuid, tabs, gradio_graph, uuid, messages, prompt):
    """Switch to a different chat tab"""
    try:
        # Save current state if there are messages
        if messages and len(messages) > 0:
            tabs[uuid] = {
                "graph": gradio_graph if gradio_graph else {},
                "messages": messages,
                "prompt": prompt
            }

        if selected_uuid not in tabs:
            logger.error(f"Could not find the selected tab in tabs storage: {selected_uuid}")
            return gr.skip(), gr.skip(), gr.skip(), gr.skip(), gr.skip(), *[gr.Button(visible=False) for _ in range(FOLLOWUP_QUESTION_NUMBER)]
            
        selected_tab_state = tabs[selected_uuid]
        selected_graph = selected_tab_state.get("graph", {})
        selected_messages = selected_tab_state.get("messages", [])
        selected_prompt = selected_tab_state.get("prompt", "You are a helpful DIY assistant.")
        
        suggestion_buttons = [gr.Button(visible=False) for _ in range(FOLLOWUP_QUESTION_NUMBER)]
        
        return selected_graph, selected_uuid, selected_messages, tabs, selected_prompt, *suggestion_buttons
        
    except Exception as e:
        logger.error(f"Error switching tabs: {e}")
        return gr.skip(), gr.skip(), gr.skip(), gr.skip(), gr.skip(), *[gr.Button(visible=False) for _ in range(FOLLOWUP_QUESTION_NUMBER)]

def delete_tab(current_chat_uuid, selected_uuid, sidebar_summaries, tabs):
    """Delete a chat tab"""
    output_messages = gr.skip()
    
    # If deleting the current tab, clear the chatbot
    if current_chat_uuid == selected_uuid:
        output_messages = []
        
    # Remove from storage
    if selected_uuid in tabs:
        del tabs[selected_uuid]
    if selected_uuid in sidebar_summaries:
        del sidebar_summaries[selected_uuid]
        
    return sidebar_summaries, tabs, output_messages

def submit_edit_tab(selected_uuid, sidebar_summaries, text):
    """Submit edited tab name"""
    if text.strip():
        sidebar_summaries[selected_uuid] = text.strip()[:50]  # Limit length
    return sidebar_summaries, ""

def load_mesh(mesh_file_name):
    """Load a 3D mesh file"""
    return mesh_file_name

def get_sorted_3d_model_examples():
    """Get sorted list of 3D model examples"""
    examples_dir = Path("./generated_3d_models")
    
    # Create directory if it doesn't exist
    examples_dir.mkdir(exist_ok=True)
    
    if not examples_dir.exists():
        return []

    # Get all 3D model files with desired extensions
    model_files = [
        file for file in examples_dir.glob("*")
        if file.suffix.lower() in {".obj", ".glb", ".gltf"}
    ]

    # Sort files by creation time (latest first)
    try:
        sorted_files = sorted(
            model_files,
            key=lambda x: x.stat().st_ctime,
            reverse=True
        )
    except (OSError, AttributeError):
        # Fallback to name sorting if stat fails
        sorted_files = sorted(model_files, key=lambda x: x.name, reverse=True)

    # Convert to format [[path1], [path2], ...]
    return [[str(file)] for file in sorted_files]

CSS = """
footer {visibility: hidden}
.followup-question-button {font-size: 12px }
.chat-tab {
    font-size: 12px;
    padding-inline: 0;
}
.chat-tab.active {
    background-color: #654343;
}
#new-chat-button { background-color: #0f0f11; color: white; }

.tab-button-control {
    min-width: 0;
    padding-left: 0;
    padding-right: 0;
}

.sidebar-collapsed {
    display: none !important;
}

.wrap.sidebar-parent {
    min-height: 2400px !important;
    height: 2400px !important;
}

#main-app {
    height: 4600px;
    overflow-y: auto;
    padding-top: 20px;
}
"""

TRIGGER_CHATINTERFACE_BUTTON = """
function triggerChatButtonClick() {
  const chatTextbox = document.getElementById("chat-textbox");
  if (!chatTextbox) {
    console.error("Error: Could not find element with id 'chat-textbox'");
    return;
  }
  const button = chatTextbox.querySelector("button");
  if (!button) {
    console.error("Error: No button found inside the chat-textbox element");
    return;
  }
  button.click();
}"""

if __name__ == "__main__":
    logger.info("Starting the DIYO interface")
    
