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# Chat_ui.py
# Description: Chat interface functions for Gradio
#
# Imports
import html
import json
import logging
import os
import sqlite3
from datetime import datetime
from pathlib import Path
#
# External Imports
import gradio as gr
#
# Local Imports
from App_Function_Libraries.Chat import chat, save_chat_history, update_chat_content, save_chat_history_to_db_wrapper
from App_Function_Libraries.DB.DB_Manager import add_chat_message, search_chat_conversations, create_chat_conversation, \
get_chat_messages, update_chat_message, delete_chat_message, load_preset_prompts, db
from App_Function_Libraries.Gradio_UI.Gradio_Shared import update_dropdown, update_user_prompt
#
#
########################################################################################################################
#
# Functions:
def show_edit_message(selected):
if selected:
return gr.update(value=selected[0], visible=True), gr.update(value=selected[1], visible=True), gr.update(
visible=True)
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)
def show_delete_message(selected):
if selected:
return gr.update(value=selected[1], visible=True), gr.update(visible=True)
return gr.update(visible=False), gr.update(visible=False)
def debug_output(media_content, selected_parts):
print(f"Debug - Media Content: {media_content}")
print(f"Debug - Selected Parts: {selected_parts}")
return ""
def update_selected_parts(use_content, use_summary, use_prompt):
selected_parts = []
if use_content:
selected_parts.append("content")
if use_summary:
selected_parts.append("summary")
if use_prompt:
selected_parts.append("prompt")
print(f"Debug - Update Selected Parts: {selected_parts}")
return selected_parts
# Old update_user_prompt shim for backwards compatibility
def get_system_prompt(preset_name):
# For backwards compatibility
prompts = update_user_prompt(preset_name)
return prompts["system_prompt"]
def clear_chat():
# Return empty list for chatbot and None for conversation_id
return gr.update(value=[]), None
# FIXME - add additional features....
def chat_wrapper(message, history, media_content, selected_parts, api_endpoint, api_key, custom_prompt, conversation_id,
save_conversation, temperature, system_prompt, max_tokens=None, top_p=None, frequency_penalty=None,
presence_penalty=None, stop_sequence=None):
try:
if save_conversation:
if conversation_id is None:
# Create a new conversation
media_id = media_content.get('id', None)
conversation_name = f"Chat about {media_content.get('title', 'Unknown Media')} - {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}"
conversation_id = create_chat_conversation(media_id, conversation_name)
# Add user message to the database
user_message_id = add_chat_message(conversation_id, "user", message)
# Include the selected parts and custom_prompt only for the first message
if not history and selected_parts:
message_body = "\n".join(selected_parts)
full_message = f"{custom_prompt}\n\n{message}\n\n{message_body}"
elif custom_prompt:
full_message = f"{custom_prompt}\n\n{message}"
else:
full_message = message
# Generate bot response
bot_message = chat(full_message, history, media_content, selected_parts, api_endpoint, api_key, custom_prompt,
temperature, system_prompt)
if save_conversation:
# Add assistant message to the database
add_chat_message(conversation_id, "assistant", bot_message)
# Update history
history.append((message, bot_message))
return bot_message, history, conversation_id
except Exception as e:
logging.error(f"Error in chat wrapper: {str(e)}")
return "An error occurred.", history, conversation_id
def search_conversations(query):
try:
conversations = search_chat_conversations(query)
if not conversations:
print(f"Debug - Search Conversations - No results found for query: {query}")
return gr.update(choices=[])
conversation_options = [
(f"{c['conversation_name']} (Media: {c['media_title']}, ID: {c['id']})", c['id'])
for c in conversations
]
print(f"Debug - Search Conversations - Options: {conversation_options}")
return gr.update(choices=conversation_options)
except Exception as e:
print(f"Debug - Search Conversations - Error: {str(e)}")
return gr.update(choices=[])
def load_conversation(conversation_id):
if not conversation_id:
return [], None
messages = get_chat_messages(conversation_id)
history = [
(msg['message'], None) if msg['sender'] == 'user' else (None, msg['message'])
for msg in messages
]
return history, conversation_id
def update_message_in_chat(message_id, new_text, history):
update_chat_message(message_id, new_text)
updated_history = [(msg1, msg2) if msg1[1] != message_id and msg2[1] != message_id
else ((new_text, msg1[1]) if msg1[1] == message_id else (new_text, msg2[1]))
for msg1, msg2 in history]
return updated_history
def delete_message_from_chat(message_id, history):
delete_chat_message(message_id)
updated_history = [(msg1, msg2) for msg1, msg2 in history if msg1[1] != message_id and msg2[1] != message_id]
return updated_history
def create_chat_interface():
custom_css = """
.chatbot-container .message-wrap .message {
font-size: 14px !important;
}
"""
with gr.TabItem("Remote LLM Chat (Horizontal)"):
gr.Markdown("# Chat with a designated LLM Endpoint, using your selected item as starting context")
chat_history = gr.State([])
media_content = gr.State({})
selected_parts = gr.State([])
conversation_id = gr.State(None)
with gr.Row():
with gr.Column(scale=1):
search_query_input = gr.Textbox(label="Search Query", placeholder="Enter your search query here...")
