import pickle
import subprocess
import sys
import gradio as gr
import os
from openai import AsyncOpenAI
from openai import OpenAI
from huggingface_hub import InferenceClient
# File: enhanced_gradio_interface.py
import asyncio
from collections import defaultdict
import json
import os
import re
from time import time
import uuid
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
from threading import Lock
import threading
import json
import os
import queue
import traceback
import uuid
from typing import Coroutine, Dict, List, Any, Optional, Callable
from dataclasses import dataclass
from queue import Queue, Empty
from threading import Lock, Event, Thread
import threading
from concurrent.futures import ThreadPoolExecutor
import time
import gradio as gr
from openai import AsyncOpenAI, OpenAI
import pyttsx3
from rich.console import Console
api_key = ""
client = OpenAI(
base_url="https://Localhost/v1",
api_key=api_key
)
BASE_URL="http://localhost:1234/v1"
BASE_API_KEY="not-needed"
BASE_CLIENT = AsyncOpenAI(
base_url=BASE_URL,
api_key=BASE_API_KEY
) # Global state for client
BASEMODEL_ID = "leroydyer/qwen/qwen3-0.6b-q4_k_m.gguf" # Global state for selected model ID
CLIENT =OpenAI(
base_url=BASE_URL,
api_key=BASE_API_KEY
) # Global state for client
# --- Global Variables (if needed) ---
console = Console()
# --- Configuration ---
LOCAL_BASE_URL = "http://localhost:1234/v1"
LOCAL_API_KEY = "not-needed"
# HuggingFace Spaces configuration
HF_INFERENCE_URL = "https://api-inference.huggingface.co/models/"
HF_API_KEY = os.getenv("HF_API_KEY", "")
DEFAULT_TEMPERATURE = 0.7
DEFAULT_MAX_TOKENS = 5000
console = Console()
#############################################################
@dataclass
class LLMMessage:
role: str
content: str
message_id: str = None
conversation_id: str = None
timestamp: float = None
metadata: Dict[str, Any] = None
def __post_init__(self):
if self.message_id is None:
self.message_id = str(uuid.uuid4())
if self.timestamp is None:
self.timestamp = time.time()
if self.metadata is None:
self.metadata = {}
@dataclass
class LLMRequest:
message: LLMMessage
response_event: str = None
callback: Callable = None
def __post_init__(self):
if self.response_event is None:
self.response_event = f"llm_response_{self.message.message_id}"
@dataclass
class LLMResponse:
message: LLMMessage
request_id: str
success: bool = True
error: str = None
#############################################################
class EventManager:
def __init__(self):
self._handlers = defaultdict(list)
self._lock = threading.Lock()
def register(self, event: str, handler: Callable):
with self._lock:
self._handlers[event].append(handler)
def unregister(self, event: str, handler: Callable):
with self._lock:
if event in self._handlers and handler in self._handlers[event]:
self._handlers[event].remove(handler)
def raise_event(self, event: str, data: Any):
with self._lock:
handlers = self._handlers[event][:]
for handler in handlers:
try:
handler(data)
except Exception as e:
console.log(f"Error in event handler for {event}: {e}", style="bold red")
EVENT_MANAGER = EventManager()
def RegisterEvent(event: str, handler: Callable):
EVENT_MANAGER.register(event, handler)
def RaiseEvent(event: str, data: Any):
EVENT_MANAGER.raise_event(event, data)
def UnregisterEvent(event: str, handler: Callable):
EVENT_MANAGER.unregister(event, handler)
#############################################################
@dataclass
class CanvasArtifact:
id: str
type: str # 'code', 'diagram', 'text', 'image'
content: str
title: str
timestamp: float
metadata: Dict[str, Any] = None
def __post_init__(self):
if self.metadata is None:
self.metadata = {}
class LLMAgent:
"""Main Agent Driver !
Agent For Multiple messages at once ,
has a message queing service as well as agenerator method for easy intergration with console
applications as well as ui !"""
def __init__(
self,
model_id: str = BASEMODEL_ID,
system_prompt: str = None,
max_queue_size: int = 1000,
max_retries: int = 3,
timeout: int = 30000,
max_tokens: int = 5000,
temperature: float = 0.3,
base_url: str = "http://localhost:1234/v1",
api_key: str = "not-needed",
generate_fn: Callable[[List[Dict[str, str]]], Coroutine[Any, Any, str]] = None,
):
self.model_id = model_id
self.system_prompt = system_prompt or "You are a helpful AI assistant."
self.request_queue = Queue(maxsize=max_queue_size)
self.max_retries = max_retries
self.timeout = timeout
self.is_running = False
self._stop_event = Event()
self.processing_thread = None
# Canvas artifacts
self.canvas_artifacts: Dict[str, List[CanvasArtifact]] = defaultdict(list)
self.max_canvas_artifacts = 1000
# Conversation tracking
self.conversations: Dict[str, List[LLMMessage]] = {}
self.max_history_length = 100
self._generate = generate_fn or self._default_generate
self.api_key = api_key
self.base_url = base_url
self.max_tokens = max_tokens
self.temperature = temperature
self.async_client = self.CreateClient(base_url, api_key)
self.current_conversation = "default"
# Active requests waiting for responses
self.pending_requests: Dict[str, LLMRequest] = {}
self.pending_requests_lock = Lock()
# Register internal event handlers
self._register_event_handlers()
# Register internal event handlers
self._register_event_handlers()
# Speech synthesis
try:
self.tts_engine = pyttsx3.init()
self.setup_tts()
self.speech_enabled = True
except Exception as e:
console.log(f"[yellow]TTS not available: {e}[/yellow]")
self.speech_enabled = False
console.log("[bold green]🚀 Enhanced LLM Agent Initialized[/bold green]")
# Start the processing thread immediately
self.start()
def setup_tts(self):
"""Configure text-to-speech engine"""
if hasattr(self, 'tts_engine'):
voices = self.tts_engine.getProperty('voices')
if voices:
self.tts_engine.setProperty('voice', voices[0].id)
self.tts_engine.setProperty('rate', 150)
self.tts_engine.setProperty('volume', 0.8)
def speak(self, text: str):
"""Convert text to speech in a non-blocking way"""
if not hasattr(self, 'speech_enabled') or not self.speech_enabled:
return
def _speak():
try:
# Clean text for speech (remove markdown, code blocks)
clean_text = re.sub(r'```.*?```', '', text, flags=re.DOTALL)
clean_text = re.sub(r'`.*?`', '', clean_text)
clean_text = clean_text.strip()
if clean_text:
self.tts_engine.say(clean_text)
self.tts_engine.runAndWait()
else:
self.tts_engine.say(text)
self.tts_engine.runAndWait()
except Exception as e:
console.log(f"[red]TTS Error: {e}[/red]")
thread = threading.Thread(target=_speak, daemon=True)
thread.start()
async def _default_generate(self, messages: List[Dict[str, str]]) -> str:
"""Default generate function if none provided"""
return await self.openai_generate(messages)
def create_interface(self):
"""Create the full LCARS-styled interface without HuggingFace options"""
lcars_css = """
:root {
--lcars-orange: #FF9900;
--lcars-red: #FF0033;
--lcars-blue: #6699FF;
--lcars-purple: #CC99FF;
--lcars-pale-blue: #99CCFF;
--lcars-black: #000000;
--lcars-dark-blue: #3366CC;
--lcars-gray: #424242;
--lcars-yellow: #FFFF66;
}
body {
background: var(--lcars-black);
color: var(--lcars-orange);
font-family: 'Antonio', 'LCD', 'Courier New', monospace;
margin: 0;
padding: 0;
}
.gradio-container {
background: var(--lcars-black) !important;
min-height: 100vh;
}
.lcars-container {
background: var(--lcars-black);
border: 4px solid var(--lcars-orange);
border-radius: 0 30px 0 0;
min-height: 100vh;
padding: 20px;
}
.lcars-header {
background: linear-gradient(90deg, var(--lcars-red), var(--lcars-orange));
padding: 20px 40px;
border-radius: 0 60px 0 0;
margin: -20px -20px 20px -20px;
border-bottom: 6px solid var(--lcars-blue);
}
.lcars-title {
font-size: 2.5em;
font-weight: bold;
color: var(--lcars-black);
margin: 0;
}
.lcars-subtitle {
font-size: 1.2em;
color: var(--lcars-black);
margin: 10px 0 0 0;
}
.lcars-panel {
background: rgba(66, 66, 66, 0.9);
border: 2px solid var(--lcars-orange);
border-radius: 0 20px 0 20px;
padding: 15px;
margin-bottom: 15px;
}
.lcars-button {
background: var(--lcars-orange);
color: var(--lcars-black) !important;
border: none !important;
border-radius: 0 15px 0 15px !important;
padding: 10px 20px !important;
font-family: inherit !important;
font-weight: bold !important;
margin: 5px !important;
}
.lcars-button:hover {
background: var(--lcars-red) !important;
}
.lcars-input {
background: var(--lcars-black) !important;
color: var(--lcars-orange) !important;
border: 2px solid var(--lcars-blue) !important;
border-radius: 0 10px 0 10px !important;
padding: 10px !important;
}
.lcars-chatbot {
background: var(--lcars-black) !important;
border: 2px solid var(--lcars-purple) !important;
border-radius: 0 15px 0 15px !important;
}
.status-indicator {
display: inline-block;
width: 12px;
height: 12px;
border-radius: 50%;
background: var(--lcars-red);
margin-right: 8px;
}
.status-online {
background: var(--lcars-blue);
animation: pulse 2s infinite;
}
@keyframes pulse {
0% { opacity: 1; }
50% { opacity: 0.5; }
100% { opacity: 1; }
}
"""
with gr.Blocks(css=lcars_css, theme=gr.themes.Default(), title="LCARS Terminal") as interface:
with gr.Column(elem_classes="lcars-container"):