    # Check if BrowserState is available
    has_browser_state = hasattr(gr, 'BrowserState')
    logger.info(f"BrowserState available: {has_browser_state}")
    
    if not has_browser_state:
        logger.warning("BrowserState not available in this Gradio version. Using regular State instead.")
        logger.warning("To use BrowserState, upgrade Gradio: pip install gradio>=4.0.0")
    
    with gr.Blocks(title="DIYO - DIY Assistant", fill_height=True, css=CSS, elem_id="main-app") as demo:
        # State management - Use BrowserState if available, otherwise regular State
        is_new_user_for_greeting = gr.State(True)
        
        if has_browser_state:
            current_prompt_state = gr.BrowserState(
                value="You are a helpful DIY assistant.",
                storage_key="current_prompt_state",
                secret=BROWSER_STORAGE_SECRET,
            )
            current_uuid_state = gr.BrowserState(
                value=uuid4,
                storage_key="current_uuid_state",
                secret=BROWSER_STORAGE_SECRET,
            )
            current_langgraph_state = gr.BrowserState(
                value=dict,
                storage_key="current_langgraph_state",
                secret=BROWSER_STORAGE_SECRET,
            )
            sidebar_names_state = gr.BrowserState(
                value=dict,
                storage_key="sidebar_names_state",
                secret=BROWSER_STORAGE_SECRET,
            )
            offloaded_tabs_data_storage = gr.BrowserState(
                value=dict,
                storage_key="offloaded_tabs_data_storage",
                secret=BROWSER_STORAGE_SECRET,
            )
            chatbot_message_storage = gr.BrowserState(
                value=list,
                storage_key="chatbot_message_storage",
                secret=BROWSER_STORAGE_SECRET,
            )
        else:
            # Fallback to regular State
            current_prompt_state = gr.State("You are a helpful DIY assistant.")
            current_uuid_state = gr.State(uuid4())
            current_langgraph_state = gr.State({})
            sidebar_names_state = gr.State({})
            offloaded_tabs_data_storage = gr.State({})
            chatbot_message_storage = gr.State([])
        
        end_of_assistant_response_state = gr.State(False)
        
        # Header
        with gr.Row(elem_classes="header-margin"):
            gr.Markdown("""
            <div style="display: flex; align-items: center; justify-content: center; text-align: center; padding: 20px; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); border-radius: 15px; margin-bottom: 20px; color: white; box-shadow: 0 4px 15px rgba(0,0,0,0.2);">
                <h1>🔧 DIYO - Your DIY Assistant 🛠️</h1>
            </div>
            """)

        # System prompt input
        with gr.Row():
            prompt_textbox = gr.Textbox(
                label="System Prompt",
                value="You are a helpful DIY assistant.",
                show_label=True,
                interactive=True,
                placeholder="Enter custom system prompt..."
            )
        
        # Tool settings
        with gr.Row():
            checkbox_search_enabled = gr.Checkbox(
                value=True,
                label="Enable web search",
                show_label=True,
                visible=search_enabled,
                scale=1,
            )
            checkbox_download_website_text = gr.Checkbox(
                value=True,
                show_label=True,
                label="Enable downloading text from URLs",
                scale=1,
            )
            
        # 3D Model display and controls
        with gr.Row():
            with gr.Column(scale=2):
                model_3d_output = gr.Model3D(
                    clear_color=[0.0, 0.0, 0.0, 0.0],
                    label="3D Model Viewer",
                    height=400
                )
            with gr.Column(scale=1):
                model_3d_upload_button = gr.UploadButton(
                    "📁 Upload 3D Model (.obj, .glb, .gltf)",
                    file_types=[".obj", ".glb", ".gltf"],
                )
                model_3d_upload_button.upload(
                    fn=load_mesh,
                    inputs=model_3d_upload_button,
                    outputs=model_3d_output
                )
                
                # Examples with error handling
                try:
                    examples_list = get_sorted_3d_model_examples()
                    if examples_list:
                        gr.Examples(
                            label="Example 3D Models",
                            examples=examples_list,
                            inputs=model_3d_upload_button,
                            outputs=model_3d_output,
                            fn=load_mesh,
                            cache_examples=False
                        )
                except Exception as e:
                    logger.error(f"Error setting up 3D model examples: {e}")
        
        # Chat interface setup
        with gr.Row():
            multimodal = False
            textbox_component = gr.MultimodalTextbox if multimodal else gr.Textbox
            
            textbox = textbox_component(
                show_label=False,
                label="Message",
                placeholder="Type a message...",
                scale=1,
                autofocus=True,
                submit_btn=True,
                stop_btn=True,
                elem_id="chat-textbox",
                lines=1,
            )
            chatbot = gr.Chatbot(
                type="messages",
                scale=0,
                show_copy_button=True,
                height=400,
                editable="all",
                elem_classes="main-chatbox"
            )
            
