app.py

import os
import json
from datetime import datetime
import asyncio
import aiohttp
from typing import Dict, List, Optional
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, HttpUrl
import uvicorn
from git_clone import clone_repository

# ===== CONFIG =====
class Settings:
    # Server URLs and Ports
    CONTROLLER_HOST = "0.0.0.0"  # Listen on all interfaces
    CONTROLLER_PORT = 8000
    # This should be the actual IP or hostname where controller is accessible
    CONTROLLER_BASE_URL = os.getenv("CONTROLLER_BASE_URL", "http://192.168.1.100:8000")

    # List of tensor server URLs - should be actual IP addresses or hostnames
    TENSOR_SERVER_URLS = os.getenv("TENSOR_SERVER_URLS", "").split(",") or [
        "https://fred808-ilob.hf.space",  # Example IP for tensor server 1
        "https://fred808-tserv.hf.space",  # Example IP for tensor server 2
        "https://fred808-tserve2.hf.space"   # Example IP for tensor server 3
    ]
    
    # Aggregator settings - should be actual IP or hostname
    AGGREGATOR_URL = os.getenv("AGGREGATOR_URL", "http://192.168.1.104:8002")
    
    # Model settings
    MODEL_REPO = "https://huggingface.co/microsoft/Florence-2-large"
    
    # Server settings
    TENSOR_SERVER_TIMEOUT = 30  # seconds
    MAX_ERROR_THRESHOLD = 5     # maximum number of errors
    SERVER_TIMEOUT = 60         # seconds before marking as error
    MONITORING_INTERVAL = 15    # seconds between health checks
    
    # Dynamic distribution settings
    @classmethod
    def get_optimal_chunk_size(cls, total_params: int, num_servers: int) -> int:
        """Calculate optimal chunk size based on number of servers"""
        # Aim for 2-3 chunks per server for better parallelism
        target_chunks = num_servers * 2
        return max(1, total_params // target_chunks)
    
    @classmethod
    def get_min_servers_required(cls) -> int:
        """Dynamically calculate minimum servers needed based on registered servers"""
        return max(2, len(cls.TENSOR_SERVER_URLS) // 3)  # At least 1/3 of registered servers
        
    @classmethod
    def get_min_replica_count(cls, num_servers: int) -> int:
        """Calculate minimum replicas based on server count"""
        return max(2, num_servers // 4)  # At least 25% of servers should have each chunk
    
    # Tokenizer settings
    MAX_SEQUENCE_LENGTH = 2048
    VOCAB_SIZE = 50257
    
    @classmethod
    def from_env(cls):
        """Load settings from environment variables"""
        cls.CONTROLLER_HOST = os.getenv("CONTROLLER_HOST", cls.CONTROLLER_HOST)
        cls.CONTROLLER_PORT = int(os.getenv("CONTROLLER_PORT", cls.CONTROLLER_PORT))
        cls.CONTROLLER_BASE_URL = os.getenv("CONTROLLER_BASE_URL", cls.CONTROLLER_BASE_URL)
        
        # Load tensor server URLs from environment
        tensor_urls = os.getenv("TENSOR_SERVER_URLS")
        if tensor_urls:
            cls.TENSOR_SERVER_URLS = tensor_urls.split(",")
            
        cls.AGGREGATOR_HOST = os.getenv("AGGREGATOR_HOST", cls.AGGREGATOR_HOST)
        cls.AGGREGATOR_PORT = int(os.getenv("AGGREGATOR_PORT", cls.AGGREGATOR_PORT))
        cls.AGGREGATOR_URL = os.getenv("AGGREGATOR_URL", 
            f"http://{cls.AGGREGATOR_HOST}:{cls.AGGREGATOR_PORT}")
        
        return cls

# ===== State Models =====
class ServerMetrics(BaseModel):
    """Metrics for tensor server performance and load"""
    cpu_usage: float = 0.0
    memory_usage: float = 0.0
    gpu_usage: Optional[float] = None
    active_requests: int = 0
    total_requests: int = 0
    average_response_time: float = 0.0
    last_error: Optional[str] = None
    error_count: int = 0

class TensorServer(BaseModel):
    """Represents a registered tensor server"""
    url: HttpUrl
    status: str = "initializing"  # initializing, ready, busy, error, degraded
    last_heartbeat: datetime = datetime.now()
    model_chunks: List[int] = []  # List of chunk IDs assigned to this server
    metrics: ServerMetrics = ServerMetrics()
    version: str = "1.0.0"
    capabilities: Dict[str, bool] = {
        "gpu_available": False,
        "quantization_support": False,
        "tensor_parallelism": False
    }
    
