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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
import subprocess
# def update_repo():
# try:
# subprocess.run(["git", "fetch", "origin"], check=True)
# subprocess.run(["git", "reset", "--hard", "origin/main"], check=True)
# subprocess.run([sys.executable, "-m", "pip", "install", "-r", "requirements.txt"], check=True)
# subprocess.run([sys.executable, "app.py"], check=True)
# except Exception as e:
# print(f"Git update failed: {e}")
# update_repo()
load_dotenv()
# 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"
with open('logging-config.json', 'r') as fh:
config = json.load(fh)
logging.config.dictConfig(config)
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:
# Use a logger if you have one configured, otherwise print
# logger.warning(f"Warning: Prompt file '{filepath}' not found.")
print(f"Warning: Prompt file '{filepath}' not found. Using default.")
return "Welcome to the application! (Default Greeting)"
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
We do not use gradio history in the graph since we want the ToolMessage in the history
ordered properly. GraphProcessingState.messages is used as history instead
"""
try:
logger.info(f"Prompt: {prompt}")
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": 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
last_message = chunk["messages"][-1]
if hasattr(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()
# download_website_text is the name of the function defined in graph.py
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
input_graph_state["awaiting_human_input"] = True
input_graph_state["human_assistance_tool_id"] = msg_tool_call["id"]
# Indicate that human input is needed
yield "\n".join(waiting_output_seq), input_graph_state, gr.skip(), 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
# print("output: ", msg, metadata)
# assistant_node is the name we defined in the langgraph graph
if metadata.get('langgraph_node') == "assistant_node": # Use .get for safety
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): # Fallback if content is list of strings
current_chunk_text += block
if current_chunk_text: # Only add and yield if there's actually text
output += current_chunk_text
yield output, gr.skip(), gr.skip()
# Trigger for asking follow up questions
# + store the graph state for next iteration
# yield output, dict(final_state), gr.skip()
yield output + " ", dict(final_state), True
except Exception:
logger.exception("Exception occurred")
user_error_message = "There was an error processing your request. Please try again."
yield user_error_message, gr.skip(), False
def clear():
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 messages[-1]["role"] != "assistant":
return *[gr.skip() for _ in range(FOLLOWUP_QUESTION_NUMBER)], False
config = RunnableConfig(
run_name="populate_followup_questions",
configurable={"thread_id": 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:
raise ValueError("Invalid value of followup questions")
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
async def summarize_chat(end_of_chat_response: bool, messages: dict, sidebar_summaries: dict, uuid: UUID):
"""Summarize chat for tab names"""
# print("\n------------------------")
# print("not end_of_chat_response", not end_of_chat_response)
# print("not messages", not messages)
# if messages:
# print("messages[-1][role] != assistant", messages[-1]["role"] != "assistant")
# print("isinstance(sidebar_summaries, type(lambda x: x))", isinstance(sidebar_summaries, type(lambda x: x)))
# print("uuid in sidebar_summaries", uuid in sidebar_summaries)
should_return = (
not end_of_chat_response or
not messages or
messages[-1]["role"] != "assistant" or
# This is a bug with gradio
isinstance(sidebar_summaries, type(lambda x: x)) or
# Already created summary
uuid in sidebar_summaries
)
if should_return:
return gr.skip(), gr.skip()
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] = "Chat History"
return sidebar_summaries, False
config = RunnableConfig(
run_name="summarize_chat",
configurable={"thread_id": uuid}
)
try:
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
except Exception as e:
logger.error(f"Error summarizing chat: {e}")
# Provide a fallback summary if an error occurs
if uuid not in sidebar_summaries:
sidebar_summaries[uuid] = "Previous Chat"
return sidebar_summaries, False
async def new_tab(uuid, gradio_graph, messages, tabs, prompt, sidebar_summaries):
new_uuid = uuid4()
new_graph = {}
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,
}
suggestion_buttons = []
for _ in range(FOLLOWUP_QUESTION_NUMBER):
suggestion_buttons.append(gr.Button(visible=False))
