Update app2.py

app2.py

@@ -15,502 +15,30 @@ 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 = "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)
-# --- Configuration ---
-DEFAULT_BASE_URL = "http://localhost:1234/v1"
-DEFAULT_API_KEY = "not-needed"
-DEFAULT_MODEL_ID = "leroydyer/qwen/qwen3-0.6b-q4_k_m.gguf"
-DEFAULT_TEMPERATURE = 0.3
-DEFAULT_MAX_TOKENS = 5000
-
 
 # --- Configuration ---
-DEFAULT_BASE_URL = "http://localhost:1234/v1"
-DEFAULT_API_KEY = "not-needed"
-DEFAULT_MODEL_ID = "leroydyer/qwen/qwen3-0.6b-q4_k_m.gguf"
+LOCAL_BASE_URL = "http://localhost:1234/v1"
+LOCAL_API_KEY = "not-needed"
+
+# HuggingFace Spaces configuration
+HF_INFERENCE_URL = "https://api-inference.huggingface.co/models/"
+HF_API_KEY = os.getenv("HF_API_KEY", "")
+
+# 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"
+}
+
 DEFAULT_TEMPERATURE = 0.7
 DEFAULT_MAX_TOKENS = 5000
-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
-        }
+console = Console()
 
-# --- Enhanced Canvas Management ---
+# --- Canvas Artifact Dataclass ---
 @dataclass
 class CanvasArtifact:
     id: str
@@ -520,365 +48,31 @@ class CanvasArtifact:
     timestamp: float
     metadata: Dict[str, Any]
 