class ModelChunk(BaseModel):
    """Represents a chunk of the model to be sent to a tensor server"""
    chunk_id: int
    files: List[str]  # files included in this chunk
    config: Dict      # configuration for this chunk
    size_bytes: int = 0
    server_assignments: List[str] = []  # URLs of servers holding this chunk
    status: str = "unassigned"  # unassigned, assigned, loaded, error
    metrics: Dict[str, float] = {
        "load_time": 0.0,
        "memory_usage": 0.0,
        "average_inference_time": 0.0
    }

# ===== FastAPI App =====
app = FastAPI(
    title="Florence-2 Model Controller",
    description="Controls model distribution across tensor servers",
    version="1.0.0"
)

# ===== Global State =====
class ControllerState:
    def __init__(self):
        self.model_files: Dict[str, str] = {}  # Mapping of filename to file path
        self.model_config: Dict = {}           # Model configuration
        self.tensor_servers: Dict[str, TensorServer] = {}
        self.model_chunks: Dict[int, ModelChunk] = {}
        self.is_model_loaded = False
        self.operation_results: Dict[str, Dict] = {}  # Track operation results from tensor servers
        self.pending_operations: Dict[str, asyncio.Task] = {}  # Track ongoing operations
        
state = ControllerState()

# ===== Helper Functions =====
async def split_model_weights():
    """Split model weights into chunks based on available servers"""
    try:
        import torch
        
        # Load the full model weights
        model_file = next(f for f in state.model_files.values() if f.endswith('.safetensors') or f.endswith('.bin'))
        weights = torch.load(model_file, map_location='cpu')
        
        # Calculate chunks based on number of servers
        total_params = sum(p.numel() for p in weights.values())
        num_servers = len(state.tensor_servers) or len(Settings.TENSOR_SERVER_URLS)
        params_per_chunk = Settings.get_optimal_chunk_size(total_params, num_servers)
        
        print(f"[INFO] Total parameters: {total_params:,}")
        print(f"[INFO] Available servers: {num_servers}")
        print(f"[INFO] Parameters per chunk: {params_per_chunk:,}")
        
        current_chunk = []
        current_size = 0
        chunk_id = 0
        
        for key, tensor in weights.items():
            tensor_size = tensor.numel()
            
            if current_size + tensor_size > params_per_chunk and current_chunk:
                # Save current chunk
                chunk_path = os.path.join(state.model_path, f"chunk_{chunk_id}.safetensors")
                torch.save({k: weights[k] for k in current_chunk}, chunk_path)
                
                # Create chunk metadata
                state.model_chunks[chunk_id] = ModelChunk(
                    chunk_id=chunk_id,
                    files=[f"chunk_{chunk_id}.safetensors"],
                    config={
                        "weight_keys": current_chunk,
                        "input_size": weights[current_chunk[0]].size(1),
                        "output_size": weights[current_chunk[-1]].size(0)
                    }
                )
                
                # Reset for next chunk
                current_chunk = []
                current_size = 0
                chunk_id += 1
            
            current_chunk.append(key)
            current_size += tensor_size
        
        # Save last chunk if not empty
        if current_chunk:
            chunk_path = os.path.join(state.model_path, f"chunk_{chunk_id}.safetensors")
            torch.save({k: weights[k] for k in current_chunk}, chunk_path)
            
            state.model_chunks[chunk_id] = ModelChunk(
                chunk_id=chunk_id,
                files=[f"chunk_{chunk_id}.safetensors"],
                config={
                    "weight_keys": current_chunk,
                    "input_size": weights[current_chunk[0]].size(1),
                    "output_size": weights[current_chunk[-1]].size(0)
                }
            )
        
        print(f"[INFO] Split model into {len(state.model_chunks)} chunks")
        return True
        
    except Exception as e:
        print(f"[ERROR] Failed to split model weights: {str(e)}")
        return False

async def distribute_model_chunks():
    """Distribute model chunks across available tensor servers"""
    try:
        available_servers = [
            server for server in state.tensor_servers.values()
            if server.status in ["ready", "busy"] and server.metrics.error_count < Settings.MAX_ERROR_THRESHOLD
        ]
        
        min_required = Settings.get_min_servers_required()
        if len(available_servers) < min_required:
            raise Exception(f"Not enough healthy servers. Need {min_required}, got {len(available_servers)}")
            