        # Follow-up question buttons
        with gr.Row():
            followup_question_buttons = []
            for i in range(FOLLOWUP_QUESTION_NUMBER):
                btn = gr.Button(f"Button {i+1}", visible=False, elem_classes="followup-question-button")
                followup_question_buttons.append(btn)

        # Tab management state
        tab_edit_uuid_state = gr.State("")
        
        # Update prompt state when changed
        prompt_textbox.change(
            fn=lambda prompt: prompt, 
            inputs=[prompt_textbox], 
            outputs=[current_prompt_state]
        )
        
        # Sidebar with chat history
        with gr.Sidebar() as sidebar:
            @gr.render(inputs=[tab_edit_uuid_state, end_of_assistant_response_state, sidebar_names_state, current_uuid_state, chatbot, offloaded_tabs_data_storage])
            def render_chats(tab_uuid_edit, end_of_chat_response, sidebar_summaries, active_uuid, messages, tabs):
                # Ensure sidebar_summaries is a dict
                if not isinstance(sidebar_summaries, dict):
                    sidebar_summaries = {}
                    
                # Current tab button
                current_tab_button_text = sidebar_summaries.get(active_uuid, "Current Chat")
                if active_uuid not in tabs or not tabs[active_uuid]:
                    unique_id = f"current-tab-{active_uuid}-{uuid4()}"
                    gr.Button(
                        current_tab_button_text, 
                        elem_classes=["chat-tab", "active"], 
                        elem_id=unique_id
                    )
                
                # Historical tabs
                for chat_uuid, tab in reversed(tabs.items()):
                    if not tab:  # Skip empty tabs
                        continue
                        
                    elem_classes = ["chat-tab"]
                    if chat_uuid == active_uuid:
                        elem_classes.append("active")
                        
                    button_uuid_state = gr.State(chat_uuid)
                    
                    with gr.Row():
                        # Delete button
                        clear_tab_button = gr.Button(
                            "🗑",
                            scale=0,
                            elem_classes=["tab-button-control"],
                            elem_id=f"delete-btn-{chat_uuid}-{uuid4()}"
                        )
                        clear_tab_button.click(
                            fn=delete_tab,
                            inputs=[
                                current_uuid_state,
                                button_uuid_state,
                                sidebar_names_state,
                                offloaded_tabs_data_storage
                            ],
                            outputs=[
                                sidebar_names_state,
                                offloaded_tabs_data_storage,
                                chatbot
                            ]
                        )
                        
                        # Tab name/edit functionality
                        chat_button_text = sidebar_summaries.get(chat_uuid, str(chat_uuid)[:8])
                        
                        if chat_uuid != tab_uuid_edit:
                            # Edit button
                            set_edit_tab_button = gr.Button(
                                "✎",
                                scale=0,
                                elem_classes=["tab-button-control"],
                                elem_id=f"edit-btn-{chat_uuid}-{uuid4()}"
                            )
                            set_edit_tab_button.click(
                                fn=lambda x: x,
                                inputs=[button_uuid_state],
                                outputs=[tab_edit_uuid_state]
                            )
                            
                            # Tab button
                            chat_tab_button = gr.Button(
                                chat_button_text,
                                elem_id=f"chat-{chat_uuid}-{uuid4()}",
                                elem_classes=elem_classes,
                                scale=2
                            )
                            chat_tab_button.click(
                                fn=switch_tab,
                                inputs=[
                                    button_uuid_state,
                                    offloaded_tabs_data_storage,
                                    current_langgraph_state,
                                    current_uuid_state,
                                    chatbot,
                                    prompt_textbox
                                ],
                                outputs=[
                                    current_langgraph_state,
                                    current_uuid_state,
                                    chatbot,
                                    offloaded_tabs_data_storage,
                                    prompt_textbox,
                                    *followup_question_buttons
                                ]
                            )
                        else:
                            # Edit textbox
                            chat_tab_text = gr.Textbox(
                                chat_button_text,
                                scale=2,
                                interactive=True,
                                show_label=False,
                                elem_id=f"edit-text-{chat_uuid}-{uuid4()}"
                            )
                            chat_tab_text.submit(
                                fn=submit_edit_tab,
                                inputs=[
                                    button_uuid_state,
                                    sidebar_names_state,
                                    chat_tab_text
                                ],
                                outputs=[
                                    sidebar_names_state,
                                    tab_edit_uuid_state
                                ]
                            )
            
