# File: lcars_enhanced_interface.py import asyncio import json import os import time import uuid from typing import Dict, List, Any, Optional from dataclasses import dataclass import threading import pyttsx3 import re from pathlib import Path import gradio as gr from rich.console import Console from openai import OpenAI, AsyncOpenAI import asyncio from collections import defaultdict import json import os import queue import traceback import uuid from typing import Dict, List, Any, Optional, Callable, Coroutine from dataclasses import dataclass from queue import Queue, Empty from threading import Lock, Event, Thread import threading from concurrent.futures import ThreadPoolExecutor import time from openai import OpenAI, AsyncOpenAI from rich.console import Console import gradio as gr import pyttsx3 import re from pathlib import Path ############################################################# 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 = "https://huggingface.co/LeroyDyer/_Starfleet_II_-Q4_K_S-GGUF/resolve/main/_starfleet_ii_-q4_k_s.gguf" # Global state for selected model ID CLIENT =OpenAI( base_url=BASE_URL, api_key=BASE_API_KEY) # --- Configuration --- DEFAULT_BASE_URL = "http://localhost:1234/v1" DEFAULT_API_KEY = "not-needed" DEFAULT_MODEL_ID = "https://huggingface.co/LeroyDyer/_Starfleet_II_-Q4_K_S-GGUF/resolve/main/_starfleet_ii_-q4_k_s.gguf" DEFAULT_TEMPERATURE = 0.3 DEFAULT_MAX_TOKENS = 5000 # Add this configuration section at the top import os # Configuration that works for both local and HuggingFace Spaces LOCAL_BASE_URL = "http://localhost:1234/v1" LOCAL_API_KEY = "not-needed" # HuggingFace Spaces configuration - using free inference endpoints HF_INFERENCE_URL = "https://api-inference.huggingface.co/models/" HF_API_KEY = os.getenv("HF_API_KEY", "") # Set this in Spaces secrets # Available model options MODEL_OPTIONS = { "Local LM Studio": LOCAL_BASE_URL, "Codellama 7B": "codellama/CodeLlama-7b-hf", "Mistral 7B": "mistralai/Mistral-7B-v0.1", "Llama 2 7B": "meta-llama/Llama-2-7b-chat-hf", "Falcon 7B": "tiiuae/falcon-7b-instruct" } console = Console() 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 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 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 = DEFAULT_MODEL_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 # Conversation tracking self.conversations: Dict[str, List[LLMMessage]] = {} self.max_history_length = 20 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) # Active requests waiting for responses self.pending_requests: Dict[str, LLMRequest] = {} self.pending_requests_lock = Lock() # Register internal event handlers self._register_event_handlers() # Start the processing thread immediately self.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 _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 time.sleep(1) # 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 = DEFAULT_MODEL_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) -> Dict[str, Any]: """Get agent status information""" return { "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 } # --- Enhanced LLMAgent with Canvas Support --- @dataclass class CanvasArtifact: id: str type: str # 'code', 'diagram', 'text', 'image' content: str title: str timestamp: float metadata: Dict[str, Any] class EnhancedLLMAgent: def __init__(self, model_id: str = DEFAULT_MODEL_ID, system_prompt: str = None, base_url: str = LOCAL_BASE_URL, api_key: str = LOCAL_API_KEY, use_huggingface: bool = False): self.model_id = model_id self.system_prompt = system_prompt or """You are an advanced AI development assistant operating in a Star Trek LCARS interface. You specialize in code generation, analysis, and collaborative development. Always provide practical, executable code solutions when appropriate. Format code responses clearly with proper markdown code blocks and explain your reasoning.""" self.base_url = base_url self.api_key = api_key self.client = OpenAI(base_url=base_url, api_key=api_key) self.use_huggingface = use_huggingface if use_huggingface: # Use HuggingFace Inference API self.base_url = "https://api-inference.huggingface.co/models/" self.api_key = HF_API_KEY self.client = None # We'll use requests for HF console.log("[green]🚀 Using HuggingFace Inference API[/green]") else: # Use local LM Studio self.base_url = base_url self.api_key = api_key self.client = OpenAI(base_url=base_url, api_key=api_key) console.log(f"[green]🚀 Using Local LM Studio: {base_url}[/green]") # Enhanced conversation and canvas management self.conversations: Dict[str, List[Dict]] = {} self.canvas_artifacts: Dict[str, List[CanvasArtifact]] = {} self.max_history_length = 50 # 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]") 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) # Limit length self.tts_engine.runAndWait() else: self.tts_engine.say(text) # Limit length 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() def setup_tts(self): """Configure text-to-speech engine""" try: self.tts_engine = pyttsx3.init() voices = self.tts_engine.getProperty('voices') if voices: # Try to find a better voice for voice in voices: if 'female' in voice.name.lower() or 'zira' in voice.name.lower(): self.tts_engine.setProperty('voice', voice.id) break else: self.tts_engine.setProperty('voice', voices[0].id) self.tts_engine.setProperty('rate', 180) # Slightly faster self.tts_engine.setProperty('volume', 1.0) # Maximum volume self.speech_enabled = True console.log("[green]TTS engine initialized successfully[/green]") except Exception as e: console.log(f"[red]TTS initialization failed: {e}[/red]") self.speech_enabled = False async def _local_inference(self, messages: List[Dict]) -> str: """Use local LM Studio""" async_client = AsyncOpenAI(base_url=self.base_url, api_key=self.api_key) response = await async_client.chat.completions.create( model=self.model_id, messages=messages, temperature=0.7, max_tokens=DEFAULT_MAX_TOKENS ) return response.choices[0].message.content async def _hf_inference(self, messages: List[Dict]) -> str: """Use HuggingFace Inference API""" import requests import json # Convert to HF format prompt = self._convert_messages_to_prompt(messages) headers = { "Authorization": f"Bearer {self.api_key}", "Content-Type": "application/json" } payload = { "inputs": prompt, "parameters": { "max_new_tokens": DEFAULT_MAX_TOKENS, "temperature": 0.7, "do_sample": True, "return_full_text": False } } # Use the selected model model_url = f"{self.base_url}{self.model_id}" try: response = requests.post(model_url, headers=headers, json=payload) response.raise_for_status() result = response.json() return result[0]['generated_text'] except Exception as e: return f"HuggingFace API Error: {str(e)}" 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) 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""" if conversation_id not in self.conversations: self.conversations[conversation_id] = [] # Build messages with system prompt and canvas context messages = [{"role": "system", "content": self.system_prompt}] # Include canvas context if requested if include_canvas: canvas_context = self.get_canvas_context(conversation_id) if canvas_context: messages.append({"role": "system", "content": f"Current collaborative canvas state:\n{canvas_context}"}) # Add conversation history for msg in self.conversations[conversation_id][-self.max_history_length:]: messages.append(msg) # Add current message messages.append({"role": "user", "content": message}) try: # Use async client for better performance async_client = AsyncOpenAI(base_url=self.base_url, api_key=self.api_key) response = await async_client.chat.completions.create( model=self.model_id, messages=messages, temperature=0.7, max_tokens=DEFAULT_MAX_TOKENS ) response_text = response.choices[0].message.content # Update conversation history self.conversations[conversation_id].extend([ {"role": "user", "content": message}, {"role": "assistant", "content": response_text} ]) # Auto-extract and add code artifacts to canvas self._extract_artifacts_to_canvas(response_text, conversation_id) return response_text 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, code_block in enumerate(code_blocks): if len(code_block.strip()) > 10: # Only add substantial code blocks # Try to detect language from the code block marker lang_match = re.search(r'```(\w+)\n', response) lang = lang_match.group(1) if lang_match else "unknown" self.add_artifact_to_canvas( conversation_id, code_block.strip(), "code", f"code_snippet_{lang}_{len(self.canvas_artifacts.get(conversation_id, [])) + 1}" ) def clear_conversation(self, conversation_id: str = "default"): """Clear conversation but keep canvas artifacts""" if conversation_id in self.conversations: self.conversations[conversation_id] = [] console.log(f"[yellow]Cleared conversation: {conversation_id}[/yellow]") 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_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 @staticmethod async def fetch_available_models(base_url: str, api_key: str) -> List[str]: """Fetch available models from the API""" try: console.log(f"[blue]Fetching models from {base_url}[/blue]") async_client = AsyncOpenAI(base_url=base_url, api_key=api_key) models = await async_client.models.list() model_list = [model.id for model in