        # Create or update weight chunks based on current server count
        if not state.model_chunks or len(state.model_chunks) > len(available_servers) * 3:
            if not await split_model_weights():
                raise Exception("Failed to split model weights")
        
        # Prepare for parallel distribution
        tasks = []
        min_replicas = Settings.get_min_replica_count(len(available_servers))
        chunks_per_server = len(state.model_chunks) / len(available_servers)
        print(f"[INFO] Distributing chunks with min {min_replicas} replicas per chunk")
        print(f"[INFO] Target chunks per server: {chunks_per_server:.1f}")
        
        # Distribute chunks
        for chunk_id, chunk in state.model_chunks.items():
            # Calculate optimal number of replicas based on chunk size and server capacity
            target_replicas = max(min_replicas,
                                int(chunks_per_server * len(available_servers) / len(state.model_chunks)))
            
            current_assignments = set(chunk.server_assignments)
            current_healthy = [url for url in current_assignments 
                             if state.tensor_servers[url].status in ["ready", "busy"]]
            
            # Remove unhealthy assignments
            chunk.server_assignments = current_healthy
            
            # Add new assignments if needed
            while len(chunk.server_assignments) < target_replicas:
                # Find least loaded eligible server
                eligible_servers = [
                    server for server in available_servers
                    if str(server.url) not in chunk.server_assignments
                    and len(server.model_chunks) < (len(state.model_chunks) / len(available_servers) * 1.5)
                ]
                
                if not eligible_servers:
                    break
                    
                # Sort by load and error count
                eligible_servers.sort(key=lambda s: (
                    len(s.model_chunks),
                    s.metrics.error_count,
                    s.metrics.cpu_usage
                ))
                
                # Assign to best server
                best_server = eligible_servers[0]
                chunk.server_assignments.append(str(best_server.url))
                best_server.model_chunks.append(chunk_id)
                print(f"[INFO] Assigned chunk {chunk_id} to server {best_server.url}")
                
        return True
        
    except Exception as e:
        print(f"[ERROR] Failed to distribute model chunks: {str(e)}")
        return False

async def monitor_tensor_servers():
    """Periodically check health and update metrics of all tensor servers"""
    while True:
        for server_url, server in state.tensor_servers.items():
            try:
                # Check basic health
                is_healthy = await check_tensor_server_health(server_url)
                
                if not is_healthy:
                    server.status = "error"
                    server.metrics.error_count += 1
                    print(f"[WARN] Server {server_url} is unhealthy")
                    continue
                
                # Get detailed metrics
                async with aiohttp.ClientSession() as session:
                    async with session.get(f"{server_url}/metrics", timeout=Settings.TENSOR_SERVER_TIMEOUT) as response:
                        if response.status == 200:
                            metrics = await response.json()
                            server.metrics = ServerMetrics(**metrics)
                            
                            # Update server status based on metrics
                            if server.metrics.error_count > Settings.MAX_ERROR_THRESHOLD:
                                server.status = "degraded"
                            elif server.metrics.cpu_usage > 90 or server.metrics.memory_usage > 90:
                                server.status = "busy"
                            else:
                                server.status = "ready"
                                
                server.last_heartbeat = datetime.now()
                
            except Exception as e:
                print(f"[ERROR] Failed to monitor server {server_url}: {str(e)}")
                server.status = "error"
                server.metrics.last_error = str(e)
                server.metrics.error_count += 1
        
        # Check for servers that haven't responded in a while
        current_time = datetime.now()
        for server_url, server in state.tensor_servers.items():
            if (current_time - server.last_heartbeat).seconds > Settings.SERVER_TIMEOUT:
                print(f"[WARN] Server {server_url} hasn't responded in {Settings.SERVER_TIMEOUT} seconds")
                server.status = "error"
                
        await asyncio.sleep(Settings.MONITORING_INTERVAL)

def get_next_model_version(base_dir: str, model_name: str) -> int:
    """Get the next available version number for the model"""
    existing_versions = []
    model_base_dir = os.path.join(base_dir, model_name)
    if os.path.exists(model_base_dir):
        for d in os.listdir(model_base_dir):
            if d.startswith('v') and d[1:].isdigit():
                existing_versions.append(int(d[1:]))
    return max(existing_versions + [0]) + 1

def check_existing_model(model_path: str) -> bool:
    """Check if a model exists and has required files"""
    if not os.path.exists(model_path):
        return False
    
    # Check for essential files
    required_files = ['config.json']
    model_files = os.listdir(model_path)
    
    # Check for any weight files
    has_weights = any(f.endswith(('.bin', '.safetensors')) for f in model_files)
    
    return all(f in model_files for f in required_files) and has_weights

async def download_model_files():
    """Downloads the model files using git clone from Hugging Face repository"""
    try:
        print(f"[INFO] Processing model from {Settings.MODEL_REPO}...")
        