search_type_input = gr.Radio(choices=["Title", "URL", "Keyword", "Content"], value="Title",
label="Search By")
search_button = gr.Button("Search")
items_output = gr.Dropdown(label="Select Item", choices=[], interactive=True)
item_mapping = gr.State({})
with gr.Row():
use_content = gr.Checkbox(label="Use Content")
use_summary = gr.Checkbox(label="Use Summary")
use_prompt = gr.Checkbox(label="Use Prompt")
save_conversation = gr.Checkbox(label="Save Conversation", value=False, visible=True)
with gr.Row():
temperature = gr.Slider(label="Temperature", minimum=0.00, maximum=1.0, step=0.05, value=0.7)
with gr.Row():
conversation_search = gr.Textbox(label="Search Conversations")
with gr.Row():
search_conversations_btn = gr.Button("Search Conversations")
with gr.Row():
previous_conversations = gr.Dropdown(label="Select Conversation", choices=[], interactive=True)
with gr.Row():
load_conversations_btn = gr.Button("Load Selected Conversation")
api_endpoint = gr.Dropdown(label="Select API Endpoint",
choices=["Local-LLM", "OpenAI", "Anthropic", "Cohere", "Groq", "DeepSeek",
"Mistral", "OpenRouter",
"Llama.cpp", "Kobold", "Ooba", "Tabbyapi", "VLLM", "ollama",
"HuggingFace"])
api_key = gr.Textbox(label="API Key (if required)", type="password")
custom_prompt_checkbox = gr.Checkbox(label="Use a Custom Prompt",
value=False,
visible=True)
preset_prompt_checkbox = gr.Checkbox(label="Use a pre-set Prompt",
value=False,
visible=True)
preset_prompt = gr.Dropdown(label="Select Preset Prompt",
choices=load_preset_prompts(),
visible=False)
user_prompt = gr.Textbox(label="Custom Prompt",
placeholder="Enter custom prompt here",
lines=3,
visible=False)
system_prompt_input = gr.Textbox(label="System Prompt",
value="You are a helpful AI assitant",
lines=3,
visible=False)
with gr.Column():
chatbot = gr.Chatbot(height=600, elem_classes="chatbot-container")
msg = gr.Textbox(label="Enter your message")
submit = gr.Button("Submit")
clear_chat_button = gr.Button("Clear Chat")
edit_message_id = gr.Number(label="Message ID to Edit", visible=False)
edit_message_text = gr.Textbox(label="Edit Message", visible=False)
update_message_button = gr.Button("Update Message", visible=False)
delete_message_id = gr.Number(label="Message ID to Delete", visible=False)
delete_message_button = gr.Button("Delete Message", visible=False)
save_chat_history_to_db = gr.Button("Save Chat History to DataBase")
save_chat_history_as_file = gr.Button("Save Chat History as File")
download_file = gr.File(label="Download Chat History")
save_status = gr.Textbox(label="Save Status", interactive=False)
# Restore original functionality
search_button.click(
fn=update_dropdown,
inputs=[search_query_input, search_type_input],
outputs=[items_output, item_mapping]
)
def save_chat_wrapper(history, conversation_id, media_content):
file_path = save_chat_history(history, conversation_id, media_content)
if file_path:
return file_path, f"Chat history saved successfully as {os.path.basename(file_path)}!"
else:
return None, "Error saving chat history. Please check the logs and try again."