new_messages = {}
# --- MODIFICATION FOR GREETING IN EVERY NEW CHAT ---
greeting_text = load_initial_greeting() # Get the greeting
# `gr.Chatbot` expects a list of tuples or list of dicts.
# For `type="messages"`, it's list of dicts: [{"role": "assistant", "content": "Hello"}]
# Or list of tuples: [(None, "Hello")]
# Let's assume your chatbot is configured for list of tuples (None, bot_message) for initial messages
new_chat_messages_for_display = [{"role": "assistant", "content": greeting_text}]
# If your chat_interface.chatbot_value expects list of dicts:
# new_messages_history = [{"role": "assistant", "content": greeting_text}]
# --- END MODIFICATION ---
new_prompt = "You are a helpful 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):
# I don't know of another way to lookup uuid other than
# by the button value
# Save current state
if messages:
tabs[uuid] = {
"graph": gradio_graph,
"messages": messages,
"prompt": prompt
}
if selected_uuid not in tabs:
logger.error(f"Could not find the selected tab in offloaded_tabs_data_storage {selected_uuid}")
return gr.skip(), gr.skip(), gr.skip(), gr.skip()
selected_tab_state = tabs[selected_uuid]
selected_graph = selected_tab_state["graph"]
selected_messages = selected_tab_state["messages"]
selected_prompt = selected_tab_state.get("prompt", "")
suggestion_buttons = []
for _ in range(FOLLOWUP_QUESTION_NUMBER):
suggestion_buttons.append(gr.Button(visible=False))
return selected_graph, selected_uuid, selected_messages, tabs, selected_prompt, *suggestion_buttons
def delete_tab(current_chat_uuid, selected_uuid, sidebar_summaries, tabs):
output_messages = gr.skip()
if current_chat_uuid == selected_uuid:
output_messages = dict()
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):
sidebar_summaries[selected_uuid] = text
return sidebar_summaries, ""
def load_mesh(mesh_file_name):
return mesh_file_name
def display_initial_greeting(is_new_user_state_value: bool):
"""
Determines if a greeting should be displayed and returns the UI updates.
It also returns the new state for 'is_new_user_for_greeting'.
"""
if is_new_user_state_value:
greeting_message_text = load_initial_greeting()
# For a chatbot, the history is a list of tuples: [(user_msg, bot_msg)]
# For an initial message from the bot, user_msg is None.
initial_chat_history = [(None, greeting_message_text)]
updated_is_new_user_flag = False # Greeting shown, so set to False
return initial_chat_history, updated_is_new_user_flag
else:
# Not a new user (or already greeted), so no initial message in chat history
# and the flag remains False.
return [], False
def get_sorted_3d_model_examples():
examples_dir = Path("./generated_3d_models")
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)
sorted_files = sorted(
model_files,
key=lambda x: x.stat().st_ctime,
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; /* or 800px, or 100% */
overflow-y: auto; /* optional if you want it scrollable */\
padding-top:2000px;
}
"""
# We set the ChatInterface textbox id to chat-textbox for this to work
TRIGGER_CHATINTERFACE_BUTTON = """
function triggerChatButtonClick() {
// Find the div with id "chat-textbox"
const chatTextbox = document.getElementById("chat-textbox");
if (!chatTextbox) {
console.error("Error: Could not find element with id 'chat-textbox'");
return;
}
// Find the button that is a descendant of the div
const button = chatTextbox.querySelector("button");
if (!button) {
console.error("Error: No button found inside the chat-textbox element");
return;
}
// Trigger the click event
button.click();
}"""
TOGGLE_SIDEBAR_JS = """
function toggleSidebarVisibility() {
console.log("Called the side bar funnction");
const sidebar = document.querySelector(".sidebar svelte-7y53u7 open");
if (!sidebar) {
console.error("Error: Could not find the sidebar element");
return;
}
sidebar.classList.toggle("sidebar-collapsed");
}
"""
if __name__ == "__main__":