# Header
with gr.Row(elem_classes="lcars-header"):
gr.Markdown("""
🚀 LCARS TERMINAL
STARFLEET AI DEVELOPMENT CONSOLE
SYSTEM ONLINE
""")
# Main Content
with gr.Row():
# Left Sidebar
with gr.Column(scale=1):
# Configuration Panel
with gr.Column(elem_classes="lcars-panel"):
pass
# Canvas Artifacts
with gr.Column(elem_classes="lcars-panel"):
gr.Markdown("""### 🎨 CANVAS ARTIFACTS""")
artifact_display = gr.JSON(label="")
with gr.Row():
refresh_artifacts_btn = gr.Button("🔄 Refresh", elem_classes="lcars-button")
clear_canvas_btn = gr.Button("🗑️ Clear Canvas", elem_classes="lcars-button")
# Main Content Area
with gr.Column(scale=2):
# Code Canvas
with gr.Accordion("💻 COLLABORATIVE CODE CANVAS", open=False):
code_editor = gr.Code(interactive=True,
value="# Welcome to LCARS Collaborative Canvas\nprint('Hello, Starfleet!')",
language="python",
lines=15,
label=""
)
with gr.Row():
load_to_chat_btn = gr.Button("💬 Discuss Code", elem_classes="lcars-button")
analyze_btn = gr.Button("🔍 Analyze", elem_classes="lcars-button")
optimize_btn = gr.Button("⚡ Optimize", elem_classes="lcars-button")
# Chat Interface
with gr.Column(elem_classes="lcars-panel"):
gr.Markdown("""### 💬 MISSION LOG""")
chatbot = gr.Chatbot(label="", height=300)
with gr.Row():
message_input = gr.Textbox(
placeholder="Enter your command or query...",
show_label=False,
lines=2,
scale=4
)
send_btn = gr.Button("🚀 SEND", elem_classes="lcars-button", scale=1)
# Status
with gr.Row():
status_display = gr.Textbox(
value="LCARS terminal operational. Awaiting commands.",
label="Status",
max_lines=2
)
with gr.Column(scale=0):
clear_chat_btn = gr.Button("🗑️ Clear Chat", elem_classes="lcars-button")
new_session_btn = gr.Button("🆕 New Session", elem_classes="lcars-button")
# Event handlers are connected here, no change needed
async def process_message(message, history, speech_enabled=True):
if not message.strip():
return "", history, "Please enter a message"
history = history + [[message, None]]
try:
# Fixed: Uses the new chat_with_canvas method which includes canvas context
response = await self.chat_with_canvas(
message, self.current_conversation, include_canvas=True
)
history[-1][1] = response
if speech_enabled and self.speech_enabled:
self.speak(response)
artifacts = self.get_canvas_summary(self.current_conversation)
status = f"✅ Response received. Canvas artifacts: {len(artifacts)}"
return "", history, status, artifacts
except Exception as e:
error_msg = f"❌ Error: {str(e)}"
history[-1][1] = error_msg
return "", history, error_msg, self.get_canvas_summary(self.current_conversation)
def get_artifacts():
return self.get_canvas_summary(self.current_conversation)
def clear_canvas():
self.clear_canvas(self.current_conversation)
return [], "✅ Canvas cleared"
def clear_chat():
self.clear_conversation(self.current_conversation)
return [], "✅ Chat cleared"
def new_session():
self.clear_conversation(self.current_conversation)
self.clear_canvas(self.current_conversation)
return [], "# New session started\nprint('Ready!')", "🆕 New session started", []
# Connect events
send_btn.click(process_message,
inputs=[message_input, chatbot],
outputs=[message_input, chatbot, status_display, artifact_display])
message_input.submit(process_message,
inputs=[message_input, chatbot],
outputs=[message_input, chatbot, status_display, artifact_display])
refresh_artifacts_btn.click(get_artifacts, outputs=artifact_display)
clear_canvas_btn.click(clear_canvas, outputs=[artifact_display, status_display])
clear_chat_btn.click(clear_chat, outputs=[chatbot, status_display])
new_session_btn.click(new_session, outputs=[chatbot, code_editor, status_display, artifact_display])
return interface
def _register_event_handlers(self):
"""Register internal event handlers for response routing"""
RegisterEvent("llm_internal_response", self._handle_internal_response)
def _handle_internal_response(self, response: LLMResponse):
"""Route responses to the appropriate request handlers"""
console.log(f"[bold cyan]Handling internal response for: {response.request_id}[/bold cyan]")
request = None
with self.pending_requests_lock:
if response.request_id in self.pending_requests:
request = self.pending_requests[response.request_id]
del self.pending_requests[response.request_id]
console.log(f"Found pending request for: {response.request_id}")
else:
console.log(f"No pending request found for: {response.request_id}", style="yellow")
return
# Raise the specific response event
if request.response_event:
console.log(f"[bold green]Raising event: {request.response_event}[/bold green]")
RaiseEvent(request.response_event, response)
# Call callback if provided
if request.callback:
try:
console.log(f"[bold yellow]Calling callback for: {response.request_id}[/bold yellow]")
request.callback(response)
except Exception as e:
console.log(f"Error in callback: {e}", style="bold red")
def _add_to_conversation_history(self, conversation_id: str, message: LLMMessage):
"""Add message to conversation history"""
if conversation_id not in self.conversations:
self.conversations[conversation_id] = []
self.conversations[conversation_id].append(message)
# Trim history if too long
if len(self.conversations[conversation_id]) > self.max_history_length * 2:
self.conversations[conversation_id] = self.conversations[conversation_id][-(self.max_history_length * 2):]
def _build_messages_from_conversation(self, conversation_id: str, new_message: LLMMessage) -> List[Dict[str, str]]:
"""Build message list from conversation history"""
messages = []
# Add system prompt
if self.system_prompt:
messages.append({"role": "system", "content": self.system_prompt})
# Add conversation history
if conversation_id in self.conversations:
for msg in self.conversations[conversation_id][-self.max_history_length:]:
messages.append({"role": msg.role, "content": msg.content})
# Add the new message
messages.append({"role": new_message.role, "content": new_message.content})
return messages
def _process_llm_request(self, request: LLMRequest):
"""Process a single LLM request"""
console.log(f"[bold green]Processing LLM request: {request.message.message_id}[/bold green]")
try:
# Build messages for LLM
messages = self._build_messages_from_conversation(
request.message.conversation_id or "default",
request.message
)
console.log(f"Calling LLM with {len(messages)} messages")
# Call LLM - Use sync call for thread compatibility
response_content = self._call_llm_sync(messages)
console.log(f"[bold green]LLM response received: {response_content}...[/bold green]")
# Create response message
response_message = LLMMessage(
role="assistant",
content=response_content,
conversation_id=request.message.conversation_id,
metadata={"request_id": request.message.message_id}
)
# Update conversation history
self._add_to_conversation_history(
request.message.conversation_id or "default",
request.message
)
self._add_to_conversation_history(
request.message.conversation_id or "default",
response_message
)
# Create and send response
response = LLMResponse(
message=response_message,
request_id=request.message.message_id,
success=True
)
console.log(f"[bold blue]Sending internal response for: {request.message.message_id}[/bold blue]")
RaiseEvent("llm_internal_response", response)
except Exception as e:
console.log(f"[bold red]Error processing LLM request: {e}[/bold red]")
traceback.print_exc()
# Create error response
error_response = LLMResponse(
message=LLMMessage(
role="system",
content=f"Error: {str(e)}",
conversation_id=request.message.conversation_id
),
request_id=request.message.message_id,
success=False,
error=str(e)
)
RaiseEvent("llm_internal_response", error_response)
def _call_llm_sync(self, messages: List[Dict[str, str]]) -> str:
"""Sync call to the LLM with retry logic"""
console.log(f"Making LLM call to {self.model_id}")
for attempt in range(self.max_retries):
try:
response = CLIENT.chat.completions.create(
model=self.model_id,
messages=messages,
temperature=self.temperature,
max_tokens=self.max_tokens
)
content = response.choices[0].message.content
console.log(f"LLM call successful, response length: {len(content)}")
return content
except Exception as e:
console.log(f"LLM call attempt {attempt + 1} failed: {e}")
if attempt == self.max_retries - 1:
raise e
# Wait before retry
def _process_queue(self):
"""Main queue processing loop"""
console.log("[bold cyan]LLM Agent queue processor started[/bold cyan]")
while not self._stop_event.is_set():
try:
request = self.request_queue.get(timeout=1.0)
if request:
console.log(f"Got request from queue: {request.message.message_id}")
self._process_llm_request(request)
self.request_queue.task_done()
except Empty:
continue
except Exception as e:
console.log(f"Error in queue processing: {e}", style="bold red")
traceback.print_exc()
console.log("[bold cyan]LLM Agent queue processor stopped[/bold cyan]")
def send_message(
self,
content: str,
role: str = "user",
conversation_id: str = None,
response_event: str = None,
callback: Callable = None,
metadata: Dict = None
) -> str:
"""Send a message to the LLM and get response via events"""
if not self.is_running:
raise RuntimeError("LLM Agent is not running. Call start() first.")