save_chat_history_as_file.click(
save_chat_wrapper,
inputs=[chatbot, conversation_id, media_content],
outputs=[download_file, save_status]
)
def update_prompts(preset_name):
prompts = update_user_prompt(preset_name)
return (
gr.update(value=prompts["user_prompt"], visible=True),
gr.update(value=prompts["system_prompt"], visible=True)
)
def clear_chat():
return [], None # Return empty list for chatbot and None for conversation_id
clear_chat_button.click(
clear_chat,
outputs=[chatbot, conversation_id]
)
preset_prompt.change(
update_prompts,
inputs=preset_prompt,
outputs=[user_prompt, system_prompt_input]
)
custom_prompt_checkbox.change(
fn=lambda x: (gr.update(visible=x), gr.update(visible=x)),
inputs=[custom_prompt_checkbox],
outputs=[user_prompt, system_prompt_input]
)
preset_prompt_checkbox.change(
fn=lambda x: gr.update(visible=x),
inputs=[preset_prompt_checkbox],
outputs=[preset_prompt]
)
submit.click(
chat_wrapper,
inputs=[msg, chatbot, media_content, selected_parts, api_endpoint, api_key, user_prompt,
conversation_id, save_conversation, temperature, system_prompt_input],
outputs=[msg, chatbot, conversation_id]
).then( # Clear the message box after submission
lambda x: gr.update(value=""),
inputs=[chatbot],
outputs=[msg]
).then( # Clear the user prompt after the first message
lambda: (gr.update(value=""), gr.update(value="")),
outputs=[user_prompt, system_prompt_input]
)
items_output.change(
update_chat_content,
inputs=[items_output, use_content, use_summary, use_prompt, item_mapping],
outputs=[media_content, selected_parts]
)
use_content.change(update_selected_parts, inputs=[use_content, use_summary, use_prompt],
outputs=[selected_parts])
use_summary.change(update_selected_parts, inputs=[use_content, use_summary, use_prompt],
outputs=[selected_parts])
use_prompt.change(update_selected_parts, inputs=[use_content, use_summary, use_prompt],
outputs=[selected_parts])
items_output.change(debug_output, inputs=[media_content, selected_parts], outputs=[])
search_conversations_btn.click(
search_conversations,
inputs=[conversation_search],
outputs=[previous_conversations]
)
load_conversations_btn.click(
clear_chat,
outputs=[chatbot, chat_history]
).then(
load_conversation,
inputs=[previous_conversations],
outputs=[chatbot, conversation_id]
)
previous_conversations.change(
load_conversation,
inputs=[previous_conversations],
outputs=[chat_history]
)
update_message_button.click(
update_message_in_chat,
inputs=[edit_message_id, edit_message_text, chat_history],
outputs=[chatbot]
)
delete_message_button.click(
delete_message_from_chat,
inputs=[delete_message_id, chat_history],
outputs=[chatbot]
)
save_chat_history_as_file.click(
save_chat_history,
inputs=[chatbot, conversation_id],
outputs=[download_file]
)
save_chat_history_to_db.click(
save_chat_history_to_db_wrapper,
inputs=[chatbot, conversation_id, media_content],
outputs=[conversation_id, gr.Textbox(label="Save Status")]
)
chatbot.select(show_edit_message, None, [edit_message_text, edit_message_id, update_message_button])
chatbot.select(show_delete_message, None, [delete_message_id, delete_message_button])
def create_chat_interface_stacked():
custom_css = """
.chatbot-container .message-wrap .message {
font-size: 14px !important;
}
"""
with gr.TabItem("Remote LLM Chat - Stacked"):
gr.Markdown("# Stacked Chat")
chat_history = gr.State([])
media_content = gr.State({})
selected_parts = gr.State([])
conversation_id = gr.State(None)
with gr.Row():
with gr.Column():
search_query_input = gr.Textbox(label="Search Query", placeholder="Enter your search query here...")
search_type_input = gr.Radio(choices=["Title", "URL", "Keyword", "Content"], value="Title",
label="Search By")
search_button = gr.Button("Search")
items_output = gr.Dropdown(label="Select Item", choices=[], interactive=True)
item_mapping = gr.State({})
with gr.Row():
use_content = gr.Checkbox(label="Use Content")
use_summary = gr.Checkbox(label="Use Summary")
use_prompt = gr.Checkbox(label="Use Prompt")
save_conversation = gr.Checkbox(label="Save Conversation", value=False, visible=True)
temp = gr.Slider(label="Temperature", minimum=0.00, maximum=1.0, step=0.05, value=0.7)
with gr.Row():
conversation_search = gr.Textbox(label="Search Conversations")
with gr.Row():
previous_conversations = gr.Dropdown(label="Select Conversation", choices=[], interactive=True)
with gr.Row():
search_conversations_btn = gr.Button("Search Conversations")
load_conversations_btn = gr.Button("Load Selected Conversation")
with gr.Column():
api_endpoint = gr.Dropdown(label="Select API Endpoint",
choices=["Local-LLM", "OpenAI", "Anthropic", "Cohere", "Groq", "DeepSeek",
"OpenRouter", "Mistral", "Llama.cpp", "Kobold", "Ooba", "Tabbyapi",
"VLLM", "ollama", "HuggingFace"])
api_key = gr.Textbox(label="API Key (if required)", type="password")
preset_prompt = gr.Dropdown(label="Select Preset Prompt",
choices=load_preset_prompts(),
visible=True)
system_prompt = gr.Textbox(label="System Prompt",
value="You are a helpful AI assistant.",
lines=3,
visible=True)
user_prompt = gr.Textbox(label="Custom User Prompt",
placeholder="Enter custom prompt here",
lines=3,
visible=True)
gr.Markdown("Scroll down for the chat window...")