models.data] console.log(f"[green]Found {len(model_list)} models[/green]") return model_list except Exception as e: console.log(f"[red]Error fetching models: {e}[/red]") return ["default-model"] def update_config(self, base_url: str, api_key: str, model_id: str, temperature: float, max_tokens: int): """Update agent configuration""" self.base_url = base_url self.api_key = api_key self.model_id = model_id console.log(f"[blue]Updated config: {model_id} @ {base_url}[/blue]") @staticmethod async def fetch_available_models(base_url: str, api_key: str, use_huggingface: bool = False) -> List[str]: """Fetch available models - works for both local and HF""" if use_huggingface: # Return popular HF models return list(MODEL_OPTIONS.keys())[1:] # Skip "Local LM Studio" else: # Fetch from local LM Studio try: console.log(f"[blue]Fetching models from {base_url}[/blue]") async_client = AsyncOpenAI(base_url=base_url, api_key=api_key) models = await async_client.models.list() model_list = [model.id for model in models.data] console.log(f"[green]Found {len(model_list)} local models[/green]") return model_list except Exception as e: console.log(f"[red]Error fetching local models: {e}[/red]") return ["local-model"] # Fallback async def chat_with_canvas(self, message: str, conversation_id: str = "default", include_canvas: bool = True) -> str: """Enhanced chat that works with both local and HF""" if conversation_id not in self.conversations: self.conversations[conversation_id] = [] # Build messages with system prompt and canvas context messages = [{"role": "system", "content": self.system_prompt}] # Include canvas context if requested if include_canvas: canvas_context = self.get_canvas_context(conversation_id) if canvas_context: messages.append({"role": "system", "content": f"Current collaborative canvas state:\n{canvas_context}"}) # Add conversation history for msg in self.conversations[conversation_id][-self.max_history_length:]: messages.append(msg) # Add current message messages.append({"role": "user", "content": message}) try: if self.use_huggingface: response_text = await self._hf_inference(messages) else: response_text = await self._local_inference(messages) # Update conversation history self.conversations[conversation_id].extend([ {"role": "user", "content": message}, {"role": "assistant", "content": response_text} ]) # Auto-extract and add code artifacts to canvas self._extract_artifacts_to_canvas(response_text, conversation_id) return response_text 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 _convert_messages_to_prompt(self, messages: List[Dict]) -> str: """Convert conversation messages to a single prompt for HF""" prompt = "" for msg in messages: if msg["role"] == "system": prompt += f"System: {msg['content']}\n\n" elif msg["role"] == "user": prompt += f"User: {msg['content']}\n\n" elif msg["role"] == "assistant": prompt += f"Assistant: {msg['content']}\n\n" prompt += "Assistant:" return prompt # --- LCARS Styled Gradio Interface --- class LcarsInterface: def __init__(self, agent: EnhancedLLMAgent): self.agent = agent self.current_conversation = "default" def create_interface(self): """Create the full LCARS-styled interface""" # Enhanced LCARS CSS with proper Star Trek styling 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); box-shadow: 0 4px 20px rgba(255, 153, 0, 0.3); } .lcars-title { font-size: 3em; font-weight: bold; color: var(--lcars-black); text-shadow: 3px 3px 6px rgba(255, 255, 255, 0.4); margin: 0; letter-spacing: 2px; } .lcars-subtitle { font-size: 1.4em; color: var(--lcars-black); margin: 10px 0 0 0; font-weight: bold; } .lcars-panel { background: linear-gradient(135deg, rgba(66, 66, 66, 0.9), rgba(40, 40, 40, 0.9)); border: 3px solid var(--lcars-orange); border-radius: 0 25px 0 25px; padding: 20px; margin-bottom: 20px; box-shadow: 0 4px 15px rgba(255, 153, 0, 0.2); } .lcars-button { background: linear-gradient(135deg, var(--lcars-orange), var(--lcars-red)); color: var(--lcars-black) !important; border: none !important; border-radius: 0 20px 0 20px !important; padding: 12px 24px !important; font-family: inherit !important; font-weight: bold !important; font-size: 1.1em !important; cursor: pointer !important; transition: all 0.3s ease !important; margin: 8px !important; box-shadow: 0 4px 8px rgba(255, 153, 0, 0.3) !important; } .lcars-button:hover { background: linear-gradient(135deg, var(--lcars-red), var(--lcars-orange)) !important; transform: translateY(-2px) !important; box-shadow: 0 6px 12px rgba(255, 153, 0, 0.4) !important; } .lcars-input { background: var(--lcars-black) !important; color: var(--lcars-orange) !important; border: 2px solid