        # Create models directory
        models_dir = os.path.join(os.getcwd(), "models")
        os.makedirs(models_dir, exist_ok=True)
        print(f"[INFO] Models directory: {models_dir}")
        
        # Get the model name from the repository URL
        model_name = Settings.MODEL_REPO.split('/')[-1]
        
        # Create versioned model directory
        version = get_next_model_version(models_dir, model_name)
        model_base_dir = os.path.join(models_dir, model_name)
        model_version_dir = os.path.join(model_base_dir, f"v{version}")
        
        # Check if previous version exists and is valid
        if version > 1:
            prev_version_dir = os.path.join(model_base_dir, f"v{version-1}")
            if check_existing_model(prev_version_dir):
                print(f"[INFO] Using existing model from {prev_version_dir}")
                model_path = prev_version_dir
                state.is_model_loaded = True
            else:
                # Clone new version if previous is invalid or incomplete
                os.makedirs(model_version_dir, exist_ok=True)
                success = clone_repository(Settings.MODEL_REPO, model_version_dir)
                if not success:
                    raise Exception("Failed to clone repository")
                model_path = model_version_dir
                print(f"[INFO] Successfully cloned model to {model_path}")
        else:
            # First time download
            os.makedirs(model_version_dir, exist_ok=True)
            success = clone_repository(Settings.MODEL_REPO, model_version_dir)
            if not success:
                raise Exception("Failed to clone repository")
            model_path = model_version_dir
            print(f"[INFO] Successfully cloned model to {model_path}")
        
        # Load and parse the config
        config_path = os.path.join(model_path, "config.json")
        if os.path.exists(config_path):
            with open(config_path, 'r') as f:
                state.model_config = json.load(f)
                print("[INFO] Loaded model configuration")
                print(f"[INFO] Model type: {state.model_config.get('model_type', 'unknown')}")
                print(f"[INFO] Architecture: {state.model_config.get('architectures', ['unknown'])[0]}")
        else:
            print("[WARN] No config.json found in model directory")
        
        # Scan for model files
        print("[INFO] Scanning for model files...")
        for root, _, files in os.walk(model_path):
            for file in files:
                if file.endswith(('.bin', '.json', '.safetensors')):
                    file_path = os.path.join(root, file)
                    state.model_files[file] = file_path
                    print(f"[INFO] Found model file: {file}")
        
        if state.model_files:
            state.is_model_loaded = True
            print(f"[INFO] Model files found successfully! Total files: {len(state.model_files)}")
            print(f"[INFO] Model location: {model_path}")
            return True
        else:
            raise ValueError("No model files were found in the repository")
            
    except Exception as e:
        print(f"[ERROR] Failed to process model files: {e}")
        state.is_model_loaded = False
        raise

async def check_tensor_server_health(url: HttpUrl) -> bool:
    """Checks if a tensor server is healthy"""
    try:
        async with aiohttp.ClientSession() as session:
            async with session.get(f"{url}/health", timeout=Settings.TENSOR_SERVER_TIMEOUT) as response:
                return response.status == 200
    except:
        return False

# ===== API Endpoints =====
async def execute_tensor_operation(operation_id: str, server_url: HttpUrl, operation: str, data: Dict):
    """Execute an operation on a tensor server and wait for results"""
    try:
        async with aiohttp.ClientSession() as session:
            # Start the operation
            async with session.post(
                f"{server_url}/{operation}",
                json=data,
                timeout=Settings.TENSOR_SERVER_TIMEOUT
            ) as response:
                if response.status != 200:
                    error_msg = await response.text()
                    raise HTTPException(
                        status_code=response.status,
                        detail=f"Operation failed on server {server_url}: {error_msg}"
                    )
                
                initial_response = await response.json()
                if initial_response.get("status") == "completed":
                    # Operation completed immediately
                    state.operation_results[operation_id] = initial_response
                    return initial_response
                