with gr.Row():
with gr.Column(scale=1):
chatbot = gr.Chatbot(height=600, elem_classes="chatbot-container")
msg = gr.Textbox(label="Enter your message")
with gr.Row():
with gr.Column():
submit = gr.Button("Submit")
clear_chat_button = gr.Button("Clear Chat")
edit_message_id = gr.Number(label="Message ID to Edit", visible=False)
edit_message_text = gr.Textbox(label="Edit Message", visible=False)
update_message_button = gr.Button("Update Message", visible=False)
delete_message_id = gr.Number(label="Message ID to Delete", visible=False)
delete_message_button = gr.Button("Delete Message", visible=False)
save_chat_history_to_db = gr.Button("Save Chat History to DataBase")
save_chat_history_as_file = gr.Button("Save Chat History as File")
with gr.Column():
download_file = gr.File(label="Download Chat History")
# Restore original functionality
search_button.click(
fn=update_dropdown,
inputs=[search_query_input, search_type_input],
outputs=[items_output, item_mapping]
)
def update_prompts(preset_name):
prompts = update_user_prompt(preset_name)
return (
gr.update(value=prompts["user_prompt"], visible=True),
gr.update(value=prompts["system_prompt"], visible=True)
)
clear_chat_button.click(
clear_chat,
outputs=[chatbot, conversation_id]
)
preset_prompt.change(
update_prompts,
inputs=preset_prompt,
outputs=[user_prompt, system_prompt]
)
submit.click(
chat_wrapper,
inputs=[msg, chatbot, media_content, selected_parts, api_endpoint, api_key, user_prompt,
conversation_id, save_conversation, temp, system_prompt],
outputs=[msg, chatbot, conversation_id]
).then( # Clear the message box after submission
lambda x: gr.update(value=""),
inputs=[chatbot],
outputs=[msg]
).then( # Clear the user prompt after the first message
lambda: gr.update(value=""),
outputs=[user_prompt, system_prompt]
)
items_output.change(
update_chat_content,
inputs=[items_output, use_content, use_summary, use_prompt, item_mapping],
outputs=[media_content, selected_parts]
)
use_content.change(update_selected_parts, inputs=[use_content, use_summary, use_prompt],
outputs=[selected_parts])
use_summary.change(update_selected_parts, inputs=[use_content, use_summary, use_prompt],
outputs=[selected_parts])
use_prompt.change(update_selected_parts, inputs=[use_content, use_summary, use_prompt],
outputs=[selected_parts])
items_output.change(debug_output, inputs=[media_content, selected_parts], outputs=[])
search_conversations_btn.click(
search_conversations,
inputs=[conversation_search],
outputs=[previous_conversations]
)
load_conversations_btn.click(
clear_chat,
outputs=[chatbot, chat_history]
).then(
load_conversation,
inputs=[previous_conversations],
outputs=[chatbot, conversation_id]
)
previous_conversations.change(
load_conversation,
inputs=[previous_conversations],
outputs=[chat_history]
)
update_message_button.click(
update_message_in_chat,
inputs=[edit_message_id, edit_message_text, chat_history],
outputs=[chatbot]
)
delete_message_button.click(
delete_message_from_chat,
inputs=[delete_message_id, chat_history],
outputs=[chatbot]
)
save_chat_history_as_file.click(
save_chat_history,
inputs=[chatbot, conversation_id],
outputs=[download_file]
)
save_chat_history_to_db.click(
save_chat_history_to_db_wrapper,
inputs=[chatbot, conversation_id, media_content],
outputs=[conversation_id, gr.Textbox(label="Save Status")]
)
chatbot.select(show_edit_message, None, [edit_message_text, edit_message_id, update_message_button])
chatbot.select(show_delete_message, None, [delete_message_id, delete_message_button])
# FIXME - System prompts
def create_chat_interface_multi_api():
custom_css = """
.chatbot-container .message-wrap .message {
font-size: 14px !important;
}
.chat-window {
height: 400px;
overflow-y: auto;
}
"""
with gr.TabItem("One Prompt - Multiple APIs"):
gr.Markdown("# One Prompt but Multiple API Chat Interface")
with gr.Row():
with gr.Column(scale=1):
search_query_input = gr.Textbox(label="Search Query", placeholder="Enter your search query here...")
search_type_input = gr.Radio(choices=["Title", "URL", "Keyword", "Content"], value="Title",
label="Search By")
search_button = gr.Button("Search")
items_output = gr.Dropdown(label="Select Item", choices=[], interactive=True)
item_mapping = gr.State({})
with gr.Row():
use_content = gr.Checkbox(label="Use Content")
use_summary = gr.Checkbox(label="Use Summary")
use_prompt = gr.Checkbox(label="Use Prompt")
with gr.Column():
preset_prompt = gr.Dropdown(label="Select Preset Prompt", choices=load_preset_prompts(), visible=True)
system_prompt = gr.Textbox(label="System Prompt", value="You are a helpful AI assistant.", lines=5)
user_prompt = gr.Textbox(label="Modify Prompt", lines=5, value=".")