var(--lcars-blue) !important; border-radius: 0 15px 0 15px !important; padding: 12px !important; font-family: inherit !important; font-size: 1.1em !important; } .lcars-chatbot { background: var(--lcars-black) !important; border: 3px solid var(--lcars-purple) !important; border-radius: 0 20px 0 20px !important; min-height: 400px; max-height: 500px; } .lcars-code-editor { background: var(--lcars-black) !important; color: var(--lcars-pale-blue) !important; border: 3px solid var(--lcars-blue) !important; border-radius: 0 20px 0 20px !important; font-family: 'Fira Code', 'Courier New', monospace !important; font-size: 1em !important; } .user-message { background: linear-gradient(135deg, rgba(102, 153, 255, 0.2), rgba(51, 102, 204, 0.2)) !important; border-left: 6px solid var(--lcars-blue) !important; padding: 12px !important; margin: 8px 0 !important; border-radius: 0 15px 0 15px !important; } .assistant-message { background: linear-gradient(135deg, rgba(255, 153, 0, 0.2), rgba(255, 102, 0, 0.2)) !important; border-left: 6px solid var(--lcars-orange) !important; padding: 12px !important; margin: 8px 0 !important; border-radius: 0 15px 0 15px !important; } .artifact-item { background: linear-gradient(135deg, rgba(204, 153, 255, 0.15), rgba(153, 102, 204, 0.15)); border: 2px solid var(--lcars-purple); padding: 10px; margin: 6px 0; border-radius: 0 12px 0 12px; cursor: pointer; transition: all 0.3s ease; } .artifact-item:hover { background: linear-gradient(135deg, rgba(204, 153, 255, 0.3), rgba(153, 102, 204, 0.3)); transform: translateX(5px); } .status-indicator { display: inline-block; width: 16px; height: 16px; border-radius: 50%; background: var(--lcars-red); margin-right: 12px; box-shadow: 0 0 10px currentColor; } .status-online { background: var(--lcars-blue); animation: pulse 1.5s infinite; } @keyframes pulse { 0% { transform: scale(1); opacity: 1; } 50% { transform: scale(1.1); opacity: 0.7; } 100% { transform: scale(1); opacity: 1; } } .panel-title { color: var(--lcars-yellow) !important; font-size: 1.4em !important; font-weight: bold !important; margin-bottom: 15px !important; border-bottom: 2px solid var(--lcars-orange); padding-bottom: 8px; } .gradio-accordion { border: 2px solid var(--lcars-orange) !important; border-radius: 0 20px 0 20px !important; margin-bottom: 20px !important; } .gradio-accordion .label { background: linear-gradient(90deg, var(--lcars-orange), var(--lcars-red)) !important; color: var(--lcars-black) !important; font-size: 1.3em !important; font-weight: bold !important; padding: 15px 20px !important; } """ with gr.Blocks(css=lcars_css, theme=gr.themes.Default(), title="LCARS Terminal") as interface: with gr.Column(elem_classes="lcars-container"): # Header Section with gr.Row(elem_classes="lcars-header"): gr.Markdown("""
🚀 LCARS TERMINAL v4.2
STARFLEET AI DEVELOPMENT CONSOLE
SYSTEM ONLINE
""") # Main Content Area with gr.Row(): # Left Sidebar - Controls and Configuration with gr.Column(scale=1, min_width=400): # Configuration Panel with gr.Column(elem_classes="lcars-panel"): gr.Markdown("### 🔧 SYSTEM CONFIGURATION", elem_classes="panel-title") with gr.Row(): base_url = gr.Textbox( value=DEFAULT_BASE_URL, label="API Base URL", elem_classes="lcars-input" ) api_key = gr.Textbox( value=DEFAULT_API_KEY, label="API Key", type="password", elem_classes="lcars-input" ) with gr.Row(): model_dropdown = gr.Dropdown( choices=["Fetching models..."], value="default-model", 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", elem_classes="lcars-input" ) max_tokens = gr.Slider( 128, 8192, value=2000, step=128, label="Max Tokens", elem_classes="lcars-input" ) 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 Panel with gr.Column(elem_classes="lcars-panel"): gr.Markdown("### 🎨 CANVAS ARTIFACTS", elem_classes="panel-title") artifact_display = gr.JSON( label="", elem_id="artifact-display" ) 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 - Chat and Code Canvas with gr.Column(scale=2): # Collaborative Code Canvas with gr.Accordion("💻 COLLABORATIVE CODE CANVAS", open=True): code_editor = gr.Code( value="# Welcome to LCARS Collaborative Canvas\n# Your code artifacts will appear here\n\nprint('Hello, Starfleet!')", language="python", lines=20, label="", elem_classes="lcars-code-editor" ) with gr.Row(): load_to_chat_btn = gr.Button("💬 Discuss This Code", elem_classes="lcars-button") analyze_btn = gr.Button("🔍 Analyze Code", elem_classes="lcars-button") optimize_btn = gr.Button("⚡ Optimize", elem_classes="lcars-button") document_btn = gr.Button("📚 Document", elem_classes="lcars-button") # Chat Interface with gr.Column(elem_classes="lcars-panel"): gr.Markdown("### 