-class EnhancedLLMAgent(LLMAgent):
-    def __init__(self, *args, **kwargs):
-        super().__init__(*args, **kwargs)
-        # Enhanced canvas management
-        self.canvas_artifacts: Dict[str, List[CanvasArtifact]] = {}
-
-    def add_artifact_to_canvas(self, conversation_id: str, content: str, artifact_type: str = "code", title: str = None):
-        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)
-        return artifact
-
-    def get_canvas_context(self, conversation_id: str) -> str:
-        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()}] ---")
-            context_lines.append(artifact.content[:500] + "..." if len(artifact.content) > 500 else artifact.content)
-        return "\n".join(context_lines) + "\n=================================\n"
-
-    def _extract_artifacts_to_canvas(self, response: str, conversation_id: str):
-        code_blocks = re.findall(r'```(?:\w+)?\n(.*?)```', response, re.DOTALL)
-        for code_block in code_blocks:
-            if len(code_block.strip()) > 10:
-                lang_match = re.search(r'```(\w+)', response)
-                lang = lang_match.group(1).lower() if lang_match else "python"
-                self.add_artifact_to_canvas(
-                    conversation_id,
-                    code_block.strip(),
-                    "code",
-                    f"Extracted Code ({lang})"
-                )
-
-    def get_canvas_summary(self, conversation_id: str) -> List[Dict]:
-        if conversation_id not in self.canvas_artifacts:
-            return []
-        return [
-            {
-                "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", time.localtime(artifact.timestamp))
-            }
-            for artifact in reversed(self.canvas_artifacts[conversation_id])  # Newest first
-        ]
-
-    def get_artifact_by_id(self, conversation_id: str, artifact_id: str):
-        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"):
-        if conversation_id in self.canvas_artifacts:
-            self.canvas_artifacts[conversation_id] = []
-
-    async def chat_with_canvas(self, message: str, conversation_id: str = "default", include_canvas: bool = True) -> str:
-        """Enhanced chat that includes canvas context"""
-        # 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.get(conversation_id, [])[-self.max_history_length:]:
-            messages.append({"role": msg.role, "content": msg.content})
-
-        # Add current message
-        messages.append({"role": "user", "content": message})
-
-        try:
-            response = await self._call_(messages)
-            
-            # Update conversation history
-            user_msg = LLMMessage(role="user", content=message, conversation_id=conversation_id)
-            self._add_to_conversation_history(conversation_id, user_msg)
-            
-            response_msg = LLMMessage(role="assistant", content=response, conversation_id=conversation_id)
-            self._add_to_conversation_history(conversation_id, response_msg)
-
-            # 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
-
-
-
 # --- Enhanced LLMAgent with Canvas Support ---
-class AI_Agent:
-    def __init__(self, model_id: str, system_prompt: str = "You are a helpful assistant. Respond concisely in 1-2 sentences.", history: List[Dict] = None):
-        self.model_id = model_id
-        self.system_prompt = system_prompt
-        self.history = history or []
-        self.conversation_id = f"conv_{uuid.uuid4().hex[:8]}"
-        
-        # Create agent instance
-        self.client = LLMAgent(
-            model_id=model_id,
-            system_prompt=self.system_prompt,
-            generate_fn=LLMAgent.openai_generate
-        )
-        
-        console.log(f"[bold green]✓ MyAgent initialized with model: {model_id}[/bold green]")
-    
-    async def call_llm(self, messages: List[Dict], use_history: bool = True) -> str:
-        """
-        Send messages to LLM and get response
-        Args:
-            messages: List of message dicts with 'role' and 'content'
-            use_history: Whether to include conversation history
-        Returns:
-            str: LLM response
-        """
-        try:
-            console.log(f"[bold yellow]Sending {len(messages)} messages to LLM (use_history: {use_history})...[/bold yellow]")
-            
-            # Enhance messages based on history setting
-            enhanced_messages = await self._enhance_messages(messages, use_history)
-            
-            response = await self.client.chat(enhanced_messages)
-            console.log(f"[bold green]✓ Response received ({len(response)} chars)[/bold green]")
-            
-            # Update conversation history ONLY if we're using history
-            if use_history:
-                self._update_history(messages, response)
-            
-            return response
-            
-        except Exception as e:
-            console.log(f"[bold red]✗ ERROR: {e}[/bold red]")
-            traceback.print_exc()
-            return f"Error: {str(e)}"
-    
-    async def _enhance_messages(self, messages: List[Dict], use_history: bool) -> List[Dict]:
-        """Enhance messages with system prompt and optional history"""
-        enhanced = []
-        
-        # Add system prompt if not already in messages
-        has_system = any(msg.get('role') == 'system' for msg in messages)
-        if not has_system and self.system_prompt:
-            enhanced.append({"role": "system", "content": self.system_prompt})
-        
-        # Add conversation history only if requested
-        if use_history and self.history:
-            enhanced.extend(self.history[-10:])  # Last 10 messages for context
-        
-        # Add current messages
-        enhanced.extend(messages)
-        
-        return enhanced
-    
-    def _update_history(self, messages: List[Dict], response: str):
-        """Update conversation history with new exchange"""
-        # Add user messages to history
-        for msg in messages:
-            if msg.get('role') in ['user', 'assistant']:
-                self.history.append(msg)
-        
-        # Add assistant response to history
-        self.history.append({"role": "assistant", "content": response})
-        
-        # Keep history manageable (last 20 exchanges)
-        if len(self.history) > 40:  # 20 user + 20 assistant messages
-            self.history = self.history[-40:]
-    
-    async def simple_query(self, query: str) -> str:
-        """Simple one-shot query method - NO history/context"""
-        messages = [{"role": "user", "content": query}]
-        return await self.call_llm(messages, use_history=False)
-    
-    async def multi_turn_chat(self, user_input: str) -> str:
-        """Multi-turn chat that maintains context across calls"""
-        messages = [{"role": "user", "content": user_input}]
-        response = await self.call_llm(messages, use_history=True)
-        return response
-    
-
-    def get_conversation_summary(self) -> Dict:
-        """Get conversation summary"""
-        return {
-            "conversation_id": self.conversation_id,
-            "total_messages": len(self.history),
-            "user_messages": len([msg for msg in self.history if msg.get('role') == 'user']),