                # Operation is async, poll for results
                while True:
                    await asyncio.sleep(1)  # Poll interval
                    async with session.get(
                        f"{server_url}/operation/{initial_response['operation_id']}",
                        timeout=Settings.TENSOR_SERVER_TIMEOUT
                    ) as status_response:
                        if status_response.status != 200:
                            raise HTTPException(
                                status_code=status_response.status,
                                detail=f"Failed to get operation status from {server_url}"
                            )
                        
                        status_data = await status_response.json()
                        if status_data["status"] in ["completed", "failed"]:
                            state.operation_results[operation_id] = status_data
                            if status_data["status"] == "failed":
                                raise HTTPException(
                                    status_code=500,
                                    detail=f"Operation failed on server {server_url}: {status_data.get('error')}"
                                )
                            return status_data
                            
    except asyncio.TimeoutError:
        raise HTTPException(
            status_code=504,
            detail=f"Operation timed out on server {server_url}"
        )
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"Error executing operation on {server_url}: {str(e)}"
        )

@app.post("/execute/{operation}")
async def execute_operation(operation: str, data: Dict):
    """Execute an operation across tensor servers and collect results"""
    operation_id = f"{operation}_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{len(state.operation_results)}"
    
    # Get available servers with required chunks
    available_servers = [
        server for server in state.tensor_servers.values()
        if server.status in ["ready", "busy"] 
        and server.metrics.error_count < Settings.MAX_ERROR_THRESHOLD
    ]
    
    if not available_servers:
        raise HTTPException(
            status_code=503,
            detail="No available tensor servers"
        )
    
    # Start operations on all relevant servers in parallel
    tasks = []
    for server in available_servers:
        if operation in ["compute", "forward"]:
            # For compute operations, only use servers with required chunks
            required_chunks = data.get("required_chunks", [])
            if not all(chunk_id in server.model_chunks for chunk_id in required_chunks):
                continue
                
        task = asyncio.create_task(
            execute_tensor_operation(
                f"{operation_id}_{server.url}",
                server.url,
                operation,
                data
            )
        )
        tasks.append(task)
        state.pending_operations[f"{operation_id}_{server.url}"] = task
    
    if not tasks:
        raise HTTPException(
            status_code=400,
            detail="No servers available with required model chunks"
        )
    
    try:
        # Wait for all operations to complete
        results = await asyncio.gather(*tasks)
        
        # Process and aggregate results
        aggregated_result = {
            "operation_id": operation_id,
            "status": "completed",
            "server_results": results,
            "timestamp": datetime.now().isoformat()
        }
        
        # Clean up
        for task_id in list(state.pending_operations.keys()):
            if task_id.startswith(operation_id):
                del state.pending_operations[task_id]
        
        return aggregated_result
        
    except Exception as e:
        # Cancel any remaining tasks
        for task in tasks:
            if not task.done():
                task.cancel()
        
        # Clean up
        for task_id in list(state.pending_operations.keys()):
            if task_id.startswith(operation_id):
                del state.pending_operations[task_id]
        
        raise HTTPException(
            status_code=500,
            detail=f"Operation failed: {str(e)}"
        )

@app.get("/operation/{operation_id}")
async def get_operation_status(operation_id: str):
    """Get the status of an operation"""
    # Check completed operations
    results = {
        k: v for k, v in state.operation_results.items()
        if k.startswith(operation_id)
    }
    
    if results:
        return {
            "operation_id": operation_id,
            "status": "completed",
            "results": results
        }
    
    # Check pending operations
    pending = {
        k: "running" for k in state.pending_operations.keys()
        if k.startswith(operation_id)
    }
    
    if pending:
        return {
            "operation_id": operation_id,
            "status": "running",
            "pending_servers": list(pending.keys())
        }
    
    raise HTTPException(
        status_code=404,
        detail=f"Operation {operation_id} not found"
    )

@app.get("/")
async def root():
    """Health check endpoint"""
    return {
        "status": "running",
        "model_loaded": state.is_model_loaded,
        "registered_servers": len(state.tensor_servers),
        "downloaded_files": len(state.model_files),
        "config_loaded": bool(state.model_config)
    }