💬 MISSION LOG", elem_classes="panel-title") chatbot = gr.Chatbot( label="", elem_classes="lcars-chatbot", show_label=False, height=400 ) with gr.Row(): message_input = gr.Textbox( placeholder="Enter your command or query...", show_label=False, lines=2, elem_classes="lcars-input", scale=4 ) send_btn = gr.Button("🚀 TRANSMIT", elem_classes="lcars-button", scale=1) # Status and Controls with gr.Row(): status_display = gr.Textbox( value="LCARS terminal operational. Awaiting commands.", label="Status", max_lines=2, elem_classes="lcars-input" ) 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_and_update_models(base_url, api_key): """Fetch models and update dropdown""" try: models = await EnhancedLLMAgent.fetch_available_models(base_url, api_key) if models: return gr.update(choices=models, value=models[0]) else: return gr.update(choices=["No models found"], value="No models found") except Exception as e: console.log(f"[red]Error fetching models: {e}[/red]") return gr.update(choices=[f"Error: {str(e)}"], value=f"Error: {str(e)}") def update_agent_config(base_url, api_key, model_id, temperature_val, max_tokens_val): """Update agent configuration""" try: self.agent.update_config(base_url, api_key, model_id, temperature_val, max_tokens_val) return f"✅ Configuration updated: {model_id}" except Exception as e: return f"❌ Config error: {str(e)}" def get_artifacts(): """Get current canvas artifacts""" return self.agent.get_canvas_summary(self.current_conversation) def clear_canvas(): """Clear the canvas""" self.agent.clear_canvas(self.current_conversation) return [], "✅ Canvas cleared" async def process_message(message, history, speech_enabled): """Process a chat message""" if not message.strip(): return "", history, "Please enter a message" # Add user message to history history = history + [[message, None]] try: # Get AI response response = await self.agent.chat_with_canvas( message, self.current_conversation, include_canvas=True ) # Update history with response history[-1][1] = response # Speech synthesis if enabled if speech_enabled and self.agent.speech_enabled: self.agent.speak(response) # Get updated artifacts artifacts = get_artifacts() 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, get_artifacts() def load_code_to_chat(code): """Load code from canvas into chat""" if not code.strip(): return "" return f"Please analyze this code:\n```python\n{code}\n```" def analyze_code(code): """Quick analysis of code""" if not code.strip(): return "Please provide some code to analyze" return f"Perform a comprehensive analysis of this code:\n```python\n{code}\n```" def optimize_code(code): """Quick optimization request""" if not code.strip(): return "Please provide some code to optimize" return f"Optimize this code for performance and best practices:\n```python\n{code}\n```" def document_code(code): """Quick documentation request""" if not code.strip(): return "Please provide some code to document" return f"Generate comprehensive documentation for this code:\n```python\n{code}\n```" def clear_chat(): """Clear chat history""" self.agent.clear_conversation(self.current_conversation) return [], "✅ Chat cleared" def new_session(): """Start new session""" self.agent.clear_conversation(self.current_conversation) self.agent.clear_canvas(self.current_conversation) return [], "# New collaborative session started\n\nprint('Ready for development!')", "🆕 New session started", [] # Connect event handlers fetch_models_btn.click( fetch_and_update_models, inputs=[base_url, api_key], outputs=model_dropdown ) update_config_btn.click( update_agent_config, inputs=[base_url, api_key, model_dropdown, temperature, max_tokens], 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] ) load_to_chat_btn.click( load_code_to_chat, inputs=code_editor, outputs=message_input ) analyze_btn.click( analyze_code, inputs=code_editor, outputs=message_input ) optimize_btn.click( optimize_code, inputs=code_editor, outputs=message_input ) document_btn.click( document_code, inputs=code_editor, outputs=message_input ) 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] ) # Initialize artifacts on load interface.load(get_artifacts, outputs=artifact_display) return interface # Update the LcarsInterface to include connection options class LcarsInterface: def __init__(self): # Start with HuggingFace by default for Spaces self.use_huggingface = True self.agent = EnhancedLLMAgent(use_huggingface=self.use_huggingface) self.current_conversation = "default" def create_interface(self): """Create the full LCARS-styled interface""" # Enhanced LCARS CSS with proper Star Trek styling 