logger.info("Starting the interface")
with gr.Blocks(title="DIYO is here", fill_height=True, css=CSS, elem_id="main-app") as demo:
is_new_user_for_greeting = gr.State(True)
chatbot_message_storage = gr.State([])
current_prompt_state = gr.BrowserState(
storage_key="current_prompt_state",
secret=BROWSER_STORAGE_SECRET,
)
current_uuid_state = gr.BrowserState(
uuid4,
storage_key="current_uuid_state",
secret=BROWSER_STORAGE_SECRET,
)
current_langgraph_state = gr.BrowserState(
dict(),
storage_key="current_langgraph_state",
secret=BROWSER_STORAGE_SECRET,
)
end_of_assistant_response_state = gr.State(
bool(),
)
# [uuid] -> summary of chat
sidebar_names_state = gr.BrowserState(
dict(),
storage_key="sidebar_names_state",
secret=BROWSER_STORAGE_SECRET,
)
# [uuid] -> {"graph": gradio_graph, "messages": messages}
offloaded_tabs_data_storage = gr.BrowserState(
dict(),
storage_key="offloaded_tabs_data_storage",
secret=BROWSER_STORAGE_SECRET,
)
chatbot_message_storage = gr.BrowserState(
[],
storage_key="chatbot_message_storage",
secret=BROWSER_STORAGE_SECRET,
)
with gr.Row(elem_classes="header-margin"):
# Add the decorated header with ASCII art
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);">
╔══════════════════════════════════════════════════════════════════════════════════════════════╗
║ ║
║ █████╗ ██████╗ ███████╗███╗ ██╗████████╗ ██████╗ ██╗██╗ ██╗ ██████╗ ║
║ ██╔══██╗██╔════╝ ██╔════╝████╗ ██║╚══██╔══╝ ██╔══██╗██║╚██╗ ██╔╝██╔═══██╗ ║
║ ███████║██║ ███╗█████╗ ██╔██╗ ██║ ██║ ██║ ██║██║ ╚████╔╝ ██║ ██║ ║
║ ██╔══██║██║ ██║██╔══╝ ██║╚██╗██║ ██║ ██║ ██║██║ ╚██╔╝ ██║ ██║ ║
║ ██║ ██║╚██████╔╝███████╗██║ ╚████║ ██║ ██████╔╝██║ ██║ ╚██████╔╝ ║
║ ╚═╝ ╚═╝ ╚═════╝ ╚══════╝╚═╝ ╚═══╝ ╚═╝ ╚═════╝ ╚═╝ ╚═╝ ╚═════╝ ║
║ ║
╚══════════════════════════════════════════════════════════════════════════════════════════════╝
Let's build things, break boundaries with the help of AI!
</div>
""")
with gr.Row():
prompt_textbox = gr.Textbox(show_label=False, interactive=True)
with gr.Row():
checkbox_search_enabled = gr.Checkbox(
value=True,
label="Enable 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,
)
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",
height=400 # Adjust height to align better with chatbot
)
with gr.Column(scale=1):
# Input for the 3D model
# Using UploadButton is often clearer for users than a clickable Model3D input
model_3d_upload_button = gr.UploadButton(
"Upload 3D Model (.obj, .glb, .gltf)",
file_types=[".obj", ".glb", ".gltf"],
# scale=0 # make it take less space if needed
)
model_3d_upload_button.upload(
fn=load_mesh,
inputs=model_3d_upload_button,
outputs=model_3d_output
)
gr.Examples(
label="Example 3D Models",
examples=get_sorted_3d_model_examples(),
inputs=model_3d_upload_button, # Dummy input for examples to load into Model3D
outputs=model_3d_output,
fn=load_mesh,
cache_examples=True # Caching might be useful
)
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"
)
with gr.Row():
followup_question_buttons = []
for i in range(FOLLOWUP_QUESTION_NUMBER):
btn = gr.Button(f"Button {i+1}", visible=False)
followup_question_buttons.append(btn)
tab_edit_uuid_state = gr.State(
str()
)
prompt_textbox.change(lambda prompt: prompt, inputs=[prompt_textbox], outputs=[current_prompt_state])
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):
current_tab_button_text = ""
if active_uuid not in sidebar_summaries:
current_tab_button_text = "Current Chat"
elif active_uuid not in tabs:
current_tab_button_text = sidebar_summaries[active_uuid]
if current_tab_button_text:
unique_id = f"current-tab-{active_uuid}-{uuid4()}"
gr.Button(
current_tab_button_text,
elem_classes=["chat-tab", "active"],
elem_id=unique_id # Add unique elem_id
)
for chat_uuid, tab in reversed(tabs.items()):
elem_classes = ["chat-tab"]
if chat_uuid == active_uuid:
elem_classes.append("active")
button_uuid_state = gr.State(chat_uuid)
with gr.Row():
