http_server.py

import asyncio
import websockets
import json
import os
from pathlib import Path
import uuid
import time
import jwt
from typing import Dict, Any, Optional, List
import numpy as np
from fastapi import FastAPI, WebSocket, HTTPException, Depends, Request, Response
from fastapi.responses import HTMLResponse, JSONResponse
from fastapi.middleware.cors import CORSMiddleware
from fastapi.security import HTTPBearer, HTTPAuthorizationCredentials
from datetime import datetime, timedelta
import hashlib
import gzip
import base64
import logging
from pydantic import BaseModel

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)

# Create FastAPI instance with enhanced configuration
app = FastAPI(
    title="Virtual GPU Server",
    description="HTTP and WebSocket API for Virtual GPU v2",
    version="2.0.0"
)

# Add CORS middleware for cross-origin requests
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],  # Allow all origins for development
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# JWT configuration
JWT_SECRET = "virtual_gpu_secret_key_2025"  # In production, use environment variable
JWT_ALGORITHM = "HS256"
JWT_EXPIRATION_HOURS = 24

# HTTP Bearer security scheme
security = HTTPBearer()

# Pydantic models for request/response validation
class SessionCreateRequest(BaseModel):
    client_id: Optional[str] = None
    resource_limits: Optional[Dict[str, Any]] = None

class SessionResponse(BaseModel):
    session_token: str
    session_id: str
    expires_at: datetime

class VRAMWriteRequest(BaseModel):
    data: List[Any]
    metadata: Optional[Dict[str, Any]] = None
    model_size: Optional[int] = None

class VRAMResponse(BaseModel):
    status: str
    message: Optional[str] = None
    data: Optional[List[Any]] = None
    metadata: Optional[Dict[str, Any]] = None
    source: Optional[str] = None

class StateRequest(BaseModel):
    data: Dict[str, Any]
    timestamp: Optional[float] = None

class StateResponse(BaseModel):
    status: str
    message: Optional[str] = None
    data: Optional[Dict[str, Any]] = None
    source: Optional[str] = None

class CacheRequest(BaseModel):
    data: Any
    ttl: Optional[int] = None

class CacheResponse(BaseModel):
    status: str
    message: Optional[str] = None
    data: Optional[Any] = None
    source: Optional[str] = None

class ModelLoadRequest(BaseModel):
    model_data: Optional[Dict[str, Any]] = None
    model_path: Optional[str] = None
    model_hash: Optional[str] = None

class ModelInferenceRequest(BaseModel):
    input_data: List[Any]
    batch_size: Optional[int] = None

class ErrorResponse(BaseModel):
    status: str
    error_code: str
    message: str
    details: Optional[Dict[str, Any]] = None
    retry_after: Optional[int] = None
    request_id: str

class VirtualGPUServer:
    def __init__(self):
        self.base_path = Path(__file__).parent / "storage"
        self.vram_path = self.base_path / "vram_blocks"
        self.state_path = self.base_path / "gpu_state"
        self.cache_path = self.base_path / "cache"
        self.models_path = self.base_path / "models"
        
        # Ensure all storage directories exist
        self.vram_path.mkdir(parents=True, exist_ok=True)
        self.state_path.mkdir(parents=True, exist_ok=True)
        self.cache_path.mkdir(parents=True, exist_ok=True)
        self.models_path.mkdir(parents=True, exist_ok=True)
        
        # In-memory caches for faster access
        self.vram_cache: Dict[str, Any] = {}
        self.state_cache: Dict[str, Any] = {}
        self.memory_cache: Dict[str, Any] = {}
        self.model_cache: Dict[str, Any] = {}
        
        # Session management for HTTP API
        self.http_sessions: Dict[str, Dict[str, Any]] = {}
        
        # Active WebSocket connections and sessions (for backward compatibility)
        self.active_connections: Dict[str, WebSocket] = {}
        self.active_sessions: Dict[str, Dict[str, Any]] = {}
        self.heartbeat_interval = 5  # seconds
        self.connection_timeout = 30  # seconds
        