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); box-shadow: 0 4px 20px rgba(255, 153, 0, 0.3); } .lcars-title { font-size: 3em; font-weight: bold; color: var(--lcars-black); text-shadow: 3px 3px 6px rgba(255, 255, 255, 0.4); margin: 0; letter-spacing: 2px; } .lcars-subtitle { font-size: 1.4em; color: var(--lcars-black); margin: 10px 0 0 0; font-weight: bold; } .lcars-panel { background: linear-gradient(135deg, rgba(66, 66, 66, 0.9), rgba(40, 40, 40, 0.9)); border: 3px solid var(--lcars-orange); border-radius: 0 25px 0 25px; padding: 20px; margin-bottom: 20px; box-shadow: 0 4px 15px rgba(255, 153, 0, 0.2); } .lcars-button { background: linear-gradient(135deg, var(--lcars-orange), var(--lcars-red)); color: var(--lcars-black) !important; border: none !important; border-radius: 0 20px 0 20px !important; padding: 12px 24px !important; font-family: inherit !important; font-weight: bold !important; font-size: 1.1em !important; cursor: pointer !important; transition: all 0.3s ease !important; margin: 8px !important; box-shadow: 0 4px 8px rgba(255, 153, 0, 0.3) !important; } .lcars-button:hover { background: linear-gradient(135deg, var(--lcars-red), var(--lcars-orange)) !important; transform: translateY(-2px) !important; box-shadow: 0 6px 12px rgba(255, 153, 0, 0.4) !important; } .lcars-input { background: var(--lcars-black) !important; color: var(--lcars-orange) !important; border: 2px solid var(--lcars-blue) !important; border-radius: 0 15px 0 15px !important; padding: 12px !important; font-family: inherit !important; font-size: 1.1em !important; } .lcars-chatbot { background: var(--lcars-black) !important; border: 3px solid var(--lcars-purple) !important; border-radius: 0 20px 0 20px !important; min-height: 400px; max-height: 500px; } .lcars-code-editor { background: var(--lcars-black) !important; color: var(--lcars-pale-blue) !important; border: 3px solid var(--lcars-blue) !important; border-radius: 0 20px 0 20px !important; font-family: 'Fira Code', 'Courier New', monospace !important; font-size: 1em !important; } .user-message { background: linear-gradient(135deg, rgba(102, 153, 255, 0.2), rgba(51, 102, 204, 0.2)) !important; border-left: 6px solid var(--lcars-blue) !important; padding: 12px !important; margin: 8px 0 !important; border-radius: 0 15px 0 15px !important; } .assistant-message { background: linear-gradient(135deg, rgba(255, 153, 0, 0.2), rgba(255, 102, 0, 0.2)) !important; border-left: 6px solid var(--lcars-orange) !important; padding: 12px !important; margin: 8px 0 !important; border-radius: 0 15px 0 15px !important; } .artifact-item { background: linear-gradient(135deg, rgba(204, 153, 255, 0.15), rgba(153, 102, 204, 0.15)); border: 2px solid var(--lcars-purple); padding: 10px; margin: 6px 0; border-radius: 0 12px 0 12px; cursor: pointer; transition: all 0.3s ease; } .artifact-item:hover { background: linear-gradient(135deg, rgba(204, 153, 255, 0.3), rgba(153, 102, 204, 0.3)); transform: translateX(5px); } .status-indicator { display: inline-block; width: 16px; height: 16px; border-radius: 50%; background: var(--lcars-red); margin-right: 12px; box-shadow: 0 0 10px currentColor; } .status-online { background: var(--lcars-blue); animation: pulse 1.5s infinite; } @keyframes pulse { 0% { transform: scale(1); opacity: 1; } 50% { transform: scale(1.1); opacity: 0.7; } 100% { transform: scale(1); opacity: 1; } } .panel-title { color: var(--lcars-yellow) !important; font-size: 1.4em !important; font-weight: bold !important; margin-bottom: 15px !important; border-bottom: 2px solid var(--lcars-orange); padding-bottom: 8px; } .gradio-accordion { border: 2px solid var(--lcars-orange) !important; border-radius: 0 20px 0 20px !important; margin-bottom: 20px !important; } .gradio-accordion .label { background: linear-gradient(90deg, var(--lcars-orange), var(--lcars-red)) !important; color: var(--lcars-black) !important; font-size: 1.3em !important; font-weight: bold !important; padding: 15px 20px !important; } """ with gr.Blocks(css=lcars_css, theme=gr.themes.Default(), title="LCARS Terminal") as interface: with gr.Column(elem_classes="lcars-container"): # Header Section with gr.Row(elem_classes="lcars-header"): gr.Markdown("""
🚀 LCARS TERMINAL v4.2
STARFLEET AI DEVELOPMENT CONSOLE
SYSTEM ONLINE
