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# File: enhanced_gradio_interface.py
import asyncio
from collections import defaultdict
import json
import os
import re
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
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"
# Available model options
MODEL_OPTIONS = {
"Local LM Studio": LOCAL_BASE_URL,
}
DEFAULT_TEMPERATURE = 0.7
DEFAULT_MAX_TOKENS = 5000
console = Console()
# --- Canvas Artifact Support ---
@dataclass
class CanvasArtifact:
id: str
type: str # 'code', 'diagram', 'text', 'image'
content: str
title: str
timestamp: float
metadata: Dict[str, Any]
@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
# --- Event Manager (copied from your original code or imported) ---
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)
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"""
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("""
<div style="text-align: center; width: 100%;">
<div class="lcars-title">🚀 LCARS TERMINAL</div>
<div class="lcars-subtitle">STARFLEET AI DEVELOPMENT CONSOLE</div>
<div style="margin-top: 10px;">
<span class="status-indicator status-online"></span>
<span style="color: var(--lcars-black); font-weight: bold;">SYSTEM ONLINE</span>
</div>
</div>
""")
# Main Content
with gr.Row():
# Left Sidebar
with gr.Column(scale=1):
# Configuration Panel
with gr.Column(elem_classes="lcars-panel"):
gr.Markdown("### 🔧 LM STUDIO CONFIGURATION")
# Local LM Studio settings
with gr.Row():
base_url = gr.Textbox(
value=BASE_URL,
label="LM Studio URL",
elem_classes="lcars-input"
)
api_key = gr.Textbox(
value=BASE_API_KEY,
label="API Key",
type="password",
elem_classes="lcars-input"
)
with gr.Row():
model_dropdown = gr.Dropdown(
choices=["Fetching models..."],
value="Fetching models...",
label="AI Model",
elem_classes="lcars-input"
)
fetch_models_btn = gr.Button("📡 Fetch Models", elem_classes="lcars-button")
with gr.Row():
temperature = gr.Slider(0.0, 2.0, value=0.7, label="Temperature")
max_tokens = gr.Slider(128, 8192, value=2000, step=128, label="Max Tokens")
with gr.Row():
update_config_btn = gr.Button("💾 Apply Config", elem_classes="lcars-button")
speech_toggle = gr.Checkbox(value=True, label="🔊 Speech Output")
# 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(
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 ===
async def fetch_models_updated(base_url_val, api_key_val):
models = await LLMAgent.fetch_available_models(base_url_val, api_key_val)
if models:
return gr.update(choices=models, value=models[0])
return gr.update(choices=["No models found"])
def update_agent_connection(model_id, base_url_val, api_key_val):
self.agent = LLMAgent(
model_id=model_id,
base_url=base_url_val,
api_key=api_key_val,
generate_fn=LLMAgent.openai_generate
)
return f"✅ Connected to LM Studio: {model_id}"
async def process_message(message, history, speech_enabled):
if not message.strip():
return "", history, "Please enter a message"
history = history + [[message, None]]
try:
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
fetch_models_btn.click(fetch_models_updated,
inputs=[base_url, api_key],
outputs=model_dropdown)
update_config_btn.click(update_agent_connection,
inputs=[model_dropdown, base_url, api_key],
outputs=status_display)
send_btn.click(process_message,
inputs=[message_input, chatbot, speech_toggle],
outputs=[message_input, chatbot, status_display, artifact_display])
message_input.submit(process_message,
inputs=[message_input, chatbot, speech_toggle],
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])
interface.load(get_artifacts, outputs=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)
console = Console()
# --- Canvas Artifact Support ---
@dataclass
class CanvasArtifact:
id: str
type: str # 'code', 'diagram', 'text', 'image'
content: str
title: str
timestamp: float
metadata: Dict[str, Any]
class EnhancedAIAgent:
"""
Wrapper around your AI_Agent that adds canvas/artifact management
without modifying the original agent.