            # New chat button
            new_chat_button = gr.Button("➕ New Chat", elem_id="new-chat-button")
        
        # Clear functionality
        chatbot.clear(
            fn=clear, 
            outputs=[current_langgraph_state, current_uuid_state]
        )
        
        # Main chat interface
        chat_interface = gr.ChatInterface(
            chatbot=chatbot,
            fn=chat_fn,
            additional_inputs=[
                current_langgraph_state,
                current_uuid_state,
                prompt_textbox,
                checkbox_search_enabled,
                checkbox_download_website_text,
            ],
            additional_outputs=[
                current_langgraph_state,
                end_of_assistant_response_state
            ],
            type="messages",
            multimodal=multimodal,
            textbox=textbox,
        )

        # New chat button functionality
        new_chat_button.click(
            new_tab,
            inputs=[
                current_uuid_state,
                current_langgraph_state,
                chatbot,
                offloaded_tabs_data_storage,
                prompt_textbox,
                sidebar_names_state,
            ],
            outputs=[
                current_uuid_state,
                current_langgraph_state,
                chatbot,
                offloaded_tabs_data_storage,
                prompt_textbox,
                sidebar_names_state,
                *followup_question_buttons,
            ]
        )

        # Follow-up button functionality
        def click_followup_button(btn):
            buttons = [gr.Button(visible=False) for _ in range(len(followup_question_buttons))]
            return btn, *buttons

        for btn in followup_question_buttons:
            btn.click(
                fn=click_followup_button,
                inputs=[btn],
                outputs=[
                    chat_interface.textbox,
                    *followup_question_buttons
                ]
            ).success(lambda: None, js=TRIGGER_CHATINTERFACE_BUTTON)

        # Event handlers for chatbot changes
        chatbot.change(
            fn=populate_followup_questions,
            inputs=[
                end_of_assistant_response_state,
                chatbot,
                current_uuid_state
            ],
            outputs=[
                *followup_question_buttons,
                end_of_assistant_response_state
            ],
            trigger_mode="multiple"
        )
        
        chatbot.change(
            fn=summarize_chat,
            inputs=[
                end_of_assistant_response_state,
                chatbot,
                sidebar_names_state,
                current_uuid_state
            ],
            outputs=[
                sidebar_names_state,
                end_of_assistant_response_state
            ],
            trigger_mode="multiple"
        )
        
        chatbot.change(
            fn=lambda x: x,
            inputs=[chatbot],
            outputs=[chatbot_message_storage],
            trigger_mode="always_last"
        )

        # Load event handlers - only add these if we have BrowserState
        if has_browser_state:
            @demo.load(
                inputs=[is_new_user_for_greeting, chatbot_message_storage],
                outputs=[chatbot_message_storage, is_new_user_for_greeting]
            )
            def handle_initial_greeting_load(current_is_new_user_flag: bool, existing_chat_history: list):
                """Handle initial greeting when the app loads"""
                if current_is_new_user_flag:
                    greeting_message_text = load_initial_greeting()
                    greeting_entry = {"role": "assistant", "content": greeting_message_text}
                    
                    if not isinstance(existing_chat_history, list):
                        existing_chat_history = []
                        
                    updated_chat_history = [greeting_entry] + existing_chat_history
                    updated_is_new_user_flag = False
                    logger.info("Greeting added for new user.")
                    return updated_chat_history, updated_is_new_user_flag
                else:
                    logger.info("Not a new user or already greeted.")
                    if not isinstance(existing_chat_history, list):
                        existing_chat_history = []
                    return existing_chat_history, False

            @demo.load(inputs=[chatbot_message_storage], outputs=[chatbot])
            def load_messages(messages):
                """Load stored messages into chatbot"""
                if isinstance(messages, list):
                    return messages
                return []

            @demo.load(inputs=[current_prompt_state], outputs=[prompt_textbox])
            def load_prompt(current_prompt):
                """Load stored prompt"""
                if current_prompt:
                    return current_prompt
                return "You are a helpful DIY assistant."
        else:
            # For regular State, add a simple greeting on load
            @demo.load(outputs=[chatbot])
            def load_initial_greeting():
                """Load initial greeting for users without BrowserState"""
                greeting_text = load_initial_greeting()
                return [{"role": "assistant", "content": greeting_text}]

    # Launch the application
    demo.launch(debug=True, share=True)