# Create message
message = LLMMessage(
role=role,
content=content,
conversation_id=conversation_id,
metadata=metadata or {}
)
# Create request
request = LLMRequest(
message=message,
response_event=response_event,
callback=callback
)
# Store in pending requests BEFORE adding to queue
with self.pending_requests_lock:
self.pending_requests[message.message_id] = request
console.log(f"Added to pending requests: {message.message_id}")
# Add to queue
try:
self.request_queue.put(request, timeout=5.0)
console.log(f"[bold magenta]Message queued: {message.message_id}, Content: {content[:50]}...[/bold magenta]")
return message.message_id
except queue.Full:
console.log(f"[bold red]Queue full, cannot send message[/bold red]")
with self.pending_requests_lock:
if message.message_id in self.pending_requests:
del self.pending_requests[message.message_id]
raise RuntimeError("LLM Agent queue is full")
async def chat(self, messages: List[Dict[str, str]]) -> str:
"""
Async chat method that sends message via queue and returns response string.
This is the main method you should use.
"""
# Create future for the response
loop = asyncio.get_event_loop()
response_future = loop.create_future()
def chat_callback(response: LLMResponse):
"""Callback when LLM responds - thread-safe"""
console.log(f"[bold yellow]✓ CHAT CALLBACK TRIGGERED![/bold yellow]")
if not response_future.done():
if response.success:
content = response.message.content
console.log(f"Callback received content: {content}...")
# Schedule setting the future result on the main event loop
loop.call_soon_threadsafe(response_future.set_result, content)
else:
console.log(f"Error in response: {response.error}")
error_msg = f"❌ Error: {response.error}"
loop.call_soon_threadsafe(response_future.set_result, error_msg)
else:
console.log(f"[bold red]Future already done, ignoring callback[/bold red]")
console.log(f"Sending message to LLM agent...")
# Extract the actual message content from the messages list
user_message = ""
for msg in messages:
if msg.get("role") == "user":
user_message = msg.get("content", "")
break
if not user_message.strip():
return ""
# Send message with callback using the queue system
try:
message_id = self.send_message(
content=user_message,
conversation_id="default",
callback=chat_callback
)
console.log(f"Message sent with ID: {message_id}, waiting for response...")
# Wait for the response and return it
try:
response = await asyncio.wait_for(response_future, timeout=self.timeout)
console.log(f"[bold green]✓ Chat complete! Response length: {len(response)}[/bold green]")
return response
except asyncio.TimeoutError:
console.log("[bold red]Response timeout[/bold red]")
# Clean up the pending request
with self.pending_requests_lock:
if message_id in self.pending_requests:
del self.pending_requests[message_id]
return "❌ Response timeout - check if LLM server is running"
except Exception as e:
console.log(f"[bold red]Error sending message: {e}[/bold red]")
traceback.print_exc()
return f"❌ Error sending message: {e}"
def start(self):
"""Start the LLM agent"""
if not self.is_running:
self.is_running = True
self._stop_event.clear()
self.processing_thread = Thread(target=self._process_queue, daemon=True)
self.processing_thread.start()
console.log("[bold green]LLM Agent started[/bold green]")
def stop(self):
"""Stop the LLM agent"""
console.log("Stopping LLM Agent...")
self._stop_event.set()
if self.processing_thread and self.processing_thread.is_alive():
self.processing_thread.join(timeout=10)
self.is_running = False
console.log("LLM Agent stopped")
def get_conversation_history(self, conversation_id: str = "default") -> List[LLMMessage]:
"""Get conversation history"""
return self.conversations.get(conversation_id, [])[:]
def clear_conversation(self, conversation_id: str = "default"):
"""Clear conversation history"""
if conversation_id in self.conversations:
del self.conversations[conversation_id]
async def _chat(self, messages: List[Dict[str, str]]) -> str:
return await self._generate(messages)
@staticmethod
async def openai_generate(messages: List[Dict[str, str]], max_tokens: int = 8096, temperature: float = 0.4, model: str = BASEMODEL_ID,tools=None) -> str:
"""Static method for generating responses using OpenAI API"""
try:
resp = await BASE_CLIENT.chat.completions.create(
model=model,
messages=messages,
temperature=temperature,
max_tokens=max_tokens,
tools=tools
)
response_text = resp.choices[0].message.content or ""
return response_text
except Exception as e:
console.log(f"[bold red]Error in openai_generate: {e}[/bold red]")
return f"[LLM_Agent Error - openai_generate: {str(e)}]"
async def _call_(self, messages: List[Dict[str, str]]) -> str:
"""Internal call method using instance client"""
try:
resp = await self.async_client.chat.completions.create(
model=self.model_id,
messages=messages,
temperature=self.temperature,
max_tokens=self.max_tokens
)
response_text = resp.choices[0].message.content or ""
return response_text
except Exception as e:
console.log(f"[bold red]Error in _call_: {e}[/bold red]")
return f"[LLM_Agent Error - _call_: {str(e)}]"
@staticmethod
def CreateClient(base_url: str, api_key: str) -> AsyncOpenAI:
'''Create async OpenAI Client required for multi tasking'''
return AsyncOpenAI(
base_url=base_url,
api_key=api_key
)
@staticmethod
async def fetch_available_models(base_url: str, api_key: str) -> List[str]:
"""Fetches available models from the OpenAI API."""
try:
async_client = AsyncOpenAI(base_url=base_url, api_key=api_key)
models = await async_client.models.list()
model_choices = [model.id for model in models.data]
return model_choices
except Exception as e:
console.log(f"[bold red]LLM_Agent Error fetching models: {e}[/bold red]")
return ["LLM_Agent Error fetching models"]
def get_models(self) -> List[str]:
"""Get available models using instance credentials"""
return asyncio.run(self.fetch_available_models(self.base_url, self.api_key))
def get_queue_size(self) -> int:
"""Get current queue size"""
return self.request_queue.qsize()
def get_pending_requests_count(self) -> int:
"""Get number of pending requests"""
with self.pending_requests_lock:
return len(self.pending_requests)
def get_status(self) :
"""Get agent status information"""
return str({
"is_running": self.is_running,
"queue_size": self.get_queue_size(),
"pending_requests": self.get_pending_requests_count(),
"conversations_count": len(self.conversations),
"model": self.model_id, "BaseURL": self.base_url
})
def direct_chat(self, user_message: str, conversation_id: str = "default") -> str:
"""
Send a message and get a response using direct API call.
"""
try:
# Create message object
message = LLMMessage(role="user", content=user_message, conversation_id=conversation_id)
# Build messages for LLM
messages = self._build_messages_from_conversation(conversation_id, message)
console.log(f"Calling LLM at {self.base_url} with {len(messages)} messages")
# Make the direct API call
response = CLIENT.chat.completions.create(
model=self.model_id,
messages=messages,
temperature=self.temperature,
max_tokens=self.max_tokens
)
response_content = response.choices[0].message.content
console.log(f"[bold green]LLM response received: {response_content[:50]}...[/bold green]")
# Update conversation history
self._add_to_conversation_history(conversation_id, message)
response_message = LLMMessage(role="assistant", content=response_content, conversation_id=conversation_id)
self._add_to_conversation_history(conversation_id, response_message)
return response_content
except Exception as e:
console.log(f"[bold red]Error in chat: {e}[/bold red]")
traceback.print_exc()
return f"❌ Error communicating with LLM: {str(e)}"
# --- TEST Canvas Methods ---
def add_artifact(self, conversation_id: str, artifact_type: str, content: str, title: str = "", metadata: Dict = None):
artifact = CanvasArtifact(
id=str(uuid.uuid4()),
type=artifact_type,
content=content,
title=title,
timestamp=time.time(),
metadata=metadata or {}
)
self.canvas_artifacts[conversation_id].append(artifact)
def get_canvas_artifacts(self, conversation_id: str = "default") -> List[CanvasArtifact]:
return self.canvas_artifacts.get(conversation_id, [])
def get_canvas_summary(self, conversation_id: str = "default") -> List[Dict[str, Any]]:
artifacts = self.get_canvas_artifacts(conversation_id)
return [{"id": a.id, "type": a.type, "title": a.title, "timestamp": a.timestamp} for a in artifacts]
def clear_canvas(self, conversation_id: str = "default"):
if conversation_id in self.canvas_artifacts:
self.canvas_artifacts[conversation_id] = []
def clear_conversation(self, conversation_id: str = "default"):
if conversation_id in self.conversations:
del self.conversations[conversation_id]
def get_latest_code_artifact(self, conversation_id: str) -> Optional[str]:
"""Get the most recent code artifact content"""
if conversation_id not in self.canvas_artifacts:
return None
for artifact in reversed(self.canvas_artifacts[conversation_id]):
if artifact.type == "code":
return artifact.content
return None
def get_canvas_context(self, conversation_id: str) -> str:
"""Get formatted canvas context for LLM prompts"""
if conversation_id not in self.canvas_artifacts or not self.canvas_artifacts[conversation_id]:
return ""
context_lines = ["\n=== COLLABORATIVE CANVAS ARTIFACTS ==="]
for artifact in self.canvas_artifacts[conversation_id][-10:]: # Last 10 artifacts
context_lines.append(f"\n--- {artifact.title} [{artifact.type.upper()}] ---")
preview = artifact.content[:500] + "..." if len(artifact.content) > 500 else artifact.content
context_lines.append(preview)
return "\n".join(context_lines) + "\n=================================\n"
def get_artifact_by_id(self, conversation_id: str, artifact_id: str) -> Optional[CanvasArtifact]:
"""Get specific artifact by ID"""
if conversation_id not in self.canvas_artifacts:
return None
for artifact in self.canvas_artifacts[conversation_id]:
if artifact.id == artifact_id:
return artifact
return None
def _extract_artifacts_to_canvas(self, response: str, conversation_id: str):
"""Automatically extract code blocks and add to canvas"""
# Find all code blocks with optional language specification
code_blocks = re.findall(r'```(?:(\w+)\n)?(.*?)```', response, re.DOTALL)
for i, (lang, code_block) in enumerate(code_blocks):
if len(code_block.strip()) > 10: # Only add substantial code blocks
self.add_artifact_to_canvas(
conversation_id,
code_block.strip(),
"code",
f"code_snippet_{lang or 'unknown'}_{len(self.canvas_artifacts.get(conversation_id, [])) + 1}"
)
async def chat_with_canvas(self, message: str, conversation_id: str, include_canvas: bool = False):
"""Chat method that can optionally include canvas context."""