@app.get("/health")
async def health_check():
    """Detailed health check"""
    return {
        "status": "healthy",
        "model_loaded": state.is_model_loaded,
        "registered_servers": len(state.tensor_servers),
        "downloaded_files": list(state.model_files.keys()),
        "config_loaded": bool(state.model_config),
        "model_type": state.model_config.get("model_type", "unknown")
    }

@app.post("/register_tensor_server")
async def register_tensor_server(server_url: HttpUrl):
    """Register a new tensor server"""
    if not await check_tensor_server_health(server_url):
        raise HTTPException(status_code=400, detail="Tensor server is not healthy")
    
    state.tensor_servers[str(server_url)] = TensorServer(url=server_url)
    print(f"[INFO] Registered new tensor server at {server_url}")
    
    return {
        "status": "registered", 
        "registered_servers": len(state.tensor_servers),
        "server_id": str(server_url)
    }

@app.delete("/unregister_tensor_server")
async def unregister_tensor_server(server_url: HttpUrl):
    """Unregister a tensor server"""
    if str(server_url) in state.tensor_servers:
        # Remove server assignments from chunks
        for chunk in state.model_chunks.values():
            if str(server_url) in chunk.server_assignments:
                chunk.server_assignments.remove(str(server_url))
        
        del state.tensor_servers[str(server_url)]
        print(f"[INFO] Unregistered tensor server at {server_url}")
        
        # Trigger redistribution of chunks
        await distribute_model_chunks()
        return {"status": "unregistered"}
    raise HTTPException(status_code=404, detail="Server not found")

@app.get("/server/{server_url}/chunks")
async def get_server_chunks(server_url: HttpUrl):
    """Get the chunks assigned to a specific server"""
    if str(server_url) not in state.tensor_servers:
        raise HTTPException(status_code=404, detail="Server not found")
        
    server = state.tensor_servers[str(server_url)]
    assigned_chunks = [
        state.model_chunks[chunk_id] 
        for chunk_id in server.model_chunks
    ]
    
    return {
        "server_status": server.status,
        "assigned_chunks": assigned_chunks,
        "metrics": server.metrics.dict()
    }

@app.post("/redistribute")
async def redistribute_chunks():
    """Manually trigger redistribution of model chunks"""
    success = await distribute_model_chunks()
    if not success:
        raise HTTPException(status_code=500, detail="Failed to redistribute chunks")
    
    return {
        "status": "redistributed",
        "chunk_assignments": {
            chunk_id: chunk.server_assignments
            for chunk_id, chunk in state.model_chunks.items()
        }
    }

@app.get("/chunks/{chunk_id}/status")
async def get_chunk_status(chunk_id: int):
    """Get the status and assignments of a specific chunk"""
    if chunk_id not in state.model_chunks:
        raise HTTPException(status_code=404, detail="Chunk not found")
        
    chunk = state.model_chunks[chunk_id]
    return {
        "chunk_id": chunk_id,
        "status": chunk.status,
        "server_assignments": chunk.server_assignments,
        "metrics": chunk.metrics
    }

@app.post("/initialize")
async def initialize_system():
    """Download model files and prepare for distribution"""
    await download_model_files()
    
    # Verify downloaded files
    files_status = {}
    total_size = 0
    for filename, filepath in state.model_files.items():
        exists = os.path.exists(filepath)
        if exists:
            size = os.path.getsize(filepath)
            total_size += size
            files_status[filename] = {"exists": exists, "size_bytes": size}
        else:
            files_status[filename] = {"exists": exists, "size_bytes": 0}
    
    return {
        "status": "initialized",
        "model_loaded": state.is_model_loaded,
        "files_status": files_status,
        "total_size_bytes": total_size,
        "config_loaded": bool(state.model_config),
        "model_type": state.model_config.get("model_type", "unknown"),
        "architecture": state.model_config.get("architectures", ["unknown"])[0]
    }

# ===== Main Execution =====
@app.on_event("startup")
async def startup_event():
    """Initialize the server and start background tasks"""
    print("[INFO] Initializing system...")
    await initialize_system()
    print("[INFO] Model initialization complete")
    
    # Start monitoring task
    asyncio.create_task(monitor_tensor_servers())
    print("[INFO] Server monitoring started")

if __name__ == "__main__":
    port = int(os.getenv("PORT", 8000))
    print(f"[INFO] Starting controller server on port {port}")
    print(f"[INFO] API Documentation available at http://localhost:{port}/docs")
    
    uvicorn.run(
        "controller_server_new:app",
        host="0.0.0.0",
        port=port,
        reload=False
    )