-            "assistant_messages": len([msg for msg in self.history if msg.get('role') == 'assistant']),
-            "recent_exchanges": self.history[-4:] if self.history else []
-        }
-    
-    def clear_history(self):
-        """Clear conversation history"""
-        self.history.clear()
-        console.log("[bold yellow]Conversation history cleared[/bold yellow]")
-    
-    def update_system_prompt(self, new_prompt: str):
-        """Update the system prompt"""
-        self.system_prompt = new_prompt
-        console.log(f"[bold blue]System prompt updated[/bold blue]")
-    
-    def stop(self):
-        """Stop the client gracefully"""
-        if hasattr(self, 'client') and self.client:
-            self.client.stop()
-            console.log("[bold yellow]MyAgent client stopped[/bold yellow]") 
-    async def contextual_query(self, query: str, context_messages: List[Dict] = None, 
-                             context_text: str = None, context_files: List[str] = None) -> str:
-        """
-        Query with specific context but doesn't update main history
-        
-        Args:
-            query: The user question
-            context_messages: List of message dicts for context
-            context_text: Plain text context (will be converted to system message)
-            context_files: List of file paths to read and include as context
-        """
-        messages = []
-        
-        # Add system prompt
-        if self.system_prompt:
-            messages.append({"role": "system", "content": self.system_prompt})
-        
-        # Handle different context types
-        if context_messages:
-            messages.extend(context_messages)
-        
-        if context_text:
-            messages.append({"role": "system", "content": f"Additional context: {context_text}"})
-        
-        if context_files:
-            file_context = await self._read_files_context(context_files)
-            if file_context:
-                messages.append({"role": "system", "content": f"File contents:\n{file_context}"})
-        
-        # Add the actual query
-        messages.append({"role": "user", "content": query})
-        
-        return await self.call_llm(messages, use_history=False)
-      
-    async def _read_files_context(self, file_paths: List[str]) -> str:
-        """Read multiple files and return as context string"""
-        contexts = []
-        for file_path in file_paths:
-            try:
-                if os.path.exists(file_path):
-                    with open(file_path, 'r', encoding='utf-8') as f:
-                        content = f.read()
-                        contexts.append(f"--- {os.path.basename(file_path)} ---\n{content}")
-                else:
-                    console.log(f"[bold yellow]File not found: {file_path}[/bold yellow]")
-            except Exception as e:
-                console.log(f"[bold red]Error reading file {file_path}: {e}[/bold red]")
-        
-        return "\n\n".join(contexts) if contexts else ""
-    
-    
-    async def query_with_code_context(self, query: str, code_snippets: List[str] = None,
-                                    code_files: List[str] = None) -> str:
-        """
-        Specialized contextual query for code-related questions
-        """
-        code_context = "CODE CONTEXT:\n"
-        
-        if code_snippets:
-            for i, snippet in enumerate(code_snippets, 1):
-                code_context += f"\nSnippet {i}:\n```\n{snippet}\n```\n"
-        
-        if code_files:
-            # Read code files and include them
-            for file_path in code_files:
-                if file_path.endswith(('.py', '.js', '.java', '.cpp', '.c', '.html', '.css')):
-                    code_context += f"\nFile: {file_path}\n```\n"
-                    try:
-                        with open(file_path, 'r') as f:
-                            code_context += f.read()
-                    except Exception as e:
-                        code_context += f"Error reading file: {e}"
-                    code_context += "\n```\n"
-        
-        return await self.contextual_query(query, context_text=code_context)
-    
-    async def multi_context_query(self, query: str, contexts: Dict[str, Any]) -> str:
-        """
-        Advanced contextual query with multiple context types
-        
-        Args:
-            query: The user question
-            contexts: Dict with various context types
-                - 'messages': List of message dicts
-                - 'text': Plain text context
-                - 'files': List of file paths
-                - 'urls': List of URLs
-                - 'code': List of code snippets or files
-                - 'metadata': Any additional metadata
-        """
-        all_context_messages = []
-        
-        # Build context from different sources
-        if contexts.get('text'):
-            all_context_messages.append({"role": "system", "content": f"Context: {contexts['text']}"})
-        
-        if contexts.get('messages'):
-            all_context_messages.extend(contexts['messages'])
-        
-        if contexts.get('files'):
-            file_context = await self._read_files_context(contexts['files'])
-            if file_context:
-                all_context_messages.append({"role": "system", "content": f"File Contents:\n{file_context}"})
-        
-        if contexts.get('code'):
-            code_context = "\n".join([f"Code snippet {i}:\n```\n{code}\n```" 
-                                    for i, code in enumerate(contexts['code'], 1)])
-            all_context_messages.append({"role": "system", "content": f"Code Context:\n{code_context}"})
-        
-        if contexts.get('metadata'):
-            all_context_messages.append({"role": "system", "content": f"Metadata: {contexts['metadata']}"})
-        
-        return await self.contextual_query(query, context_messages=all_context_messages)
-            
-  
-# --- 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 = DEFAULT_BASE_URL, api_key: str = DEFAULT_API_KEY):
+    def __init__(self, model_id: str = "local-model", system_prompt: str = None, 
+                 base_url: str = LOCAL_BASE_URL, api_key: str = LOCAL_API_KEY, 
+                 use_huggingface: bool = False):
+        
+        self.use_huggingface = use_huggingface
         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)
+        
+        if use_huggingface:
+            # Use HuggingFace Inference API
+            self.base_url = HF_INFERENCE_URL
+            self.api_key = HF_API_KEY
+            self.client = None
+            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]] = {}
@@ -890,20 +84,28 @@ class EnhancedLLMAgent:
             self.tts_engine = pyttsx3.init()
             self.setup_tts()
             self.speech_enabled = True
+            console.log("[green]TTS engine initialized successfully[/green]")
         except Exception as e:
-            console.log(f"[yellow]TTS not available: {e}[/yellow]")
+            console.log(f"[red]TTS initialization failed: {e}[/red]")
             self.speech_enabled = False
-        
-        console.log("[bold green]🚀 Enhanced LLM Agent Initialized[/bold green]")
 