messages = [{"role": "user", "content": message}]
if include_canvas:
artifacts = self.get_canvas_summary(conversation_id)
if artifacts:
canvas_context = "Current Canvas Context:\\n" + "\\n".join([
f"- [{art['type'].upper()}] {art['title'] or 'Untitled'}: {art['content_preview']}"
for art in artifacts
])
messages.insert(0, {"role": "system", "content": canvas_context})
return await self.chat(messages)
def respond(
message,
history: list[dict[str, str]],
system_message,
max_tokens,
temperature,
top_p,
hf_token: gr.OAuthToken,
):
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
messages = [{"role": "system", "content": system_message}]
messages.extend(history)
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
choices = message.choices
token = ""
if len(choices) and choices[0].delta.content:
token = choices[0].delta.content
response += token
yield response
custom_css = """
.gradio-container {
background-color: rgba(243, 48, 4, 0.85);
background-image: url("https://huggingface.co/LeroyDyer/ImageFiles/resolve/main/LCARS_PANEL.png");
background-size: cover;
background-position: center;
background-repeat: no-repeat;
border-radius: 20px;
}
.agent-card { padding: 10px; margin: 5px 0; border-radius: 8px; background: #f0f8ff; }
.agent-card.active { background: #e6f2ff; border-left: 3px solid #3399FF; }
.status-indicator { display: inline-block; width: 10px; height: 10px; border-radius: 50%; margin-right: 5px; }
.online { background-color: #4CAF50; }
.offline { background-color: #F44336; }
.console-log { font-family: monospace; font-size: 0.9em; background: #1e1e1e; color: #00ff00; padding: 10px; border-radius: 5px; height: 500px; overflow-y: auto; }
.log-entry { margin: 2px 0; }
.log-public { color: #00ff00; }
.log-direct { color: #ffaa00; }
.log-system { color: #00aaff; }
.message-controls { background: #f5f5f5; padding: 10px; border-radius: 5px; margin-bottom: 10px; }
.console-log {
font-family: monospace;
font-size: 0.85em;
background: #1e1e1e;
color: #00ff00;
padding: 10px;
border-radius: 5px;
height: 600px;
overflow-y: auto;
word-wrap: break-word;
white-space: pre-wrap;
}
.log-entry {
margin: 4px 0;
padding: 2px 4px;
border-left: 2px solid #333;
}
.log-public {
color: #00ff00;
border-left-color: #00aa00;
}
.log-direct {
color: #ffaa00;
border-left-color: #ff8800;
}
.log-system {
color: #00aaff;
border-left-color: #0088ff;
}
.lcars-container {
background: #000d1a;
color: #7EC8E3;
font-family: 'Courier New', monospace;
padding: 20px;
border-radius: 0;
}
.lcars-title {
color: #7EC8E3;
text-align: center;
font-size: 2.2em;
text-shadow: 0 0 10px #7EC8E3, 0 0 20px rgba(126, 200, 227, 0.5);
margin-bottom: 10px;
letter-spacing: 2px;
}
.lcars-subtitle {
color: #aaa;
text-align: center;
font-style: italic;
margin-bottom: 30px;
}
/* Glowing Input Boxes */
.gr-box input, .gr-box textarea {
background: #001122 !important;
color: #7EC8E3 !important;
border: 1px solid #7EC8E3 !important;
box-shadow: 0 0 8px rgba(126, 200, 227, 0.3) !important;
font-family: 'Courier New', monospace !important;
}
.gr-button {
background: linear-gradient(90deg, #003366, #0055aa) !important;
color: #7EC8E3 !important;
border: 1px solid #7EC8E3 !important;
box-shadow: 0 0 10px rgba(126, 200, 227, 0.4) !important;
font-family: 'Courier New', monospace !important;
font-weight: bold !important;
letter-spacing: 1px;
transition: all 0.3s ease;
}
.gr-button:hover {
background: linear-gradient(90deg, #004488, #0077cc) !important;
box-shadow: 0 0 15px rgba(126, 200, 227, 0.6) !important;
transform: scale(1.05);
}
/* Output Panels */
.lcars-output-panel {
border: 2px solid #7EC8E3;
border-radius: 12px;
padding: 15px;
background: #00141a;
box-shadow: 0 0 15px rgba(126, 200, 227, 0.2);
margin-top: 10px;
}
.lcars-error {
color: #ff6b6b;
font-weight: bold;
text-shadow: 0 0 5px rgba(255,107,107,0.5);
padding: 20px;
text-align: center;
}
.lcars-log {
max-height: 400px;
overflow-y: auto;
background: #001018;
border: 1px solid #7EC8E3;
border-radius: 8px;
padding: 10px;
}
.lcars-step {
margin-bottom: 15px;
padding: 10px;
background: #000c14;
border-left: 3px solid #7EC8E3;
}
.lcars-step h4 {
margin: 0 0 8px 0;
color: #7EC8E3;
}
.lcars-step pre {
white-space: pre-wrap;
background: #00080c;
padding: 10px;
border-radius: 5px;
color: #ccc;
font-size: 0.9em;
margin: 10px 0 0 0;
}
code {
background: #000f1f;
color: #7EC8E3;
padding: 2px 6px;
border-radius: 4px;
font-family: 'Courier New';
}
@keyframes glow-pulse {
0% { opacity: 0.8; }
50% { opacity: 1; }
100% { opacity: 0.8; }
}
iframe {
animation: glow-pulse 2.5s infinite ease-in-out;
}
.gr-form { background: transparent !important; }
/* =========================
LCARS47 Bridge Theme
Seamless Drop-In
========================= */
:root {
/* Core LCARS Palette */
--lcars-bg: #000814;
--lcars-panel: #111827;
--lcars-red: #CC6666;
--lcars-gold: #FFCC66;
--lcars-cyan: #66CCCC;
--lcars-text: #FFFFFF;
--lcars-muted: #AAAAAA;
--lcars-orange: #FF9966;
--lcars-purple: #663399;
--lcars-rose: #FF6F91;
--lcars-gold: #FFC766;
--lcars-peach: #FFCC99;
--lcars-blue: #9999FF;
--lcars-lavender: #CCCCFF;
--lcars-tan: #FFCC99;
--lcars-rust: #CC6666;
--lcars-gold: #FFCC66;
--lcars-bg: #F5F0FF;
--lcars-panel: #E8E0F5;
--lcars-text: #2D2D5F;
--lcars-text-light: #5F5F8F;
--lcars-border: #9999CC;
--lcars-accent: #6666CC;
--lcars-dark: #111317;
/* Shared component vars */
--radius-large: 24px;
--radius-full: 50%;
--pulse-speed: 2s;
--font-stack: "Arial Narrow", "Helvetica Neue", sans-serif;
}
.lcars-thinking {{
background: linear-gradient(135deg, {self.colors['panel']}, #001122) !important;
border-left: 4px solid {self.colors['info']} !important;
color: {self.colors['text']} !important;
padding: 15px !important;
border-radius: 0px 15px 15px 0px !important;
}}
.gradio-container {{background-color: rgba(243, 48, 4, 0.85);
background: linear-gradient(135deg, {self.colors['background']}, #001122) !important;
color: {self.colors['text']} !important;
font-family: 'Courier New', monospace !important;
background-image: url("https://huggingface.co/LeroyDyer/ImageFiles/resolve/main/LCARS_PANEL.png");
background-size: cover;
background-position: center;
background-repeat: no-repeat;
border-radius: 20px;
}}
#left-panel {
flex: 0 0 250px !important; /* fixed width */
max-width: 350px !important;
padding: 20px !important;
}
@keyframes pulse {
0% { box-shadow: 0 0 5px var(--lcars-orange); }
50% { box-shadow: 0 0 20px var(--lcars-orange); }
100% { box-shadow: 0 0 5px var(--lcars-orange); }
}
.pulse-animation {
animation: pulse 2s infinite;
}
/* Panels */
.lcars-panel {
background-color: var(--lcars-panel);
border-radius: var(--radius-large);
padding: 1rem;
margin: 0.5rem;
box-shadow: 0 0 8px rgba(0,0,0,0.6);
}
/* Inputs & Outputs */
.lcars-input {{
background: {self.colors['panel']} !important;
color: {self.colors['text']} !important;
border: 2px solid {self.colors['primary']} !important;
border-radius: 0px 10px 10px 0px !important;
padding: 10px !important;
}}
.lcars-output {{
background: linear-gradient(135deg, {self.colors['background']}, {self.colors['panel']}) !important;
color: {self.colors['text']} !important;
border: 2px solid {self.colors['success']} !important;
border-radius: 0px 15px 15px 0px !important;
padding: 15px !important;
font-family: 'Courier New', monospace !important;
}}
/* Responsive */
@media (max-width: 768px) {
.gradio-container { padding: 10px; }
#lcars_logo { height: 150px !important; width: 150px !important; }
}
/* Code & Thinking blocks */
.lcars-code {{
background: {self.colors['background']} !important;
color: {self.colors['success']} !important;
border: 1px solid {self.colors['success']} !important;
border-radius: 5px !important;
font-family: 'Courier New', monospace !important;
}}
.lcars-thinking {{
background: linear-gradient(135deg, {self.colors['panel']}, #001122) !important;
border-left: 4px solid {self.colors['info']} !important;
color: {self.colors['text']} !important;
padding: 15px !important;
border-radius: 0px 15px 15px 0px !important;
}}
.lcars-artifact {{
background: {self.colors['panel']} !important;
border: 2px solid {self.colors['border']} !important;
color: {self.colors['text']} !important;
border-radius: 0px 15px 15px 0px !important;
padding: 15px !important;
margin: 10px 0 !important;
}}
/* Headers */
.lcars-header {
background: var(--lcars-red);
color: var(--lcars-text);
border-radius: var(--radius-large);
padding: 0.75rem 1.5rem;
text-transform: uppercase;
font-size: 1.25rem;