clear_tab_button = gr.Button(
"🗑",
scale=0,
elem_classes=["tab-button-control"],
elem_id=f"delete-btn-{chat_uuid}-{uuid4()}" # Add unique ID
)
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,
chat_interface.chatbot_value
]
)
chat_button_text = sidebar_summaries.get(chat_uuid)
if not chat_button_text:
chat_button_text = str(chat_uuid)
if chat_uuid != tab_uuid_edit:
set_edit_tab_button = gr.Button(
"✎",
scale=0,
elem_classes=["tab-button-control"],
elem_id=f"edit-btn-{chat_uuid}-{uuid4()}" # Add unique ID
)
set_edit_tab_button.click(
fn=lambda x: x,
inputs=[button_uuid_state],
outputs=[tab_edit_uuid_state]
)
chat_tab_button = gr.Button(
chat_button_text,
elem_id=f"chat-{chat_uuid}-{uuid4()}", # Add truly unique ID
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,
chat_interface.chatbot_value,
offloaded_tabs_data_storage,
prompt_textbox,
*followup_question_buttons
]
)
else:
chat_tab_text = gr.Textbox(
chat_button_text,
scale=2,
interactive=True,
show_label=False,
elem_id=f"edit-text-{chat_uuid}-{uuid4()}" # Add unique ID
)
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
]
)
# )
# return chat_tabs, sidebar_summaries
new_chat_button = gr.Button("New Chat", elem_id="new-chat-button")
chatbot.clear(fn=clear, outputs=[current_langgraph_state, current_uuid_state])
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.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,
chat_interface.chatbot_value,
offloaded_tabs_data_storage,
prompt_textbox,
sidebar_names_state,
*followup_question_buttons,
]
)
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)
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"
)
@demo.load( # Or demo.load
inputs=[
is_new_user_for_greeting,
chatbot_message_storage # Pass the current stored messages
],
outputs=[
chatbot_message_storage, # Update the stored messages with the greeting
is_new_user_for_greeting # Update the flag
]
)
def handle_initial_greeting_load(current_is_new_user_flag: bool, existing_chat_history: list):
"""
This function is called by the @app.load decorator above.
It decides whether to add a greeting to the chat history.
"""
# You can either put the logic directly here, or call the globally defined one.
# Option 1: Call the globally defined function (cleaner if it's complex)
# Make sure 'display_initial_greeting_on_load' is defined globally in your app.py
# For this example, I'm assuming 'display_initial_greeting_on_load' is the one we defined earlier:
# def display_initial_greeting_on_load(current_is_new_user_flag: bool, existing_chat_history: list):
# if current_is_new_user_flag:
# greeting_message_text = load_initial_greeting() # from graph.py
# greeting_entry = (None, 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, no greeting added.")
# return existing_chat_history, False
#
# return display_initial_greeting_on_load(current_is_new_user_flag, existing_chat_history)
# Option 2: Put logic directly here (if simple enough)
if current_is_new_user_flag:
greeting_message_text = load_initial_greeting() # Make sure load_initial_greeting is imported
greeting_entry = {"role": "assistant", "content": greeting_message_text}
# Ensure existing_chat_history is a list before concatenation
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 via handle_initial_greeting_load.")
return updated_chat_history, updated_is_new_user_flag
else:
logger.info("Not a new user or already greeted (handle_initial_greeting_load path).")
return existing_chat_history, False
@demo.load(inputs=[chatbot_message_storage], outputs=[chat_interface.chatbot_value])
def load_messages(messages):
return messages
@demo.load(inputs=[current_prompt_state], outputs=[prompt_textbox])
def load_prompt(current_prompt):
return current_prompt
# demo.launch(server_name="127.0.0.1", server_port=8080, share=True)
demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
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