     def setup_tts(self):
         """Configure text-to-speech engine"""
-        if hasattr(self, 'tts_engine'):
+        try:
             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)
+                # 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)
+            self.tts_engine.setProperty('volume', 1.0)
+        except Exception as e:
+            console.log(f"[red]TTS setup error: {e}[/red]")
 
     def speak(self, text: str):
         """Convert text to speech in a non-blocking way"""
@@ -912,43 +114,79 @@ class EnhancedLLMAgent:
             
         def _speak():
             try:
-                # Clean text for speech (remove markdown, code blocks)
+                # Clean text for speech
                 clean_text = re.sub(r'```.*?```', '', text, flags=re.DOTALL)
                 clean_text = re.sub(r'`.*?`', '', clean_text)
+                clean_text = re.sub(r'\n+', '. ', clean_text)
+                clean_text = re.sub(r'\s+', ' ', clean_text)
                 clean_text = clean_text.strip()
-                if clean_text:
-                    self.tts_engine.say(clean_text)  # Limit length
+                
+                if clean_text and len(clean_text) > 10:
+                    console.log(f"[blue]Speaking: {clean_text[:100]}...[/blue]")
+                    self.tts_engine.say(clean_text[:400])
                     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"""
+    async def _local_inference(self, messages: List[Dict]) -> str:
+        """Use local LM Studio"""
         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)
+            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
+        except Exception as e:
+            return f"Local inference error: {str(e)}"
 
-            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]")
+    async def _hf_inference(self, messages: List[Dict]) -> str:
+        """Use HuggingFace Inference API"""
+        try:
+            import requests
+            # 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
+                }
+            }
+            
+            model_url = f"{self.base_url}{self.model_id}"
+            response = requests.post(model_url, headers=headers, json=payload, timeout=30)
+            response.raise_for_status()
+            result = response.json()
+            return result[0]['generated_text']
         except Exception as e:
-            console.log(f"[red]TTS initialization failed: {e}[/red]")
-            self.speech_enabled = False
+            return f"HuggingFace API Error: {str(e)}"
+
+    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
 
     def add_artifact_to_canvas(self, conversation_id: str, content: str, artifact_type: str = "code", title: str = None):
         """Add artifacts to the collaborative canvas"""
@@ -974,7 +212,7 @@ class EnhancedLLMAgent:
             return ""
         
         context_lines = ["\n=== COLLABORATIVE CANVAS ARTIFACTS ==="]
-        for artifact in self.canvas_artifacts[conversation_id][-10:]:  # Last 10 artifacts
+        for artifact in self.canvas_artifacts[conversation_id][-10:]:
             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)
@@ -982,7 +220,7 @@ class EnhancedLLMAgent:
         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"""
+        """Enhanced chat that works with both local and HF"""
         if conversation_id not in self.conversations:
             self.conversations[conversation_id] = []
         