with gr.Row():
chatbots = []
api_endpoints = []
api_keys = []
temperatures = []
for i in range(3):
with gr.Column():
gr.Markdown(f"### Chat Window {i + 1}")
api_endpoint = gr.Dropdown(label=f"API Endpoint {i + 1}",
choices=["Local-LLM", "OpenAI", "Anthropic", "Cohere", "Groq",
"DeepSeek", "Mistral", "OpenRouter", "Llama.cpp", "Kobold",
"Ooba",
"Tabbyapi", "VLLM", "ollama", "HuggingFace"])
api_key = gr.Textbox(label=f"API Key {i + 1} (if required)", type="password")
temperature = gr.Slider(label=f"Temperature {i + 1}", minimum=0.0, maximum=1.0, step=0.05,
value=0.7)
chatbot = gr.Chatbot(height=800, elem_classes="chat-window")
chatbots.append(chatbot)
api_endpoints.append(api_endpoint)
api_keys.append(api_key)
temperatures.append(temperature)
with gr.Row():
msg = gr.Textbox(label="Enter your message", scale=4)
submit = gr.Button("Submit", scale=1)
# FIXME - clear chat
# clear_chat_button = gr.Button("Clear Chat")
#
# clear_chat_button.click(
# clear_chat,
# outputs=[chatbot]
# )
# State variables
chat_history = [gr.State([]) for _ in range(3)]
media_content = gr.State({})
selected_parts = gr.State([])
conversation_id = gr.State(None)
# Event handlers
search_button.click(
fn=update_dropdown,
inputs=[search_query_input, search_type_input],
outputs=[items_output, item_mapping]
)
preset_prompt.change(update_user_prompt, inputs=preset_prompt, outputs=user_prompt)
def chat_wrapper_multi(message, custom_prompt, system_prompt, *args):
chat_histories = args[:3]
chatbots = args[3:6]
api_endpoints = args[6:9]
api_keys = args[9:12]
temperatures = args[12:15]
media_content = args[15]
selected_parts = args[16]
new_chat_histories = []
new_chatbots = []
for i in range(3):
# Call chat_wrapper with dummy values for conversation_id and save_conversation
bot_message, new_history, _ = chat_wrapper(
message, chat_histories[i], media_content, selected_parts,
api_endpoints[i], api_keys[i], custom_prompt, None, # None for conversation_id
False, # False for save_conversation
temperature=temperatures[i],
system_prompt=system_prompt
)
new_chatbot = chatbots[i] + [(message, bot_message)]
new_chat_histories.append(new_history)
new_chatbots.append(new_chatbot)
return [gr.update(value="")] + new_chatbots + new_chat_histories
# In the create_chat_interface_multi_api function:
submit.click(
chat_wrapper_multi,
inputs=[msg, user_prompt,
system_prompt] + chat_history + chatbots + api_endpoints + api_keys + temperatures +
[media_content, selected_parts],
outputs=[msg] + chatbots + chat_history
).then(
lambda: (gr.update(value=""), gr.update(value="")),
outputs=[msg, user_prompt]
)
items_output.change(
update_chat_content,
inputs=[items_output, use_content, use_summary, use_prompt, item_mapping],
outputs=[media_content, selected_parts]
)
for checkbox in [use_content, use_summary, use_prompt]:
checkbox.change(
update_selected_parts,
inputs=[use_content, use_summary, use_prompt],
outputs=[selected_parts]
)
def create_chat_interface_four():
custom_css = """
.chatbot-container .message-wrap .message {
font-size: 14px !important;
}
.chat-window {
height: 400px;
overflow-y: auto;
}
"""
with gr.TabItem("Four Independent API Chats"):
gr.Markdown("# Four Independent API Chat Interfaces")
with gr.Row():
with gr.Column():
preset_prompt = gr.Dropdown(label="Select Preset Prompt", choices=load_preset_prompts(), visible=True)
user_prompt = gr.Textbox(label="Modify Prompt", lines=3, value=".")
with gr.Column():
gr.Markdown("Scroll down for the chat windows...")