"""
def __init__(self, ai_agent):
self.agent = ai_agent
self.canvas_artifacts: Dict[str, List[CanvasArtifact]] = {}
self.max_canvas_artifacts = 50
console.log("[bold green]✓ Enhanced AI Agent wrapper initialized[/bold green]")
def add_artifact_to_canvas(self, conversation_id: str, content: str,
artifact_type: str = "code", title: str = None):
"""Add artifacts to the collaborative canvas"""
if conversation_id not in self.canvas_artifacts:
self.canvas_artifacts[conversation_id] = []
artifact = CanvasArtifact(
id=str(uuid.uuid4())[:8],
type=artifact_type,
content=content,
title=title or f"{artifact_type}_{len(self.canvas_artifacts[conversation_id]) + 1}",
timestamp=time.time(),
metadata={"conversation_id": conversation_id}
)
self.canvas_artifacts[conversation_id].append(artifact)
# Keep only recent artifacts
if len(self.canvas_artifacts[conversation_id]) > self.max_canvas_artifacts:
self.canvas_artifacts[conversation_id] = self.canvas_artifacts[conversation_id][-self.max_canvas_artifacts:]
console.log(f"[green]Added artifact to canvas: {artifact.title}[/green]")
return artifact
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"
async def chat_with_canvas(self, message: str, conversation_id: str = "default",
include_canvas: bool = True) -> str:
"""Enhanced chat that includes canvas context"""
# Build context with canvas artifacts if requested
full_message = message
if include_canvas:
canvas_context = self.get_canvas_context(conversation_id)
if canvas_context:
full_message = f"{canvas_context}\n\nUser Query: {message}"
try:
# Use your original agent's multi_turn_chat method
response = await self.agent.multi_turn_chat(full_message)
# Auto-extract and add code artifacts to canvas
self._extract_artifacts_to_canvas(response, conversation_id)
return response
except Exception as e:
error_msg = f"Error in chat_with_canvas: {str(e)}"
console.log(f"[red]{error_msg}[/red]")
return error_msg
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}"
)
def get_canvas_summary(self, conversation_id: str) -> List[Dict]:
"""Get summary of canvas artifacts for display"""
if conversation_id not in self.canvas_artifacts:
return []
artifacts = []
for artifact in reversed(self.canvas_artifacts[conversation_id]): # Newest first
artifacts.append({
"id": artifact.id,
"type": artifact.type.upper(),
"title": artifact.title,
"preview": artifact.content[:100] + "..." if len(artifact.content) > 100 else artifact.content,
"timestamp": time.strftime("%H:%M:%S", time.localtime(artifact.timestamp))
})
return artifacts
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 clear_canvas(self, conversation_id: str = "default"):
"""Clear canvas artifacts"""
if conversation_id in self.canvas_artifacts:
self.canvas_artifacts[conversation_id] = []
console.log(f"[yellow]Cleared canvas: {conversation_id}[/yellow]")
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
console = Console()
# --- LCARS Styled Gradio Interface ---
class LcarsInterface:
def __init__(self):
# Local LM Studio only
self.use_huggingface = False
self.agent = LLMAgent(generate_fn=LLMAgent.openai_generate)
self.current_conversation = "default"
def create_interface(self):
"""Create the full LCARS-styled interface"""
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("""
<div style="text-align: center; width: 100%;">
<div class="lcars-title">🚀 LCARS TERMINAL</div>
<div class="lcars-subtitle">STARFLEET AI DEVELOPMENT CONSOLE</div>
<div style="margin-top: 10px;">
<span class="status-indicator status-online"></span>
<span style="color: var(--lcars-black); font-weight: bold;">SYSTEM ONLINE</span>
</div>
</div>
""")
# Main Content
with gr.Row():
# Left Sidebar
with gr.Column(scale=1):
# Configuration Panel
with gr.Column(elem_classes="lcars-panel"):
gr.Markdown("### 🔧 LM STUDIO CONFIGURATION")
# Local LM Studio settings
with gr.Row():
base_url = gr.Textbox(
value=LOCAL_BASE_URL,
label="LM Studio URL",
elem_classes="lcars-input"
)
api_key = gr.Textbox(
value=LOCAL_API_KEY,
label="API Key",
type="password",
elem_classes="lcars-input"
)
with gr.Row():
model_dropdown = gr.Dropdown(
choices=["Fetching models..."],
value="Fetching models...",
label="AI Model",
elem_classes="lcars-input"
)
fetch_models_btn = gr.Button("📡 Fetch Models", elem_classes="lcars-button")
with gr.Row():
temperature = gr.Slider(0.0, 2.0, value=0.7, label="Temperature")
max_tokens = gr.Slider(128, 8192, value=2000, step=128, label="Max Tokens")
with gr.Row():
update_config_btn = gr.Button("💾 Apply Config", elem_classes="lcars-button")
speech_toggle = gr.Checkbox(value=True, label="🔊 Speech Output")
# 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(
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 ===
async def fetch_models_updated(base_url_val, api_key_val):
models = await LLMAgent.fetch_available_models(base_url_val, api_key_val)
if models:
return gr.update(choices=models, value=models[0])
return gr.update(choices=["No models found"])
def update_agent_connection(model_id, base_url_val, api_key_val):
self.agent = LLMAgent(
model_id=model_id,
base_url=base_url_val,
api_key=api_key_val,
generate_fn=LLMAgent.openai_generate
)
return f"✅ Connected to LM Studio: {model_id}"
async def process_message(message, history, speech_enabled):
if not message.strip():
return "", history, "Please enter a message"
history = history + [[message, None]]
try:
response = await self.agent.chat_with_canvas(
message, self.current_conversation, include_canvas=True
)
history[-1][1] = response
if speech_enabled and self.agent.speech_enabled:
self.agent.speak(response)
artifacts = self.agent.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.agent.get_canvas_summary(self.current_conversation)
def get_artifacts():
return self.agent.get_canvas_summary(self.current_conversation)
def clear_canvas():
self.agent.clear_canvas(self.current_conversation)
return [], "✅ Canvas cleared"
def clear_chat():
self.agent.clear_conversation(self.current_conversation)
return [], "✅ Chat cleared"
def new_session():
self.agent.clear_conversation(self.current_conversation)
self.agent.clear_canvas(self.current_conversation)
return [], "# New session started\\nprint('Ready!')", "🆕 New session started", []
# Connect events
fetch_models_btn.click(fetch_models_updated,
inputs=[base_url, api_key],
outputs=model_dropdown)
update_config_btn.click(update_agent_connection,
inputs=[model_dropdown, base_url, api_key],
outputs=status_display)
send_btn.click(process_message,
inputs=[message_input, chatbot, speech_toggle],
outputs=[message_input, chatbot, status_display, artifact_display])
message_input.submit(process_message,
inputs=[message_input, chatbot, speech_toggle],
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])
interface.load(get_artifacts, outputs=artifact_display)
return interface
# --- Main Application ---
def main():
console.log("[bold blue]🚀 Starting LCARS Terminal...[/bold blue]")
interface = LcarsInterface()
demo = interface.create_interface()
demo.launch(
share=False
)
if __name__ == "__main__":
main() |