}
/* Chatbox */
.chatbox > div {
background: var(--lcars-dark) !important;
border-radius: 18px !important;
border: 2px solid var(--lcars-purple) !important;
}
/* =========================
Buttons / Tabs / Chips
========================= */
button, .lcars-tab, .lcars-chip {
background: var(--lcars-gold);
border: none;
border-radius: var(--radius-large);
padding: 0.5rem 1rem;
margin: 0.25rem;
color: var(--lcars-bg);
font-weight: bold;
font-size: 1rem;
transition: all 0.3s ease-in-out;
cursor: pointer;
}
button:hover, .lcars-tab:hover, .lcars-chip:hover {
background: var(--lcars-orange);
color: var(--lcars-text);
}
/* Circular buttons */
button.round, .lcars-chip.round {
border-radius: var(--radius-full);
padding: 0.75rem;
width: 3rem;
height: 3rem;
text-align: center;
}
/* =========================
Containers (Code, JSON, Chat, Artifacts)
========================= */
.json-container, .code-container, .chat-container, .artifact-container {
border-radius: var(--radius-large);
padding: 1rem;
margin: 0.5rem 0;
background: var(--lcars-panel);
color: var(--lcars-text);
font-family: monospace;
font-size: 0.9rem;
line-height: 1.4;
white-space: pre-wrap;
overflow-x: auto;
}
/* =========================
Artifact / Chat / Code Borders
========================= */
.artifact-container {
border: 3px solid var(--lcars-gold);
animation: pulse-yellow var(--pulse-speed) infinite;
}
.chat-container {
border: 3px solid var(--lcars-red);
animation: pulse-red var(--pulse-speed) infinite;
}
.code-container {
border: 3px solid var(--lcars-purple);
animation: pulse-orange var(--pulse-speed) infinite;
}
/* =========================
Animations
========================= */
@keyframes pulse-red {
0%, 100% { box-shadow: 0 0 5px var(--lcars-red); }
50% { box-shadow: 0 0 20px var(--lcars-red); }
}
@keyframes pulse-yellow {
0%, 100% { box-shadow: 0 0 5px var(--lcars-gold); }
50% { box-shadow: 0 0 20px var(--lcars-gold); }
}
@keyframes pulse-orange {
0%, 100% { box-shadow: 0 0 5px var(--lcars-orange); }
50% { box-shadow: 0 0 20px var(--lcars-orange); }
}
/* Thought styling */
.thought {
opacity: 0.8;
font-family: "Courier New", monospace;
border: 1px rgb(229, 128, 12) solid;
padding: 10px;
border-radius: 5px;
display: none;
box-shadow: 0 0 20px rgba(255, 153, 0, 0.932);
}
.thought-prompt {
opacity: 0.8;
font-family: "Courier New", monospace;
}
/* =========================
Metadata & Thought Blocks
========================= */
.metadata-display, .thought-block {
background: var(--lcars-blue);
border-radius: var(--radius-large);
padding: 0.75rem;
margin: 0.5rem 0;
color: var(--lcars-bg);
font-weight: bold;
}
.metadata-display {
background: var(--lcars-panel);
border-left: 4px solid var(--lcars-blue);
box-shadow: 0 0 20px rgba(255, 153, 0, 0.932);
padding: 10px;
border-radius: 5px;
overflow-y: auto;
max-height: 300px;
}
.metadata-display .json-container {
font-family: monospace;
font-size: 0.9em;
background: #6b50111a;
}
.primary {
background: linear-gradient(45deg, var(--lcars-orange), #ffaa33) !important;
color: hwb(90 7% 5% / 0.102);
font-family: "Courier New", monospace;
border: 1px rgb(229, 128, 12) solid;
}
.secondary {
background: linear-gradient(45deg, var(--lcars-blue), #33aacc) !important;
color: #6b50111a;
font-family: "Courier New", monospace;
border: 1px rgb(229, 128, 12) solid;
box-shadow: 0 0 20px rgba(255, 153, 0, 0.932);
}
::-webkit-scrollbar-thumb:hover {
background-color: var(--lcars-gold);
}
#lcars_logo {
border-radius: 15px;
border: 2px solid var(--lcars-orange);
box-shadow: 0 0 20px rgba(255, 153, 0, 0.932);
}
.lcars-tab {{
background: {self.colors['panel']} !important;
color: {self.colors['text']} !important;
border: 2px solid {self.colors['primary']} !important;
border-radius: 0px 10px 0px 0px !important;
}}
.lcars-tab.selected {{
background: {self.colors['primary']} !important;
color: {self.colors['background']} !important;
}}
.lcars-panel.lcars-empty {
text-align: center;
font-style: italic;
color: var(--lcars-text-light);
}
.lcars-panel.lcars-error {
background: #FFE5E5;
border-color: var(--lcars-rust);
color: #CC0000;
}
/* Input fields */
.lcars-input input,
.lcars-input textarea {
background: white !important;
border: 2px solid var(--lcars-border) !important;
border-radius: 8px !important;
color: var(--lcars-text) !important;
padding: 10px !important;
font-size: 14px !important;
}
.lcars-input input:focus,
.lcars-input textarea:focus {
border-color: var(--lcars-accent) !important;
outline: none !important;
box-shadow: 0 0 8px rgba(102, 102, 204, 0.3) !important;
}
/* Dropdowns and selects */
.lcars-dropdown select,
.lcars-dropdown input {
background: white !important;
border: 2px solid var(--lcars-border) !important;
border-radius: 8px !important;
color: var(--lcars-text) !important;
padding: 8px !important;
}
/* Checkboxes */
.lcars-checkbox label {
background: var(--lcars-panel) !important;
border: 2px solid var(--lcars-border) !important;
border-radius: 8px !important;
padding: 8px 12px !important;
margin: 4px !important;
transition: all 0.2s ease !important;
}
.lcars-checkbox label:hover {
background: var(--lcars-lavender) !important;
border-color: var(--lcars-accent) !important;
}
/* Radio buttons */
.lcars-radio label {
background: var(--lcars-panel) !important;
border: 2px solid var(--lcars-border) !important;
border-radius: 20px !important;
padding: 8px 16px !important;
margin: 4px !important;
}
/* Display fields */
.lcars-display input {
background: var(--lcars-panel) !important;
border: 2px solid var(--lcars-border) !important;
border-radius: 8px !important;
color: var(--lcars-text) !important;
font-family: 'Courier New', monospace !important;
padding: 10px !important;
}
/* Accordions */
.lcars-accordion {
background: var(--lcars-panel) !important;
border: 2px solid var(--lcars-border) !important;
border-radius: 12px !important;
margin: 8px 0 !important;
}
.lcars-accordion summary {
background: linear-gradient(135deg, var(--lcars-orange), var(--lcars-peach)) !important;
color: var(--lcars-text) !important;
font-weight: bold !important;
padding: 12px !important;
border-radius: 10px !important;
cursor: pointer !important;
}
/* Participant Cards & Collapsible Layout */
.lcars-participants-container {
display: flex;
flex-direction: column;
gap: 15px;
width: 100%;
}
/* Base Card Styles */
.lcars-collapsible-card {
border: 1px solid #444;
border-radius: 8px;
background: #1a1a1a;
color: #fff;
overflow: hidden;
transition: all 0.3s ease;
}
.lcars-collapsible-card.collapsed .lcars-participant-expanded {
display: none;
}
.lcars-collapsible-card.expanded .lcars-participant-collapsed {
display: none;
}
.lcars-collapsible-card.expanded .lcars-collapse-icon {
transform: rotate(90deg);
}
/* Card Headers */
.lcars-participant-header {
background: #3366cc;
color: white;
padding: 12px 15px;
display: flex;
justify-content: space-between;
align-items: center;
cursor: pointer;
border-bottom: 2px solid #ffcc00;
transition: background 0.2s ease;
}
.lcars-participant-header:hover {
background: #2a55a8;
}
.lcars-participant-name {
font-weight: bold;
font-size: 1.1em;
}
.lcars-collapse-icon {
transition: transform 0.3s ease;
font-size: 0.8em;
}
/* Badges */
.lcars-badge-manager {
background: #ffcc00;
color: #000;
padding: 4px 8px;
border-radius: 12px;
font-size: 0.8em;
font-weight: bold;
letter-spacing: 1px;
box-shadow: 0 2px 4px rgba(255, 215, 0, 0.3);
}
.lcars-badge-agent {
background: #00cc66;
color: #000;
padding: 4px 8px;
border-radius: 12px;
font-size: 0.8em;
font-weight: bold;
letter-spacing: 1px;
box-shadow: 0 2px 4px rgba(0, 204, 102, 0.3);
}
.lcars-badge-human {
background: #9966cc;
color: #fff;
padding: 4px 8px;
border-radius: 12px;
font-size: 0.8em;
font-weight: bold;
letter-spacing: 1px;
box-shadow: 0 2px 4px rgba(153, 102, 255, 0.3);
}
/* Card Content Sections */
.lcars-participant-collapsed,
.lcars-participant-expanded {
padding: 15px;
}
.lcars-participant-preview {
display: flex;
flex-direction: column;
gap: 8px;
}
.lcars-info-section {
margin-bottom: 20px;
padding-bottom: 15px;
border-bottom: 1px solid #333;
}
.lcars-info-section:last-child {
border-bottom: none;
margin-bottom: 0;
}
.lcars-section-title {
color: #ffcc00;
font-weight: bold;
font-size: 0.9em;
text-transform: uppercase;
letter-spacing: 1px;
margin-bottom: 10px;
border-bottom: 1px solid #444;
padding-bottom: 5px;
}
/* Info Rows */
.lcars-info-row {
display: flex;
margin-bottom: 8px;
line-height: 1.4;