        # Performance monitoring
        self.ops_counter = 0
        self.start_time = time.time()
        self.request_counter = 0

    def _make_json_serializable(self, obj):
        """Convert non-JSON-serializable objects to serializable format"""
        if isinstance(obj, dict):
            return {k: self._make_json_serializable(v) for k, v in obj.items()}
        elif isinstance(obj, list):
            return [self._make_json_serializable(i) for i in obj]
        elif isinstance(obj, tuple):
            return list(obj)
        elif isinstance(obj, (np.ndarray, np.generic)):
            return obj.tolist()
        elif isinstance(obj, (Path, uuid.UUID)):
            return str(obj)
        elif hasattr(obj, '__dict__'):
            # Handle custom objects by converting their __dict__ to serializable format
            return self._make_json_serializable(obj.__dict__)
        elif isinstance(obj, (int, float, str, bool, type(None))):
            return obj
        else:
            # Convert any other types to string representation
            return str(obj)

    def create_session_token(self, session_id: str, client_id: str = None, resource_limits: Dict[str, Any] = None) -> str:
        """Create a JWT session token"""
        payload = {
            "session_id": session_id,
            "client_id": client_id or "anonymous",
            "resource_limits": resource_limits or {},
            "created_at": time.time(),
            "expires_at": time.time() + (JWT_EXPIRATION_HOURS * 3600)
        }
        return jwt.encode(payload, JWT_SECRET, algorithm=JWT_ALGORITHM)

    def verify_session_token(self, token: str) -> Dict[str, Any]:
        """Verify and decode a JWT session token"""
        try:
            payload = jwt.decode(token, JWT_SECRET, algorithms=[JWT_ALGORITHM])
            if payload["expires_at"] < time.time():
                raise HTTPException(status_code=401, detail="Session token expired")
            return payload
        except jwt.InvalidTokenError:
            raise HTTPException(status_code=401, detail="Invalid session token")

    def generate_request_id(self) -> str:
        """Generate a unique request ID"""
        self.request_counter += 1
        return f"req_{int(time.time())}_{self.request_counter}"

    def compress_data(self, data: bytes) -> bytes:
        """Compress data using gzip"""
        return gzip.compress(data)

    def decompress_data(self, data: bytes) -> bytes:
        """Decompress gzip data"""
        return gzip.decompress(data)

    async def handle_vram_operation(self, operation: dict) -> dict:
        """Handle VRAM read/write operations (preserved from WebSocket implementation)"""
        try:
            op_type = operation.get('type')
            if not op_type:
                raise ValueError("Missing operation type")
            
            block_id = operation.get('block_id')
            if not block_id:
                raise ValueError("Missing block_id")
            
            data = operation.get('data')
            if data and isinstance(data, (dict, list)):
                data = self._make_json_serializable(data)

            if op_type == 'write':
                if data is None:
                    raise ValueError("Missing data for write operation")
                file_path = self.vram_path / f"{block_id}.npy"
                np.save(file_path, np.array(data))
                self.vram_cache[block_id] = np.array(data)
                
                # Store metadata
                metadata = operation.get('metadata', {})
                metadata_path = self.vram_path / f"{block_id}_metadata.json"
                with open(metadata_path, 'w') as f:
                    json.dump(metadata, f)
                
                return {'status': 'success', 'message': f'Block {block_id} written'}
            
            if op_type == 'read':
                if block_id in self.vram_cache:
                    # Load metadata
                    metadata_path = self.vram_path / f"{block_id}_metadata.json"
                    metadata = {}
                    if metadata_path.exists():
                        with open(metadata_path, 'r') as f:
                            metadata = json.load(f)
                    
                    return {
                        'status': 'success',
                        'data': self.vram_cache[block_id] if isinstance(self.vram_cache[block_id], list) else self.vram_cache[block_id].tolist(),
                        'metadata': metadata,
                        'source': 'cache'
                    }
                
                file_path = self.vram_path / f"{block_id}.npy"
                if file_path.exists():
                    data = np.load(file_path)
                    self.vram_cache[block_id] = np.array(data)
                    