chat_interfaces = []
for row in range(2):
with gr.Row():
for col in range(2):
i = row * 2 + col
with gr.Column():
gr.Markdown(f"### Chat Window {i + 1}")
api_endpoint = gr.Dropdown(label=f"API Endpoint {i + 1}",
choices=["Local-LLM", "OpenAI", "Anthropic", "Cohere", "Groq",
"DeepSeek", "Mistral", "OpenRouter", "Llama.cpp", "Kobold",
"Ooba",
"Tabbyapi", "VLLM", "ollama", "HuggingFace"])
api_key = gr.Textbox(label=f"API Key {i + 1} (if required)", type="password")
temperature = gr.Slider(label=f"Temperature {i + 1}", minimum=0.0, maximum=1.0, step=0.05,
value=0.7)
chatbot = gr.Chatbot(height=400, elem_classes="chat-window")
msg = gr.Textbox(label=f"Enter your message for Chat {i + 1}")
submit = gr.Button(f"Submit to Chat {i + 1}")
chat_interfaces.append({
'api_endpoint': api_endpoint,
'api_key': api_key,
'temperature': temperature,
'chatbot': chatbot,
'msg': msg,
'submit': submit,
'chat_history': gr.State([])
})
preset_prompt.change(update_user_prompt, inputs=preset_prompt, outputs=user_prompt)
def chat_wrapper_single(message, chat_history, api_endpoint, api_key, temperature, user_prompt):
logging.debug(f"Chat Wrapper Single - Message: {message}, Chat History: {chat_history}")
new_msg, new_history, _ = chat_wrapper(
message, chat_history, {}, [], # Empty media_content and selected_parts
api_endpoint, api_key, user_prompt, None, # No conversation_id
False, # Not saving conversation
temperature=temperature, system_prompt=""
)
chat_history.append((message, new_msg))
return "", chat_history, chat_history
for interface in chat_interfaces:
logging.debug(f"Chat Interface - Clicked Submit for Chat {interface['chatbot']}"),
interface['submit'].click(
chat_wrapper_single,
inputs=[
interface['msg'],
interface['chat_history'],
interface['api_endpoint'],
interface['api_key'],
interface['temperature'],
user_prompt
],
outputs=[
interface['msg'],
interface['chatbot'],
interface['chat_history']
]
)
def chat_wrapper_single(message, chat_history, chatbot, api_endpoint, api_key, temperature, media_content,
selected_parts, conversation_id, save_conversation, user_prompt):
new_msg, new_history, new_conv_id = chat_wrapper(
message, chat_history, media_content, selected_parts,
api_endpoint, api_key, user_prompt, conversation_id,
save_conversation, temperature, system_prompt=""
)
if new_msg:
updated_chatbot = chatbot + [(message, new_msg)]
else:
updated_chatbot = chatbot
return new_msg, updated_chatbot, new_history, new_conv_id
# FIXME - Finish implementing functions + testing/valdidation
def create_chat_management_tab():
with gr.TabItem("Chat Management"):
gr.Markdown("# Chat Management")
with gr.Row():
search_query = gr.Textbox(label="Search Conversations")
search_button = gr.Button("Search")
conversation_list = gr.Dropdown(label="Select Conversation", choices=[])
conversation_mapping = gr.State({})
with gr.Tabs():
with gr.TabItem("Edit"):
chat_content = gr.TextArea(label="Chat Content (JSON)", lines=20, max_lines=50)
save_button = gr.Button("Save Changes")
with gr.TabItem("Preview"):
chat_preview = gr.HTML(label="Chat Preview")
result_message = gr.Markdown("")
def search_conversations(query):
conversations = search_chat_conversations(query)
choices = [f"{conv['conversation_name']} (Media: {conv['media_title']}, ID: {conv['id']})" for conv in
conversations]
mapping = {choice: conv['id'] for choice, conv in zip(choices, conversations)}
return gr.update(choices=choices), mapping
def load_conversations(selected, conversation_mapping):
logging.info(f"Selected: {selected}")
logging.info(f"Conversation mapping: {conversation_mapping}")
try:
if selected and selected in conversation_mapping:
conversation_id = conversation_mapping[selected]
messages = get_chat_messages(conversation_id)
conversation_data = {
"conversation_id": conversation_id,
"messages": messages
}
json_content = json.dumps(conversation_data, indent=2)
# Create HTML preview
html_preview = "<div style='max-height: 500px; overflow-y: auto;'>"
for msg in messages:
sender_style = "background-color: #e6f3ff;" if msg[
'sender'] == 'user' else "background-color: #f0f0f0;"
html_preview += f"<div style='margin-bottom: 10px; padding: 10px; border-radius: 5px; {sender_style}'>"
html_preview += f"<strong>{msg['sender']}:</strong> {html.escape(msg['message'])}<br>"
html_preview += f"<small>Timestamp: {msg['timestamp']}</small>"
html_preview += "</div>"
html_preview += "</div>"
logging.info("Returning json_content and html_preview")
return json_content, html_preview
else:
logging.warning("No conversation selected or not in mapping")
return "", "<p>No conversation selected</p>"