color: var(--lcars-text-light);
}
.lcars-info-row.full-width {
flex-direction: column;
}
.lcars-label {
color: #ffcc00;
font-weight: bold;
min-width: 120px;
margin-right: 10px;
font-size: 0.9em;
}
/* Lists */
.lcars-goals-list li {
margin-bottom: 5px;
line-height: 1.4;
color: #e0e0e0;
}
/* Template Styling */
.lcars-template-container {
background: rgba(255, 255, 255, 0.05);
border: 1px solid #444;
border-radius: 4px;
padding: 10px;
max-height: 200px;
overflow-y: auto;
}
.lcars-template-preview {
color: #e0e0e0;
font-family: monospace;
font-size: 0.85em;
line-height: 1.4;
white-space: pre-wrap;
}
.lcars-template-truncated {
color: #ffcc00;
font-size: 0.8em;
font-style: italic;
margin-top: 8px;
}
.lcars-no-template {
color: #888;
font-style: italic;
}
/* More Skills Indicator */
.lcars-more-skills {
color: #ffcc00;
font-size: 0.8em;
font-style: italic;
margin-top: 5px;
display: block;
}
/* Agent Details Panel */
.lcars-agent-details {
background: white;
border: 3px solid var(--lcars-border);
border-radius: 12px;
overflow: hidden;
box-shadow: 0 4px 12px rgba(102, 102, 204, 0.2);
}
.lcars-agent-header {
background: linear-gradient(135deg, var(--lcars-blue), var(--lcars-lavender));
padding: 16px;
display: flex;
justify-content: space-between;
align-items: center;
}
.lcars-agent-name {
font-size: 20px;
font-weight: bold;
color: white;
text-transform: uppercase;
letter-spacing: 2px;
}
.lcars-status-connected {
background: #66CC66;
color: white;
padding: 6px 14px;
border-radius: 16px;
font-size: 12px;
font-weight: bold;
}
.lcars-status-available {
background: var(--lcars-orange);
color: white;
padding: 6px 14px;
border-radius: 16px;
font-size: 12px;
font-weight: bold;
}
.lcars-agent-body {
padding: 18px;
}
.lcars-detail-row {
margin: 12px 0;
display: flex;
gap: 10px;
}
.lcars-detail-label {
font-weight: bold;
color: var(--lcars-accent);
min-width: 120px;
text-transform: uppercase;
font-size: 12px;
letter-spacing: 1px;
}
.lcars-detail-value {
color: var(--lcars-text);
flex: 1;
}
.lcars-model-badge {
background: var(--lcars-panel);
color: var(--lcars-accent);
padding: 4px 10px;
border-radius: 6px;
font-family: 'Courier New', monospace;
font-size: 12px;
}
.lcars-detail-section {
margin: 16px 0;
padding: 12px;
background: var(--lcars-panel);
border-radius: 8px;
}
.lcars-skills-list {
line-height: 2;
}
.lcars-skill-item {
color: var(--lcars-text-light);
font-size: 13px;
margin-left: 8px;
}
.lcars-expertise {
color: var(--lcars-text-light);
font-size: 13px;
line-height: 1.8;
}
/* Pattern Details */
.lcars-pattern-details {
border: 1px solid #444;
border-radius: 8px;
margin: 10px 0;
background: #1a1a1a;
color: #fff;
}
.lcars-pattern-header {
background: #3366cc;
color: white;
padding: 12px 15px;
font-weight: bold;
font-size: 1.1em;
text-align: center;
border-bottom: 2px solid #ffcc00;
}
.lcars-pattern-body {
padding: 15px;
}
.lcars-pattern-section {
margin-bottom: 20px;
display: block;
}
.lcars-pattern-section:last-child {
margin-bottom: 0;
}
.lcars-pattern-label {
font-weight: bold;
color: #ffcc00;
margin-bottom: 5px;
font-size: 0.9em;
text-transform: uppercase;
letter-spacing: 1px;
}
.lcars-pattern-text {
color: #fa0404;
line-height: 1.5;
}
/* Log display */
.lcars-log-panel {
background: #00008734;
color: #050505;
font-family: 'Courier New', monospace;
font-size: 16px;
border-radius: 8px;
padding: 12px;
max-height: 500px;
overflow-y: auto;
box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.3);
}
.lcars-log-panel.lcars-empty {
color: #999;
text-align: center;
font-style: italic;
}
.lcars-log-entries {
display: flex;
flex-direction: column;
gap: 4px;
}
.lcars-log-entry {
padding: 6px 10px;
border-left: 3px solid transparent;
border-radius: 3px;
transition: all 0.2s ease;
}
.lcars-log-entry:hover {
background: rgba(255, 255, 255, 0.05);
}
.lcars-log-info {
border-left-color: #5c635cda;
color: #1636e7;
}
.lcars-log-error {
border-left-color: #202120;
color: #1636e7;
}
.lcars-log-level {
font-weight: bold;
margin-right: 8px;
}
/* Chatbot styling */
.lcars-chatbot {
border: 3px solid var(--lcars-border) !important;
border-radius: 12px !important;
background: white !important;
}
.gradio-container {
background-color: rgba(243, 48, 4, 0.85);
background-image: url("https://huggingface.co/LeroyDyer/ImageFiles/resolve/main/LCARS_PANEL.png");
background-size: cover;
background-position: center;
background-repeat: no-repeat;
border-radius: 20px;
}
.tab-nav button {
background: var(--lcars-panel) !important;
border: 2px solid var(--lcars-border) !important;
color: var(--lcars-text) !important;
border-radius: 8px 8px 0 0 !important;
margin-right: 4px !important;
font-weight: bold !important;
}
.tab-nav button.selected {
background: linear-gradient(135deg, var(--lcars-orange), var(--lcars-peach)) !important;
color: var(--lcars-text) !important;
border-bottom: none !important;
}
/* Ensure vertical stacking of participants */
.lcars-participants-container {
display: flex !important;
flex-direction: column !important;
gap: 16px !important;
width: 100% !important;
max-width: 100% !important;
margin: 0 auto !important;
align-items: stretch !important; /* Ensures full width alignment */
}
/* Make sure each participant card respects container flow */
.lcars-participant-card-manager,
.lcars-participant-card-agent,
.lcars-participant-card-human {
display: flex !important;
flex-direction: column !important;
break-inside: avoid !important; /* Prevents awkward splits in print/PDF */
position: relative !important;
width: 100% !important;
box-sizing: border-box !important;
background: white !important;
color: #2D2D5F !important;
}
.lcars-content {
background: rgba(0, 0, 0, 0.95) !important;
border: 2px solid #ff9900 !important;
color: #ffffff !important;
font-family: 'Times New Roman', serif !important;
padding: 20px !important;
height: 600px !important;
overflow-y: auto !important;
}
.gr-button:hover {
background: linear-gradient(45deg, #ffcc00, #ff9900) !important;
box-shadow: 0 0 15px rgba(255, 153, 0, 0.8) !important;
}
.block {
background: rgba(0, 0, 0, 0.8) !important;
border: 2px solid #ff9900 !important;
border-radius: 0px !important;
}
/* Scrollbar */
::-webkit-scrollbar {{ width: 12px; }}
::-webkit-scrollbar-track {{ background: {self.colors['background']}; }}
::-webkit-scrollbar-thumb {{
background: {self.colors['primary']};
border-radius: 0px 10px 10px 0px;
}}
::-webkit-scrollbar-thumb:hover {{ background: {self.colors['accent']}; }}
.lcars-button,
button[variant="primary"] {
background: linear-gradient(135deg, var(--lcars-orange), var(--lcars-peach)) !important;
color: var(--lcars-text) !important;
}
.lcars-button-add {
background: linear-gradient(135deg, var(--lcars-blue), var(--lcars-lavender)) !important;
color: white !important;
}
.lcars-button-send,
.lcars-button-task {
background: linear-gradient(135deg, var(--lcars-purple), var(--lcars-lavender)) !important;
color: white !important;
}
.lcars-button-remove {
background: linear-gradient(135deg, var(--lcars-rust), #FF9999) !important;
color: white !important;
}
.lcars-button-secondary,
.lcars-button-create {
background: linear-gradient(135deg, var(--lcars-gold), var(--lcars-tan)) !important;
color: var(--lcars-text) !important;
}
.gradio-container {{background-color: rgba(243, 48, 4, 0.85);
background: linear-gradient(135deg, {self.colors['background']}, #001122) !important;
color: {self.colors['text']} !important;
font-family: 'Courier New', monospace !important;
background-image: url("https://huggingface.co/LeroyDyer/ImageFiles/resolve/main/LCARS_PANEL.png");
background-size: cover;
background-position: center;
background-repeat: no-repeat;
border-radius: 20px;
}}
"""
# Session management
SESSION_FILE = "lcars_session.pkl"
ARTIFACTS_FILE = "lcars_artifacts.json"
# Initialize the agent
agent = LLMAgent(
model_id=BASEMODEL_ID,
system_prompt="You are L.C.A.R.S - Local Computer Advanced Reasoning System, an advanced AI assistant with capabilities for code generation, analysis, and collaborative problem solving.",
temperature=0.7,
max_tokens=5000
)
@dataclass
class ParsedResponse:
"""Fixed ParsedResponse data model"""
def __init__(self, thinking="", main_content="", code_snippets=None, raw_reasoning="", raw_content=""):
self.thinking = thinking
self.main_content = main_content
self.code_snippets = code_snippets or []
self.raw_reasoning = raw_reasoning
self.raw_content = raw_content
def execute_python_code(code):
"""Execute Python code safely and return output"""