                    # Load metadata
                    metadata_path = self.vram_path / f"{block_id}_metadata.json"
                    metadata = {}
                    if metadata_path.exists():
                        with open(metadata_path, 'r') as f:
                            metadata = json.load(f)
                    
                    return {
                        'status': 'success',
                        'data': data.tolist(),
                        'metadata': metadata,
                        'source': 'disk'
                    }
                return {'status': 'error', 'message': 'Block not found'}
            
            return {'status': 'error', 'message': f'Unknown operation type: {op_type}'}
            
        except ValueError as e:
            return {'status': 'error', 'message': str(e)}
        except Exception as e:
            return {'status': 'error', 'message': f'Operation failed: {str(e)}'}
                
    async def handle_state_operation(self, operation: dict) -> dict:
        """Handle GPU state operations (preserved from WebSocket implementation)"""
        op_type = operation.get('type')
        component = operation.get('component')
        state_id = operation.get('state_id')
        state_data = operation.get('data')

        file_path = self.state_path / component / f"{state_id}.json"
        
        if op_type == 'save':
            file_path.parent.mkdir(parents=True, exist_ok=True)
            with open(file_path, 'w') as f:
                json.dump(state_data, f)
            self.state_cache[f"{component}:{state_id}"] = state_data
            return {'status': 'success', 'message': f'State {state_id} saved'}
            
        elif op_type == 'load':
            cache_key = f"{component}:{state_id}"
            if cache_key in self.state_cache:
                return {
                    'status': 'success',
                    'data': self.state_cache[cache_key],
                    'source': 'cache'
                }
                
            if file_path.exists():
                with open(file_path) as f:
                    state_data = json.load(f)
                self.state_cache[cache_key] = state_data
                return {
                    'status': 'success',
                    'data': state_data,
                    'source': 'disk'
                }
                
            return {'status': 'error', 'message': 'State not found'}

    async def handle_cache_operation(self, operation: dict) -> dict:
        """Handle cache operations (preserved from WebSocket implementation)"""
        op_type = operation.get('type')
        key = operation.get('key')
        data = operation.get('data')
        
        if op_type == 'set':
            self.memory_cache[key] = data
            # Also persist to disk for recovery
            file_path = self.cache_path / f"{key}.json"
            with open(file_path, 'w') as f:
                json.dump(data, f)
            return {'status': 'success', 'message': f'Cache key {key} set'}
            
        elif op_type == 'get':
            if key in self.memory_cache:
                return {
                    'status': 'success',
                    'data': self.memory_cache[key],
                    'source': 'memory'
                }
                
            file_path = self.cache_path / f"{key}.json"
            if file_path.exists():
                with open(file_path) as f:
                    data = json.load(f)
                self.memory_cache[key] = data
                return {
                    'status': 'success',
                    'data': data,
                    'source': 'disk'
                }
                
            return {'status': 'error', 'message': 'Cache key not found'}

    def get_stats(self) -> dict:
        """Get server statistics"""
        current_time = time.time()
        uptime = current_time - self.start_time
        ops_per_second = self.ops_counter / uptime if uptime > 0 else 0
        
        return {
            'uptime': uptime,
            'total_operations': self.ops_counter,
            'ops_per_second': ops_per_second,
            'active_connections': len(self.active_connections),
            'active_http_sessions': len(self.http_sessions),
            'vram_cache_size': len(self.vram_cache),
            'state_cache_size': len(self.state_cache),
            'memory_cache_size': len(self.memory_cache),
            'model_cache_size': len(self.model_cache)
        }

# Create server instance
server = VirtualGPUServer()

# Dependency to get current session from JWT token
def get_current_session(credentials: HTTPAuthorizationCredentials = Depends(security)) -> Dict[str, Any]:
    return server.verify_session_token(credentials.credentials)

# HTTP API Endpoints

@app.post("/api/v1/sessions", response_model=SessionResponse)
async def create_session(request: SessionCreateRequest):
    """Create a new HTTP session"""
    session_id = str(uuid.uuid4())
    client_id = request.client_id or "anonymous"
    
    # Create session token
    token = server.create_session_token(session_id, client_id, request.resource_limits)
    
    # Store session info
    server.http_sessions[session_id] = {
        'session_id': session_id,
        'client_id': client_id,
        'created_at': time.time(),
        'resource_limits': request.resource_limits or {},
        'ops_count': 0
    }
    
    expires_at = datetime.fromtimestamp(time.time() + (JWT_EXPIRATION_HOURS * 3600))
    
    return SessionResponse(
        session_token=token,
        session_id=session_id,
        expires_at=expires_at
    )