except Exception as e:
logging.error(f"Error in load_conversations: {str(e)}")
return f"Error: {str(e)}", "<p>Error loading conversation</p>"
def validate_conversation_json(content):
try:
data = json.loads(content)
if not isinstance(data, dict):
return False, "Invalid JSON structure: root should be an object"
if "conversation_id" not in data or not isinstance(data["conversation_id"], int):
return False, "Missing or invalid conversation_id"
if "messages" not in data or not isinstance(data["messages"], list):
return False, "Missing or invalid messages array"
for msg in data["messages"]:
if not all(key in msg for key in ["sender", "message"]):
return False, "Invalid message structure: missing required fields"
return True, data
except json.JSONDecodeError as e:
return False, f"Invalid JSON: {str(e)}"
def save_conversation(selected, conversation_mapping, content):
if not selected or selected not in conversation_mapping:
return "Please select a conversation before saving.", "<p>No changes made</p>"
conversation_id = conversation_mapping[selected]
is_valid, result = validate_conversation_json(content)
if not is_valid:
return f"Error: {result}", "<p>No changes made due to error</p>"
conversation_data = result
if conversation_data["conversation_id"] != conversation_id:
return "Error: Conversation ID mismatch.", "<p>No changes made due to ID mismatch</p>"
try:
with db.get_connection() as conn:
conn.execute("BEGIN TRANSACTION")
cursor = conn.cursor()
# Backup original conversation
cursor.execute("SELECT * FROM ChatMessages WHERE conversation_id = ?", (conversation_id,))
original_messages = cursor.fetchall()
backup_data = json.dumps({"conversation_id": conversation_id, "messages": original_messages})
# You might want to save this backup_data somewhere
# Delete existing messages
cursor.execute("DELETE FROM ChatMessages WHERE conversation_id = ?", (conversation_id,))
# Insert updated messages
for message in conversation_data["messages"]:
cursor.execute('''
INSERT INTO ChatMessages (conversation_id, sender, message, timestamp)
VALUES (?, ?, ?, COALESCE(?, CURRENT_TIMESTAMP))
''', (conversation_id, message["sender"], message["message"], message.get("timestamp")))
conn.commit()
# Create updated HTML preview
html_preview = "<div style='max-height: 500px; overflow-y: auto;'>"
for msg in conversation_data["messages"]:
sender_style = "background-color: #e6f3ff;" if msg[
'sender'] == 'user' else "background-color: #f0f0f0;"
html_preview += f"<div style='margin-bottom: 10px; padding: 10px; border-radius: 5px; {sender_style}'>"
html_preview += f"<strong>{msg['sender']}:</strong> {html.escape(msg['message'])}<br>"
html_preview += f"<small>Timestamp: {msg.get('timestamp', 'N/A')}</small>"
html_preview += "</div>"
html_preview += "</div>"
return "Conversation updated successfully.", html_preview
except sqlite3.Error as e:
conn.rollback()
logging.error(f"Database error in save_conversation: {e}")
return f"Error updating conversation: {str(e)}", "<p>Error occurred while saving</p>"
except Exception as e:
conn.rollback()
logging.error(f"Unexpected error in save_conversation: {e}")
return f"Unexpected error: {str(e)}", "<p>Unexpected error occurred</p>"
def parse_formatted_content(formatted_content):
lines = formatted_content.split('\n')
conversation_id = int(lines[0].split(': ')[1])
timestamp = lines[1].split(': ')[1]
history = []
current_role = None
current_content = None
for line in lines[3:]:
if line.startswith("Role: "):
if current_role is not None:
history.append({"role": current_role, "content": ["", current_content]})
current_role = line.split(': ')[1]
elif line.startswith("Content: "):
current_content = line.split(': ', 1)[1]
if current_role is not None:
history.append({"role": current_role, "content": ["", current_content]})
return json.dumps({
"conversation_id": conversation_id,
"timestamp": timestamp,
"history": history
}, indent=2)
search_button.click(
search_conversations,
inputs=[search_query],
outputs=[conversation_list, conversation_mapping]
)
conversation_list.change(
load_conversations,
inputs=[conversation_list, conversation_mapping],
outputs=[chat_content, chat_preview]
)
save_button.click(
save_conversation,
inputs=[conversation_list, conversation_mapping, chat_content],
outputs=[result_message, chat_preview]
)
return search_query, search_button, conversation_list, conversation_mapping, chat_content, save_button, result_message, chat_preview
# Load workflows from a JSON file
json_path = Path('./Helper_Scripts/Workflows/Workflows.json')
with json_path.open('r') as f:
workflows = json.load(f)