try:
# Create a temporary file
temp_file = "temp_execution.py"
with open(temp_file, 'w', encoding='utf-8') as f:
f.write(code)
# Execute the code
result = subprocess.run(
[sys.executable, temp_file],
capture_output=True,
text=True,
timeout=30 # 30 second timeout
)
# Clean up
if os.path.exists(temp_file):
os.remove(temp_file)
output = ""
if result.stdout:
output += f"**Output:**\n{result.stdout}\n"
if result.stderr:
output += f"**Errors:**\n{result.stderr}\n"
if result.returncode != 0:
output += f"**Return code:** {result.returncode}\n"
return output.strip() if output else "Code executed (no output)"
except subprocess.TimeoutExpired:
return "❌ Execution timed out (30 seconds)"
except Exception as e:
return f"❌ Execution error: {str(e)}"
def execute_code_artifact(artifact_id, current_code):
"""Execute a specific code artifact"""
try:
artifacts = agent.get_canvas_artifacts(agent.current_conversation)
if not artifacts:
return "No artifacts available", current_code
try:
artifact_idx = int(artifact_id)
if 0 <= artifact_idx < len(artifacts):
artifact = artifacts[artifact_idx]
if artifact.type == "code":
# Execute the code
execution_result = execute_python_code(artifact.content)
display_text = f"## 🚀 Executing Artifact #{artifact_idx}\n\n**Title:** {artifact.title}\n\n**Execution Result:**\n{execution_result}"
return display_text, artifact.content
else:
return f"❌ Artifact {artifact_idx} is not code (type: {artifact.type})", current_code
else:
return f"❌ Invalid artifact ID. Available: 0-{len(artifacts)-1}", current_code
except ValueError:
return "❌ Please enter a valid numeric artifact ID", current_code
except Exception as e:
return f"❌ Error: {str(e)}", current_code
def execute_current_code(code):
"""Execute the code currently in the editor"""
try:
if not code.strip():
return "❌ No code to execute", code
execution_result = execute_python_code(code)
display_text = f"## 🚀 Code Execution Result\n\n{execution_result}"
return display_text, code
except Exception as e:
return f"❌ Execution error: {str(e)}", code
def save_session():
"""Save current session to disk"""
try:
session_data = {
'conversations': agent.conversations,
'current_conversation': agent.current_conversation,
'canvas_artifacts': dict(agent.canvas_artifacts),
'history': getattr(agent, 'display_history', [])
}
with open(SESSION_FILE, 'wb') as f:
pickle.dump(session_data, f)
print(f"💾 Session saved to {SESSION_FILE}")
return True
except Exception as e:
print(f"❌ Error saving session: {e}")
return False
def load_session():
"""Load session from disk"""
try:
if os.path.exists(SESSION_FILE):
with open(SESSION_FILE, 'rb') as f:
session_data = pickle.load(f)
agent.conversations = session_data.get('conversations', {})
agent.current_conversation = session_data.get('current_conversation', 'default')
agent.canvas_artifacts = defaultdict(list, session_data.get('canvas_artifacts', {}))
agent.display_history = session_data.get('history', [])
print(f"📂 Session loaded from {SESSION_FILE}")
return True
else:
print("📂 No existing session found, starting fresh")
return False
except Exception as e:
print(f"❌ Error loading session: {e}")
return False
def save_artifacts():
"""Save artifacts to JSON file"""
try:
artifacts_data = []
for conv_id, artifacts in agent.canvas_artifacts.items():
for artifact in artifacts:
artifacts_data.append({
'conversation_id': conv_id,
'id': artifact.id,
'type': artifact.type,
'content': artifact.content,
'title': artifact.title,
'timestamp': artifact.timestamp,
'metadata': artifact.metadata
})
with open(ARTIFACTS_FILE, 'w', encoding='utf-8') as f:
json.dump(artifacts_data, f, indent=2, ensure_ascii=False)
print(f"💾 Artifacts saved to {ARTIFACTS_FILE}")
return True
except Exception as e:
print(f"❌ Error saving artifacts: {e}")
return False
def load_artifacts():
"""Load artifacts from JSON file"""
try:
if os.path.exists(ARTIFACTS_FILE):
with open(ARTIFACTS_FILE, 'r', encoding='utf-8') as f:
artifacts_data = json.load(f)
agent.canvas_artifacts.clear()
for artifact_data in artifacts_data:
conv_id = artifact_data['conversation_id']
artifact = CanvasArtifact(
id=artifact_data['id'],
type=artifact_data['type'],
content=artifact_data['content'],
title=artifact_data['title'],
timestamp=artifact_data['timestamp'],
metadata=artifact_data.get('metadata', {})
)
agent.canvas_artifacts[conv_id].append(artifact)
print(f"📂 Artifacts loaded from {ARTIFACTS_FILE}")
return True
else:
print("📂 No existing artifacts found")
return False
except Exception as e:
print(f"❌ Error loading artifacts: {e}")
return False
def parse_llm_response(response_text):
"""Parse LLM response to extract thinking, content, and code snippets"""
parsed = ParsedResponse()
parsed.raw_content = response_text
# Patterns for different response components
thinking_patterns = [
r'🧠[^\n]*?(.*?)(?=🤖|💻|🚀|$)', # 🧠 thinking section
r'Thinking:[^\n]*?(.*?)(?=Response:|Answer:|$)', # Thinking: section
r'Reasoning:[^\n]*?(.*?)(?=Response:|Answer:|$)', # Reasoning: section
]
# Try to extract thinking/reasoning
thinking_content = ""
for pattern in thinking_patterns:
thinking_match = re.search(pattern, response_text, re.IGNORECASE | re.DOTALL)
if thinking_match:
thinking_content = thinking_match.group(1).strip()
break
if thinking_content:
parsed.thinking = thinking_content
parsed.raw_reasoning = thinking_content
# Remove thinking from main content
main_content = re.sub(pattern, '', response_text, flags=re.IGNORECASE | re.DOTALL).strip()
else:
main_content = response_text
# Extract code snippets
code_blocks = re.findall(r'```(?:(\w+)\n)?(.*?)```', main_content, re.DOTALL)
parsed.code_snippets = []
for lang, code in code_blocks:
if code.strip():
parsed.code_snippets.append({
'language': lang or 'text',
'code': code.strip(),
'description': f"Code snippet ({lang or 'unknown'})"
})
# Remove code blocks from main content for cleaner display
clean_content = re.sub(r'```.*?```', '', main_content, flags=re.DOTALL)
clean_content = re.sub(r'`.*?`', '', clean_content)
parsed.main_content = clean_content.strip()
return parsed
def extract_artifacts_from_response(parsed_response, conversation_id):
"""Extract and save artifacts from parsed response"""
artifacts_created = []
# Save code snippets as artifacts
for i, snippet in enumerate(parsed_response.code_snippets):
agent.add_artifact(
conversation_id=conversation_id,
artifact_type="code",
content=snippet['code'],
title=f"code_snippet_{snippet['language']}_{i}",
metadata={
"language": snippet['language'],
"description": snippet.get('description', ''),
"source": "llm_response"
}
)
artifacts_created.append(f"code_snippet_{i}")
# Save thinking as a text artifact if substantial
if len(parsed_response.thinking) > 50:
agent.add_artifact(
conversation_id=conversation_id,
artifact_type="text",
content=parsed_response.thinking,
title="reasoning_process",
metadata={"type": "reasoning", "source": "llm_response"}
)
artifacts_created.append("reasoning")
return artifacts_created
def process_lcars_message(message, history, speak_response=False):
"""Process messages using the LLMAgent and parse responses"""
if not message.strip():
return "", history, "Please enter a message", []
try:
# Add user message to displayed history
new_history = history + [[message, ""]]
# Use the agent's direct_chat method
raw_response = agent.direct_chat(message, agent.current_conversation)
# Parse the response
parsed_response = parse_llm_response(raw_response)
# Extract and save artifacts from the response
artifacts_created = extract_artifacts_from_response(parsed_response, agent.current_conversation)
# Update the history with the main content
display_content = parsed_response.main_content
if parsed_response.code_snippets:
display_content += "\n\n**Code Snippets Generated:**"
for i, snippet in enumerate(parsed_response.code_snippets):
display_content += f"\n```{snippet['language']}\n{snippet['code']}\n```"
new_history[-1][1] = display_content
# Speak response if enabled
if speak_response and agent.speech_enabled:
agent.speak(parsed_response.main_content)
# Get artifacts for display
artifacts = agent.get_canvas_summary(agent.current_conversation)
status = f"✅ Response parsed. Artifacts created: {len(artifacts_created)} | Total: {len(artifacts)}"
return "", new_history, status, artifacts, parsed_response.thinking
except Exception as e:
error_msg = f"❌ Error: {str(e)}"
new_history = history + [[message, error_msg]]
return "", new_history, error_msg, agent.get_canvas_summary(agent.current_conversation), ""