@app.post("/api/v1/vram/blocks/{block_id}", response_model=VRAMResponse)
async def write_vram_block(

    block_id: str,

    request: VRAMWriteRequest,

    session: Dict[str, Any] = Depends(get_current_session)

):
    """Write tensor data to VRAM block"""
    try:
        operation = {
            'operation': 'vram',
            'type': 'write',
            'block_id': block_id,
            'data': request.data,
            'metadata': request.metadata or {},
            'model_size': request.model_size
        }
        
        result = await server.handle_vram_operation(operation)
        server.ops_counter += 1
        
        if result['status'] == 'success':
            return VRAMResponse(
                status=result['status'],
                message=result['message']
            )
        else:
            raise HTTPException(status_code=400, detail=result['message'])
            
    except Exception as e:
        request_id = server.generate_request_id()
        raise HTTPException(
            status_code=500,
            detail=f"VRAM write operation failed: {str(e)}"
        )

@app.get("/api/v1/vram/blocks/{block_id}", response_model=VRAMResponse)
async def read_vram_block(

    block_id: str,

    session: Dict[str, Any] = Depends(get_current_session)

):
    """Read tensor data from VRAM block"""
    try:
        operation = {
            'operation': 'vram',
            'type': 'read',
            'block_id': block_id
        }
        
        result = await server.handle_vram_operation(operation)
        server.ops_counter += 1
        
        if result['status'] == 'success':
            return VRAMResponse(
                status=result['status'],
                data=result.get('data'),
                metadata=result.get('metadata'),
                source=result.get('source')
            )
        else:
            raise HTTPException(status_code=404, detail=result['message'])
            
    except HTTPException:
        raise
    except Exception as e:
        request_id = server.generate_request_id()
        raise HTTPException(
            status_code=500,
            detail=f"VRAM read operation failed: {str(e)}"
        )

@app.delete("/api/v1/vram/blocks/{block_id}")
async def delete_vram_block(

    block_id: str,

    session: Dict[str, Any] = Depends(get_current_session)

):
    """Delete tensor data from VRAM block"""
    try:
        # Remove from cache
        if block_id in server.vram_cache:
            del server.vram_cache[block_id]
        
        # Remove files
        file_path = server.vram_path / f"{block_id}.npy"
        metadata_path = server.vram_path / f"{block_id}_metadata.json"
        
        if file_path.exists():
            file_path.unlink()
        if metadata_path.exists():
            metadata_path.unlink()
        
        server.ops_counter += 1
        return {"status": "success", "message": f"Block {block_id} deleted"}
        
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"VRAM delete operation failed: {str(e)}"
        )

@app.post("/api/v1/state/{component}/{state_id}", response_model=StateResponse)
async def save_state(

    component: str,

    state_id: str,

    request: StateRequest,

    session: Dict[str, Any] = Depends(get_current_session)

):
    """Save component state"""
    try:
        operation = {
            'operation': 'state',
            'type': 'save',
            'component': component,
            'state_id': state_id,
            'data': request.data
        }
        
        result = await server.handle_state_operation(operation)
        server.ops_counter += 1
        
        if result['status'] == 'success':
            return StateResponse(
                status=result['status'],
                message=result['message']
            )
        else:
            raise HTTPException(status_code=400, detail=result['message'])
            
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"State save operation failed: {str(e)}"
        )

@app.get("/api/v1/state/{component}/{state_id}", response_model=StateResponse)
async def load_state(

    component: str,

    state_id: str,

    session: Dict[str, Any] = Depends(get_current_session)

):
    """Load component state"""
    try:
        operation = {
            'operation': 'state',
            'type': 'load',
            'component': component,
            'state_id': state_id
        }
        
        result = await server.handle_state_operation(operation)
        server.ops_counter += 1
        
        if result['status'] == 'success':
            return StateResponse(
                status=result['status'],
                data=result.get('data'),
                source=result.get('source')
            )
        else:
            raise HTTPException(status_code=404, detail=result['message'])
            
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"State load operation failed: {str(e)}"
        )

@app.post("/api/v1/cache/{key}", response_model=CacheResponse)
async def set_cache(

    key: str,

    request: CacheRequest,

    session: Dict[str, Any] = Depends(get_current_session)