# FIXME - broken Completely. Doesn't work.
def chat_workflows_tab():
with gr.TabItem("Chat Workflows"):
gr.Markdown("# Workflows using LLMs")
with gr.Row():
workflow_selector = gr.Dropdown(label="Select Workflow", choices=[wf['name'] for wf in workflows])
api_selector = gr.Dropdown(
label="Select API Endpoint",
choices=["OpenAI", "Anthropic", "Cohere", "Groq", "DeepSeek", "Mistral", "OpenRouter",
"Llama.cpp", "Kobold", "Ooba", "Tabbyapi", "VLLM", "ollama", "HuggingFace"],
value="OpenAI"
)
api_key_input = gr.Textbox(label="API Key (optional)", type="password")
context_input = gr.Textbox(label="Initial Context (optional)", lines=5)
# Create a container for dynamic components
with gr.Column() as dynamic_components:
prompt_displays = []
user_inputs = []
output_boxes = []
process_buttons = []
regenerate_buttons = []
# Create the maximum number of components needed
max_steps = max(len(wf['prompts']) for wf in workflows)
for i in range(max_steps):
prompt_displays.append(gr.Markdown(visible=False))
user_inputs.append(gr.Textbox(label=f"Your Response", lines=2, visible=False))
output_boxes.append(gr.Textbox(label=f"AI Output", lines=5, visible=False))
with gr.Row():
process_buttons.append(gr.Button(f"Process Step {i + 1}", visible=False))
regenerate_buttons.append(gr.Button(f"🔄 Regenerate", visible=False))
def update_workflow_ui(workflow_name):
selected_workflow = next(wf for wf in workflows if wf['name'] == workflow_name)
num_prompts = len(selected_workflow['prompts'])
prompt_updates = []
input_updates = []
output_updates = []
button_updates = []
regenerate_updates = []
for i in range(max_steps):
if i < num_prompts:
prompt_updates.append(
gr.update(value=f"**Step {i + 1}:** {selected_workflow['prompts'][i]}", visible=True))
input_updates.append(gr.update(value="", visible=True, interactive=(i == 0)))
output_updates.append(gr.update(value="", visible=True))
button_updates.append(gr.update(visible=(i == 0)))
regenerate_updates.append(gr.update(visible=False))
else:
prompt_updates.append(gr.update(visible=False))
input_updates.append(gr.update(visible=False))
output_updates.append(gr.update(visible=False))
button_updates.append(gr.update(visible=False))
regenerate_updates.append(gr.update(visible=False))
return prompt_updates + input_updates + output_updates + button_updates + regenerate_updates
def process(context, user_inputs, workflow_name, api_endpoint, api_key, step):
selected_workflow = next(wf for wf in workflows if wf['name'] == workflow_name)
# Build up the context from previous steps
full_context = context + "\n\n"
for i in range(step + 1):
full_context += f"Question: {selected_workflow['prompts'][i]}\n"
full_context += f"Answer: {user_inputs[i]}\n"
if i < step:
full_context += f"AI Output: {output_boxes[i].value}\n\n"
result = process_with_llm(workflow_name, full_context, selected_workflow['prompts'][step], api_endpoint,
api_key)
prompt_updates = [gr.update() for _ in range(max_steps)]
input_updates = []
output_updates = [gr.update() for _ in range(max_steps)]
button_updates = []
regenerate_updates = []
for i in range(len(selected_workflow['prompts'])):
if i == step:
regenerate_updates.append(gr.update(visible=True))
elif i == step + 1:
input_updates.append(gr.update(interactive=True))
button_updates.append(gr.update(visible=True))
regenerate_updates.append(gr.update(visible=False))
elif i > step + 1:
input_updates.append(gr.update(interactive=False))
button_updates.append(gr.update(visible=False))
regenerate_updates.append(gr.update(visible=False))
else:
input_updates.append(gr.update(interactive=False))
button_updates.append(gr.update(visible=False))
regenerate_updates.append(gr.update(visible=True))
return [result] + prompt_updates + input_updates + output_updates + button_updates + regenerate_updates
# Set up event handlers
workflow_selector.change(
update_workflow_ui,
inputs=[workflow_selector],
outputs=prompt_displays + user_inputs + output_boxes + process_buttons + regenerate_buttons
)
# Set up process button click events
for i, button in enumerate(process_buttons):
button.click(
fn=lambda context, *user_inputs, wf_name, api_endpoint, api_key, step=i: process(context, user_inputs,
wf_name, api_endpoint,
api_key, step),
inputs=[context_input] + user_inputs + [workflow_selector, api_selector, api_key_input],
outputs=[output_boxes[
i]] + prompt_displays + user_inputs + output_boxes + process_buttons + regenerate_buttons
)
# Set up regenerate button click events
for i, button in enumerate(regenerate_buttons):
button.click(
fn=lambda context, *user_inputs, wf_name, api_endpoint, api_key, step=i: process(context, user_inputs,
wf_name, api_endpoint,
api_key, step),
inputs=[context_input] + user_inputs + [workflow_selector, api_selector, api_key_input],
outputs=[output_boxes[
i]] + prompt_displays + user_inputs + output_boxes + process_buttons + regenerate_buttons
)
return workflow_selector, api_selector, api_key_input, context_input, dynamic_components
# Mock function to simulate LLM processing
def process_with_llm(workflow, context, prompt, api_endpoint, api_key):
api_key_snippet = api_key[:5] + "..." if api_key else "Not provided"
return f"LLM output using {api_endpoint} (API Key: {api_key_snippet}) for {workflow} with context: {context[:30]}... and prompt: {prompt[:30]}..."
#
# End of Chat_ui.py
####################################################################################################################### |