def update_chat_display(history):
"""Convert history to formatted HTML for display"""
if not history:
return "No messages yet
"
html = ""
for i, (user_msg, bot_msg) in enumerate(history):
html += f"""
👤 You: {user_msg}
🤖 L.C.A.R.S: {bot_msg}
"""
html += "
No artifacts generated yet
"
html = ""
for i, artifact in enumerate(artifacts[-10:]): # Last 10 artifacts
type_icon = {
"code": "💻",
"text": "📝",
"diagram": "📊",
"image": "🖼️"
}.get(artifact.type, "📄")
html += f"""
{type_icon} {artifact.title} (#{i})
Type: {artifact.type} | Time: {time.ctime(artifact.timestamp)}
{artifact.content[:150]}{'...' if len(artifact.content) > 150 else ''}
"""
html += "
⚙️ MODEL SETTINGS
")
with gr.Accordion("🔧 Configuration", open=True):
base_url = gr.Textbox(
value=agent.base_url,
label="Base URL",
placeholder="http://localhost:1234/v1"
)
api_key = gr.Textbox(
value=agent.api_key,
label="API Key",
placeholder="not-needed for local models",
type="password"
)
model_id = gr.Dropdown(
value=agent.model_id,
label="Model",
choices=[agent.model_id],
allow_custom_value=True
)
temperature = gr.Slider(
value=agent.temperature,
minimum=0.1,
maximum=2.0,
step=0.1,
label="Temperature"
)
max_tokens = gr.Slider(
value=agent.max_tokens,
minimum=100,
maximum=10000,
step=100,
label="Max Tokens"
)
with gr.Row():
update_settings_btn = gr.Button("🔄 Update Settings", variant="primary")
fetch_models_btn = gr.Button("📋 Fetch Models", variant="secondary")
# ============================================
# HEADER SECTION
# ============================================
with gr.Row():
with gr.Column(scale=1):
gr.Image(
value="https://cdn-avatars.huggingface.co/v1/production/uploads/65d883893a52cd9bcd8ab7cf/tRsCJlHNZo1D02kBTmfy9.jpeg",
elem_id="lcars_logo",
height=200,
show_download_button=False,
container=False,
width=200
)
with gr.Column(scale=3):
gr.HTML(f"""
🖥️ L.C.A.R.S - Local Computer Advanced Reasoning System
USS Enterprise • NCC-1701-D • Starfleet Command
""")
# ============================================
# MAIN INTERFACE TABS
# ============================================
with gr.Tabs():
# ============================================
# L.C.A.R.S MAIN CHAT TAB (Enhanced)
# ============================================
with gr.TabItem(label="🤖 L.C.A.R.S Chat Intelligence", elem_id="lcars_main_tab"):
with gr.Row():
# LEFT COLUMN - INPUT & CONTROLS
with gr.Column(scale=2):
gr.HTML("🧠 REASONING PROCESS
")
with gr.Accordion(label="🧠 AI Reasoning & Thinking", open=True):
thinking_display = gr.Markdown(
value="*AI reasoning will appear here during processing...*",
label="Thought Process",
show_label=True,
height=200
)
# Main chat input
message = gr.Textbox(
show_copy_button=True,
lines=3,
label="💬 Ask L.C.A.R.S",
placeholder="Enter your message to the Local Computer Advanced Reasoning System..."
)
# Control buttons
with gr.Row():
submit_btn = gr.Button("🚀 Ask L.C.A.R.S", variant="primary", size="lg")
clear_btn = gr.Button("🗑️ Clear Chat", variant="secondary")
new_session_btn = gr.Button("🆕 New Session", variant="secondary")
# Audio controls
with gr.Row():
speak_response = gr.Checkbox(label="🔊 Speak Response", value=False)
# Quick Actions
with gr.Accordion(label="⚡ Utility Quick Actions", open=False):
with gr.Row():
artifact_id_input = gr.Textbox(
label="Artifact ID",
placeholder="Artifact ID (0, 1, 2...)",
scale=2
)
execute_artifact_btn = gr.Button("📂 Load Artifact", variant="primary")
# MIDDLE COLUMN - RESPONSES
with gr.Column(scale=2):
gr.HTML("SYSTEM RESPONSE
")
with gr.Accordion(label="🤖 L.C.A.R.S Response", open=True):
plain_text_output = gr.Markdown(
value="## 🤖 L.C.A.R.S Response\n\n*Awaiting your query...*",
container=True,
show_copy_button=True,
label="AI Response",
height=300
)
execution_output = gr.Markdown(
value="*Execution results will appear here*",
label="Execution Results",
height=150
)
status_display = gr.Textbox(
value="System ready",
label="Status",
interactive=False
)
gr.HTML("Current Session
")
# Enhanced Chat History Display
with gr.Accordion(label="📜 Session Chat History", open=True):
chat_history_display = gr.HTML(
value="No messages yet
",
label="Full Session History",
show_label=True
)
# RIGHT COLUMN - ENHANCED CODE ARTIFACTS
with gr.Column(scale=2):
gr.HTML("🧱 ENHANCED CODE ARTIFACTS WORKSHOP
")
with gr.Accordion(label="🧱 Code Artifacts Workshop", open=True):
# Enhanced Code Editor with save functionality
code_artifacts = gr.Code(
language="python",
label="Generated Code & Artifacts",
lines=15,
interactive=True,
show_line_numbers=True,
elem_id="code_editor",
value="# Welcome to L.C.A.R.S Code Workshop\n# Write or generate code here\n\nprint('🚀 L.C.A.R.S Code Workshop Active')"
)
# Enhanced Artifact Controls
with gr.Row():
artifact_description = gr.Textbox(
label="Artifact Description",
placeholder="Brief description of the code...",
scale=2
)
artifact_language = gr.Dropdown(
choices=["python", "javascript", "html", "css", "bash", "sql", "json"],
value="python",
label="Language",
scale=1
)
with gr.Row():
execute_code_btn = gr.Button("▶️ Execute Code", variant="primary")
create_artifact_btn = gr.Button("💾 Save Artifact", variant="primary")
# Artifacts Display
with gr.Accordion(label="📊 Current Session Artifacts", open=True):
artifacts_display = gr.HTML(
value="No artifacts generated yet
",
label="Generated Artifacts Timeline",
show_label=True
)
# ============================================
# EVENT HANDLERS - WITH PARSED RESPONSE SUPPORT
# ============================================
# Main chat functionality
def handle_message(message, history, speak_response):
# Process the message
cleaned_message, new_history, status_msg, artifacts, thinking = process_lcars_message(message, history, speak_response)
# Update all displays
plain_text = get_plain_text_response(new_history)
chat_display = update_chat_display(new_history)
artifacts_html = update_artifacts_display()
# Format thinking for display
thinking_display_content = f"## 🧠 AI Reasoning\n\n{thinking}" if thinking else "*No reasoning content extracted*"
# Return in correct order for outputs
return cleaned_message, new_history, plain_text, status_msg, chat_display, artifacts_html, thinking_display_content
submit_btn.click(
fn=handle_message,
inputs=[message, history_state, speak_response],
outputs=[
message, # 0 - cleaned message input
history_state, # 1 - updated history state
plain_text_output, # 2 - markdown response (string)
status_display, # 3 - status message (string)
chat_history_display, # 4 - HTML display
artifacts_display, # 5 - HTML display
thinking_display # 6 - thinking markdown
]
)
message.submit(
fn=handle_message,
inputs=[message, history_state, speak_response],
outputs=[
message,
history_state,
plain_text_output,
status_display,
chat_history_display,
artifacts_display,
thinking_display
]
)
# Clear chat
clear_btn.click(
fn=clear_current_chat,
outputs=[
history_state, # 0 - empty history list
plain_text_output, # 1 - markdown string
status_display, # 2 - status string
chat_history_display, # 3 - HTML string
artifacts_display, # 4 - HTML string
thinking_display # 5 - thinking markdown
]
)
# New session
new_session_btn.click(
fn=new_session,
outputs=[
history_state, # 0 - empty history list
code_artifacts, # 1 - code string
plain_text_output, # 2 - markdown string
status_display, # 3 - status string
chat_history_display, # 4 - HTML string
artifacts_display, # 5 - HTML string
thinking_display # 6 - thinking markdown
]
)
# Artifact operations
create_artifact_btn.click(
fn=create_code_artifact,
inputs=[code_artifacts, artifact_description, artifact_language],
outputs=[execution_output, code_artifacts]
)
execute_artifact_btn.click(
fn=execute_code_artifact,
inputs=[artifact_id_input, code_artifacts],
outputs=[execution_output, code_artifacts]
)
execute_code_btn.click(
fn=execute_current_code,
inputs=[code_artifacts],
outputs=[execution_output, code_artifacts]
)
# Model settings
update_settings_btn.click(
fn=update_model_settings,
inputs=[base_url, api_key, model_id, temperature, max_tokens],
outputs=[status_display]
)
fetch_models_btn.click(
fn=fetch_models,
inputs=[base_url, api_key],
outputs=[model_id]
)
if __name__ == "__main__":
# Start the agent
agent.start()
print("🚀 L.C.A.R.S Agent Started!")
print(f"🤖 Model: {agent.model_id}")
print(f"🔗 Base URL: {agent.base_url}")
print(f"💬 Default Conversation: {agent.current_conversation}")
# Launch the interface
demo.launch(share=True, server_name="0.0.0.0", server_port=7860)