@@ -1003,16 +241,10 @@ class EnhancedLLMAgent:
         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
+            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([
@@ -1032,11 +264,9 @@ class EnhancedLLMAgent:
 
     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
+            if len(code_block.strip()) > 10:
                 lang_match = re.search(r'```(\w+)\n', response)
                 lang = lang_match.group(1) if lang_match else "unknown"
                 
@@ -1065,7 +295,7 @@ class EnhancedLLMAgent:
             return []
         
         artifacts = []
-        for artifact in reversed(self.canvas_artifacts[conversation_id]):  # Newest first
+        for artifact in reversed(self.canvas_artifacts[conversation_id]):
             artifacts.append({
                 "id": artifact.id,
                 "type": artifact.type.upper(),
@@ -1086,20 +316,6 @@ class EnhancedLLMAgent:
                 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
@@ -1107,16 +323,36 @@ class EnhancedLLMAgent:
         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"]
+
 # --- LCARS Styled Gradio Interface ---
 class LcarsInterface:
-    def __init__(self, agent: EnhancedLLMAgent):
-        self.agent = agent
+    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;
@@ -1157,166 +393,87 @@ class LcarsInterface:
             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-size: 2.5em;
             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;
+            font-size: 1.2em;
             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);
+            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: linear-gradient(135deg, var(--lcars-orange), var(--lcars-red));
+            background: var(--lcars-orange);
             color: var(--lcars-black) !important;
             border: none !important;
-            border-radius: 0 20px 0 20px !important;
-            padding: 12px 24px !important;
+            border-radius: 0 15px 0 15px !important;
+            padding: 10px 20px !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;
+            margin: 5px !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;
+            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 15px 0 15px !important;
-            padding: 12px !important;
-            font-family: inherit !important;
-            font-size: 1.1em !important;
+            border-radius: 0 10px 0 10px !important;
+            padding: 10px !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: 2px solid var(--lcars-purple) !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;
+            width: 12px;
+            height: 12px;
             border-radius: 50%;
             background: var(--lcars-red);
-            margin-right: 12px;
-            box-shadow: 0 0 10px currentColor;
+            margin-right: 8px;
         }
         
         .status-online {
             background: var(--lcars-blue);
-            animation: pulse 1.5s infinite;
+            animation: pulse 2s 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;
+            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 Section
+                # Header
                 with gr.Row(elem_classes="lcars-header"):
                     gr.Markdown("""
                     <div style="text-align: center; width: 100%;">
-                        <div class="lcars-title">🚀 LCARS TERMINAL v4.2</div>
+                        <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>
@@ -1325,111 +482,108 @@ class LcarsInterface:
                     </div>
                     """)
                 
-                # Main Content Area
+                # Connection Type Selector
+                with gr.Row(elem_classes="lcars-panel"):
+                    gr.Markdown("### 🌐 CONNECTION TYPE")
+                    connection_type = gr.Radio(
+                        choices=["HuggingFace Inference", "Local LM Studio"],
+                        value="HuggingFace Inference",
+                        label="Select Connection Type",
+                        elem_classes="lcars-input"
+                    )
+                
+                # Main Content
                 with gr.Row():
-                    # Left Sidebar - Controls and Configuration
-                    with gr.Column(scale=1, min_width=400):
+                    # Left Sidebar
+                    with gr.Column(scale=1):
                         # Configuration Panel
                         with gr.Column(elem_classes="lcars-panel"):
-                            gr.Markdown("### 🔧 SYSTEM CONFIGURATION", elem_classes="panel-title")
+                            gr.Markdown("### 🔧 CONFIGURATION")
                             