""") # Main Content Area with gr.Row(): # Left Sidebar - Controls and Configuration with gr.Column(scale=1, min_width=400): # Configuration Panel with gr.Column(elem_classes="lcars-panel"): gr.Markdown("### 🔧 SYSTEM CONFIGURATION", elem_classes="panel-title") with gr.Row(): base_url = gr.Textbox( value=DEFAULT_BASE_URL, label="API Base URL", elem_classes="lcars-input" ) api_key = gr.Textbox( value=DEFAULT_API_KEY, label="API Key", type="password", elem_classes="lcars-input" ) with gr.Row(): model_dropdown = gr.Dropdown( choices=["Fetching models..."], value="default-model", 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", elem_classes="lcars-input" ) max_tokens = gr.Slider( 128, 8192, value=2000, step=128, label="Max Tokens", elem_classes="lcars-input" ) 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 Panel with gr.Column(elem_classes="lcars-panel"): gr.Markdown("### 🎨 CANVAS ARTIFACTS", elem_classes="panel-title") artifact_display = gr.JSON( label="", elem_id="artifact-display" ) 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 - Chat and Code Canvas with gr.Column(scale=2): # Collaborative Code Canvas with gr.Accordion("💻 COLLABORATIVE CODE CANVAS", open=True): code_editor = gr.Code( value="# Welcome to LCARS Collaborative Canvas\n# Your code artifacts will appear here\n\nprint('Hello, Starfleet!')", language="python", lines=20, label="", elem_classes="lcars-code-editor" ) with gr.Row(): load_to_chat_btn = gr.Button("💬 Discuss This Code", elem_classes="lcars-button") analyze_btn = gr.Button("🔍 Analyze Code", elem_classes="lcars-button") optimize_btn = gr.Button("⚡ Optimize", elem_classes="lcars-button") document_btn = gr.Button("📚 Document", elem_classes="lcars-button") # Chat Interface with gr.Column(elem_classes="lcars-panel"): gr.Markdown("### 💬 MISSION LOG", elem_classes="panel-title") chatbot = gr.Chatbot( label="", elem_classes="lcars-chatbot", show_label=False, height=400 ) with gr.Row(): message_input = gr.Textbox( placeholder="Enter your command or query...", show_label=False, lines=2, elem_classes="lcars-input", scale=4 ) send_btn = gr.Button("🚀 TRANSMIT", elem_classes="lcars-button", scale=1) # Status and Controls with gr.Row(): status_display = gr.Textbox( value="LCARS terminal operational. Awaiting commands.", label="Status", max_lines=2, elem_classes="lcars-input" ) 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_and_update_models(base_url, api_key): """Fetch models and update dropdown""" try: models = await EnhancedLLMAgent.fetch_available_models(base_url, api_key) if models: return gr.update(choices=models, value=models[0]) else: return gr.update(choices=["No models found"], value="No models found") except Exception as e: console.log(f"[red]Error fetching models: {e}[/red]") return gr.update(choices=[f"Error: {str(e)}"], value=f"Error: {str(e)}") def update_agent_config(base_url, api_key, model_id, temperature_val, max_tokens_val): """Update agent configuration""" try: self.agent.update_config(base_url, api_key, model_id, temperature_val, max_tokens_val) return f"✅ Configuration updated: {model_id}" except Exception as e: return f"❌ Config error: {str(e)}" def get_artifacts(): """Get current canvas artifacts""" return self.agent.get_canvas_summary(self.current_conversation) def clear_canvas(): """Clear the canvas""" self.agent.clear_canvas(self.current_conversation) return [], "✅ Canvas cleared" async def process_message(message, history, speech_enabled): """Process a chat message""" if not message.strip(): return "", history, "Please enter a message" # Add user message to history history = history + [[message, None]] try: # Get AI response response = await self.agent.chat_with_canvas( message, self.current_conversation, include_canvas=True ) # Update history with response history[-1][1] = response # Speech synthesis if enabled if speech_enabled and self.agent.speech_enabled: self.agent.speak(response) # Get updated artifacts artifacts = get_artifacts() 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, get_artifacts() def load_code_to_chat(code): """Load code from canvas into chat""" if not code.strip(): return "" return f"Please analyze this code:\n```python\n{code}\n```" def analyze_code(code): """Quick analysis of code""" if not code.strip(): return "Please provide some code to analyze" return f"Perform a comprehensive analysis of this code:\n```python\n{code}\n```" def optimize_code(code): """Quick optimization request""" if not code.strip(): return "Please provide some code to optimize" return f"Optimize this code for performance and best practices:\n```python\n{code}\n```" def document_code(code): """Quick documentation request""" if not code.strip(): return "Please provide some code to document" return f"Generate comprehensive documentation for this code:\n```python\n{code}\n```" def clear_chat(): """Clear chat history""" self.agent.clear_conversation(self.current_conversation) return [], "✅ Chat cleared" def new_session(): """Start new