):
    """Set cache value"""
    try:
        operation = {
            'operation': 'cache',
            'type': 'set',
            'key': key,
            'data': request.data
        }
        
        result = await server.handle_cache_operation(operation)
        server.ops_counter += 1
        
        if result['status'] == 'success':
            return CacheResponse(
                status=result['status'],
                message=result['message']
            )
        else:
            raise HTTPException(status_code=400, detail=result['message'])
            
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"Cache set operation failed: {str(e)}"
        )

@app.get("/api/v1/cache/{key}", response_model=CacheResponse)
async def get_cache(

    key: str,

    session: Dict[str, Any] = Depends(get_current_session)

):
    """Get cache value"""
    try:
        operation = {
            'operation': 'cache',
            'type': 'get',
            'key': key
        }
        
        result = await server.handle_cache_operation(operation)
        server.ops_counter += 1
        
        if result['status'] == 'success':
            return CacheResponse(
                status=result['status'],
                data=result.get('data'),
                source=result.get('source')
            )
        else:
            raise HTTPException(status_code=404, detail=result['message'])
            
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"Cache get operation failed: {str(e)}"
        )

def sanitize_filename(name: str) -> str:
    """

    Sanitize a string for safe file system usage.

    Replaces slashes with double underscores.

    """
    return name.replace('/', '__')

@app.post("/api/v1/models/{model_name:path}/load")
async def load_model(

    model_name: str,

    request: ModelLoadRequest,

    session: Dict[str, Any] = Depends(get_current_session)

):
    """Load AI model"""
    try:
        # Log the received model name for debugging
        logging.info(f"Received model load request for: {model_name}")
        
        # Get safe filename for storage
        safe_name = sanitize_filename(model_name)
        
        if not request.model_data:
            raise HTTPException(
                status_code=400,
                detail="model_data is required and must include architecture configuration"
            )
            
        # Validate required model configuration
        required_fields = ['num_sms', 'tensor_cores_per_sm', 'cuda_cores_per_sm']
        missing_fields = [field for field in required_fields if field not in request.model_data]
        if missing_fields:
            raise HTTPException(
                status_code=400,
                detail=f"Missing required model configuration fields: {missing_fields}"
            )

        # Store model information with full configuration
        model_info = {
            'model_name': model_name,
            'model_data': request.model_data,
            'model_path': request.model_path,
            'model_hash': request.model_hash,
            'loaded_at': time.time(),
            'session_id': session['session_id'],
            'architecture': {
                'num_sms': request.model_data['num_sms'],
                'tensor_cores_per_sm': request.model_data['tensor_cores_per_sm'],
                'cuda_cores_per_sm': request.model_data['cuda_cores_per_sm'],
                'vram_allocation': request.model_data.get('vram_allocation', 'dynamic'),
                'compute_capability': request.model_data.get('compute_capability', '8.0')
            }
        }
        
        server.model_cache[model_name] = model_info
        
        # Store in persistent storage
        model_file = server.models_path / f"{safe_name}.json"
        model_data_file = server.models_path / f"{safe_name}.data"
        logging.info(f"Storing model info at: {model_file}")
        
        # Store metadata and configuration
        with open(model_file, 'w') as f:
            json.dump(model_info, f)
            
        # Store actual model data separately
        if request.model_data.get('weights') or request.model_data.get('parameters'):
            logging.info(f"Storing model data at: {model_data_file}")
            with open(model_data_file, 'w') as f:
                json.dump(request.model_data, f)
        
        server.ops_counter += 1
        return {
            "status": "success",
            "message": f"Model {model_name} loaded successfully",
            "model_info": {
                "name": model_name,
                "architecture": model_info['architecture'],
                "loaded_at": model_info['loaded_at']
            }
        }
        
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"Model load operation failed: {str(e)}"
        )

@app.post("/api/v1/models/{model_name:path}/inference")
async def run_inference(

    model_name: str,

    request: ModelInferenceRequest,

    session: Dict[str, Any] = Depends(get_current_session)

):
    """Run model inference"""
    try:
        logging.info(f"Running inference - Raw model name: {model_name}")
        safe_name = sanitize_model_name(model_name)
        logging.info(f"Running inference - Safe model name: {safe_name}")
        