-                            with gr.Row():
+                            # Connection-specific settings
+                            with gr.Row(visible=False) as local_settings:
                                 base_url = gr.Textbox(
-                                    value=DEFAULT_BASE_URL,
-                                    label="API Base URL",
+                                    value=LOCAL_BASE_URL,
+                                    label="LM Studio URL",
                                     elem_classes="lcars-input"
                                 )
                                 api_key = gr.Textbox(
-                                    value=DEFAULT_API_KEY,
+                                    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=["Fetching models..."],
-                                    value="default-model",
+                                    choices=list(MODEL_OPTIONS.keys())[1:],
+                                    value=list(MODEL_OPTIONS.keys())[1],
                                     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"
-                                )
+                                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 Panel
+                        # Canvas Artifacts
                         with gr.Column(elem_classes="lcars-panel"):
-                            gr.Markdown("### 🎨 CANVAS ARTIFACTS", elem_classes="panel-title")
-                            artifact_display = gr.JSON(
-                                label="",
-                                elem_id="artifact-display"
-                            )
+                            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 - Chat and Code Canvas
+                    # Main Content Area
                     with gr.Column(scale=2):
-                        # Collaborative Code Canvas
+                        # 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!')",
+                                value="# Welcome to LCARS Collaborative Canvas\n\nprint('Hello, Starfleet!')",
                                 language="python",
-                                lines=20,
-                                label="",
-                                elem_classes="lcars-code-editor"
+                                lines=15,
+                                label=""
                             )
                             
                             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")
+                                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")
-                                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
-                            )
+                            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,
-                                    elem_classes="lcars-input",
                                     scale=4
                                 )
-                                send_btn = gr.Button("🚀 TRANSMIT", elem_classes="lcars-button", scale=1)
+                                send_btn = gr.Button("🚀 SEND", elem_classes="lcars-button", scale=1)
                         
-                        # Status and Controls
+                        # Status
                         with gr.Row():
                             status_display = gr.Textbox(
                                 value="LCARS terminal operational. Awaiting commands.",
                                 label="Status",
-                                max_lines=2,
-                                elem_classes="lcars-input"
+                                max_lines=2
                             )
                             with gr.Column(scale=0):
                                 clear_chat_btn = gr.Button("🗑️ Clear Chat", elem_classes="lcars-button")
@@ -1437,201 +591,143 @@ class LcarsInterface:
             
             # === 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"
-            
+            def switch_connection(connection_type):
+                if connection_type == "Local LM Studio":
+                    return [
+                        gr.update(visible=True),
+                        gr.update(visible=False),
+                        gr.update(choices=["Fetching local models..."], value="Fetching local models...")
+                    ]
+                else:
+                    return [
+                        gr.update(visible=False),
+                        gr.update(visible=True),
+                        gr.update(choices=list(MODEL_OPTIONS.keys())[1:], value=list(MODEL_OPTIONS.keys())[1])
+                    ]
+
+            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"])
+
+            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}"
+
             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
+                        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()
-                    
+                    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, 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```"
-            
+                    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():
-                """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
+                return [], "# New session started\n\nprint('Ready!')", "🆕 New session started", []
+
+            # Connect events
+            connection_type.change(switch_connection, inputs=connection_type, 
+                                 outputs=[local_settings, hf_settings, model_dropdown])
+
+            fetch_models_btn.click(fetch_models_updated, 
+                                 inputs=[connection_type, base_url, api_key, hf_api_key],
+                                 outputs=model_dropdown)
+
+            model_dropdown.change(update_agent_connection,
+                                inputs=[connection_type, model_dropdown, base_url, api_key, hf_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 Enhanced Interface...[/bold blue]")
+    console.log("[bold blue]🚀 Starting LCARS Terminal...[/bold blue]")
     
-    try:
-        # Initialize the enhanced agent
-        agent = EnhancedLLMAgent()
-        
-        # Create and launch the interface
-        interface = LcarsInterface(agent)
-        demo = interface.create_interface()
-        
-        console.log("[bold green]✅ LCARS Interface Ready - Launching...[/bold green]")
-        demo.launch(
-            share=True,
-            show_error=True,
-            inbrowser=True
-        )
-    except Exception as e:
-        console.log(f"[bold red]Failed to start application: {e}[/bold red]")
-        raise
-
-
+    is_space = os.getenv('SPACE_ID') is not None
+    
+    if is_space:
+        console.log("[green]🌐 Detected HuggingFace Space[/green]")
+    else:
+        console.log("[blue]💻 Running locally[/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
+    )
 
 if __name__ == "__main__":
     main()