session""" self.agent.clear_conversation(self.current_conversation) self.agent.clear_canvas(self.current_conversation) return [], "# New collaborative session started\n\nprint('Ready for development!')", "🆕 New session started", [] # Connect event handlers fetch_models_btn.click( fetch_and_update_models, inputs=[base_url, api_key], outputs=model_dropdown ) update_config_btn.click( update_agent_config, inputs=[base_url, api_key, model_dropdown, temperature, max_tokens], 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] ) load_to_chat_btn.click( load_code_to_chat, inputs=code_editor, outputs=message_input ) analyze_btn.click( analyze_code, inputs=code_editor, outputs=message_input ) optimize_btn.click( optimize_code, inputs=code_editor, outputs=message_input ) document_btn.click( document_code, inputs=code_editor, outputs=message_input ) 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] ) # Initialize artifacts on load interface.load(get_artifacts, outputs=artifact_display) # Add connection type selector at the top with gr.Row(elem_classes="lcars-panel"): gr.Markdown("### 🌐 CONNECTION TYPE", elem_classes="panel-title") connection_type = gr.Radio( choices=["HuggingFace Inference", "Local LM Studio"], value="HuggingFace Inference", label="Select Connection Type", elem_classes="lcars-input" ) # Update the configuration panel with gr.Column(elem_classes="lcars-panel"): gr.Markdown("### 🔧 SYSTEM CONFIGURATION", elem_classes="panel-title") status_display = gr.Textbox() # Connection-specific settings with gr.Row(visible=False) as local_settings: 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(visible=True) as hf_settings: hf_api_key = gr.Textbox( value=HF_API_KEY, label="HuggingFace API Key", type="password", elem_classes="lcars-input", placeholder="Get from https://huggingface.co/settings/tokens" ) with gr.Row(): model_dropdown = gr.Dropdown( choices=list(MODEL_OPTIONS.keys())[1:], # Start with HF models value=list(MODEL_OPTIONS.keys())[1], label="AI Model", elem_classes="lcars-input" ) fetch_models_btn = gr.Button("📡 Fetch Models", elem_classes="lcars-button") # Update event handlers for connection switching def switch_connection(connection_type): """Switch between local and HF connection""" if connection_type == "Local LM Studio": return [ gr.update(visible=True), # local_settings gr.update(visible=False), # hf_settings gr.update(choices=["Fetching local models..."], value="Fetching local models...") ] else: return [ gr.update(visible=False), # local_settings gr.update(visible=True), # hf_settings gr.update(choices=list(MODEL_OPTIONS.keys())[1:], value=list(MODEL_OPTIONS.keys())[1]) ] connection_type.change( switch_connection, inputs=connection_type, outputs=[local_settings, hf_settings, model_dropdown] ) # Update model fetching for both connection types async def fetch_models_updated(connection_type, base_url_val, api_key_val, hf_api_key_val): if connection_type == "Local LM Studio": models = await EnhancedLLMAgent.fetch_available_models( base_url_val, api_key_val, use_huggingface=False ) else: models = await EnhancedLLMAgent.fetch_available_models( "", hf_api_key_val, use_huggingface=True ) if models: return gr.update(choices=models, value=models[0]) return gr.update(choices=["No models found"]) fetch_models_btn.click( fetch_models_updated, inputs=[connection_type, base_url, api_key, hf_api_key], outputs=model_dropdown ) # Update agent when connection changes def update_agent_connection(connection_type, model_id, base_url_val, api_key_val, hf_api_key_val): use_hf = connection_type == "HuggingFace Inference" self.use_huggingface = use_hf if use_hf: self.agent = EnhancedLLMAgent( model_id=model_id, use_huggingface=True, api_key=hf_api_key_val ) return f"✅ Switched to HuggingFace: {model_id}" else: self.agent = EnhancedLLMAgent( model_id=model_id, base_url=base_url_val, api_key=api_key_val, use_huggingface=False ) return f"✅ Switched to Local: {base_url_val}" model_dropdown.change( update_agent_connection, inputs=[connection_type, model_dropdown, base_url, api_key, hf_api_key], outputs=status_display ) return interface # Update main function for Spaces compatibility def main(): console.log("[bold blue]🚀 Starting LCARS Terminal...[/bold blue]") # Auto-detect if we're in HuggingFace Spaces is_space = os.getenv('SPACE_ID') is not None if is_space: console.log("[green]🌐 Detected HuggingFace Space - Using Inference API[/green]") else: console.log("[blue]💻 Running locally - LM Studio available[/blue]") interface = LcarsInterface() demo = interface.create_interface() demo.launch( server_name="0.0.0.0" if is_space else "127.0.0.1", server_port=7860, share=is_space # Auto-share in Spaces ) if __name__ == "__main__": main()