        # Check if model is loaded (try both original and safe names)
        if model_name not in server.model_cache:
            # Try loading from file system using safe name
            model_file = server.models_path / f"{safe_name}.json"
            if not model_file.exists():
                logging.error(f"Model {model_name} not found in cache or filesystem")
                raise HTTPException(status_code=404, detail=f"Model {model_name} not loaded")
            
            logging.info(f"Loading model info from file: {model_file}")
            with open(model_file) as f:
                model_info = json.load(f)
            server.model_cache[model_name] = model_info
        
        # Simulate inference processing
        # In a real implementation, this would invoke the actual model
        result = {
            "status": "success",
            "output": request.input_data,  # Echo input for now
            "metrics": {
                "inference_time": 0.1,
                "tokens_processed": len(request.input_data)
            },
            "model_info": server.model_cache[model_name]
        }
        
        server.ops_counter += 1
        logging.info(f"Inference completed successfully for model: {model_name}")
        return result
        
    except HTTPException:
        raise
    except Exception as e:
        logging.error(f"Inference operation failed for {model_name}: {str(e)}")
        raise HTTPException(
            status_code=500,
            detail=f"Inference operation failed: {str(e)}"
        )

@app.get("/api/v1/models/{model_name:path}/status")
async def get_model_status(

    model_name: str,

    session: Dict[str, Any] = Depends(get_current_session)

):
    """Get model status"""
    try:
        logging.info(f"Checking model status for: {model_name}")

        # Check cache first
        if model_name in server.model_cache:
            logging.info(f"Model {model_name} found in cache")
            return {
                "status": "loaded",
                "model_info": server.model_cache[model_name]
            }
        
        # Check file system using safe name
        safe_name = sanitize_filename(model_name)
        model_file = server.models_path / f"{safe_name}.json"
        if model_file.exists():
            logging.info(f"Model file found: {model_file}")
            with open(model_file) as f:
                model_info = json.load(f)
            # Update cache
            server.model_cache[model_name] = model_info
            return {
                "status": "loaded",
                "model_info": model_info
            }

        logging.info(f"Model {model_name} not found in cache or filesystem")
        return {
            "status": "not_loaded",
            "message": f"Model {model_name} is not loaded"
        }
            
    except Exception as e:
        logging.error(f"Model status check failed for {model_name}: {str(e)}")
        raise HTTPException(
            status_code=500,
            detail=f"Model status check failed: {str(e)}"
        )

# Multi-chip coordination endpoints
@app.post("/api/v1/chips/{src_chip_id}/transfer/{dst_chip_id}")
async def transfer_between_chips(

    src_chip_id: int,

    dst_chip_id: int,

    request: dict,

    session: Dict[str, Any] = Depends(get_current_session)

):
    """Transfer data between GPU chips"""
    try:
        data_id = request.get('data_id')
        if not data_id:
            raise HTTPException(status_code=400, detail="Missing data_id")
        
        # Load the source data
        source_operation = {
            'operation': 'vram',
            'type': 'read',
            'block_id': data_id
        }
        
        source_result = await server.handle_vram_operation(source_operation)
        if source_result.get('status') != 'success':
            raise HTTPException(status_code=404, detail=f"Source data {data_id} not found")
        
        # Create new data ID for destination
        new_data_id = f"{data_id}_chip_{dst_chip_id}"
        
        # Store the data with the new ID
        dest_operation = {
            'operation': 'vram',
            'type': 'write',
            'block_id': new_data_id,
            'data': source_result.get('data'),
            'metadata': source_result.get('metadata', {})
        }
        
        dest_result = await server.handle_vram_operation(dest_operation)
        if dest_result.get('status') != 'success':
            raise HTTPException(status_code=500, detail="Failed to store transferred data")
        
        # Simulate cross-chip transfer
        transfer_id = f"transfer_{time.time_ns()}"
        
        result = {
            "status": "success",
            "transfer_id": transfer_id,
            "src_chip": src_chip_id,
            "dst_chip": dst_chip_id,
            "data_id": data_id,
            "new_data_id": new_data_id
        }
        
        server.ops_counter += 1
        return result
        
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"Chip transfer failed: {str(e)}"
        )

@app.post("/api/v1/sync/barrier/{barrier_id}")
async def create_sync_barrier(

    barrier_id: str,

    request: dict,

    session: Dict[str, Any] = Depends(get_current_session)

):
    """Create synchronization barrier"""
    try:
        num_participants = request.get('num_participants', 1)
        
        # Store barrier info
        barrier_info = {
            'barrier_id': barrier_id,
            'num_participants': num_participants,
            'arrived_participants': 0,
            'created_at': time.time()
        }
        
        server.memory_cache[f"barrier_{barrier_id}"] = barrier_info
        
        return {
            "status": "success",
            "barrier_id": barrier_id,
            "num_participants": num_participants
        }
        
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"Barrier creation failed: {str(e)}"
        )

@app.put("/api/v1/sync/barrier/{barrier_id}/wait")
async def wait_sync_barrier(

    barrier_id: str,

    session: Dict[str, Any] = Depends(get_current_session)

):
    """Wait at synchronization barrier"""
    try:
        barrier_key = f"barrier_{barrier_id}"
        if barrier_key not in server.memory_cache:
            raise HTTPException(status_code=404, detail="Barrier not found")
        
        barrier_info = server.memory_cache[barrier_key]
        barrier_info['arrived_participants'] += 1
        
        # Check if all participants have arrived
        if barrier_info['arrived_participants'] >= barrier_info['num_participants']:
            # All participants arrived, release barrier
            del server.memory_cache[barrier_key]
            return {
                "status": "released",
                "message": "All participants arrived, barrier released"
            }
        else:
            return {
                "status": "waiting",
                "arrived": barrier_info['arrived_participants'],
                "total": barrier_info['num_participants']
            }
        
    except HTTPException:
        raise
    except Exception as e:
        raise HTTPException(
            status_code=500,
            detail=f"Barrier wait failed: {str(e)}"
        )

# Preserved WebSocket endpoints for backward compatibility
@app.get("/", response_class=HTMLResponse)
async def handle_index():
    """Handle HTTP index request"""
    stats = server.get_stats()
    html = f"""

    <!DOCTYPE html>

    <html>

    <head>

        <title>Virtual GPU Server v2.0</title>

        <style>

            body {{ font-family: Arial, sans-serif; margin: 40px; }}

            table {{ border-collapse: collapse; width: 100%; margin-top: 20px; }}

            th, td {{ padding: 12px; text-align: left; border-bottom: 1px solid #ddd; }}

            th {{ background-color: #f2f2f2; }}

            .stats {{ background-color: #f9f9f9; padding: 20px; border-radius: 5px; }}

            .api-info {{ background-color: #e8f4fd; padding: 20px; border-radius: 5px; margin-top: 20px; }}

        </style>

    </head>

    <body>

        <h1>Virtual GPU Server v2.0 Status</h1>

        <div class="api-info">

            <h2>API Information</h2>

            <p><strong>HTTP REST API:</strong> Available at /api/v1/</p>

            <p><strong>WebSocket API:</strong> Available at /ws (backward compatibility)</p>

            <p><strong>API Documentation:</strong> <a href="/docs">/docs</a></p>

        </div>

        <div class="stats">

            <h2>Server Statistics</h2>

            <ul>

                <li>Uptime: {stats['uptime']:.2f} seconds</li>

                <li>Total Operations: {stats['total_operations']}</li>

                <li>Operations per Second: {stats['ops_per_second']:.2f}</li>

                <li>Active WebSocket Connections: {stats['active_connections']}</li>

                <li>Active HTTP Sessions: {stats['active_http_sessions']}</li>

                <li>VRAM Cache Size: {stats['vram_cache_size']}</li>

                <li>State Cache Size: {stats['state_cache_size']}</li>

                <li>Memory Cache Size: {stats['memory_cache_size']}</li>

                <li>Model Cache Size: {stats['model_cache_size']}</li>

            </ul>

        </div>

        <h2>Server Files</h2>

        <iframe src="/files" style="width: 100%; height: 500px; border: none;"></iframe>

    </body>

    </html>

    """
    return HTMLResponse(content=html)

@app.get("/files", response_class=HTMLResponse)
async def handle_files():
    """Handle HTTP files listing request"""
    def format_size(size):
        for unit in ['B', 'KB', 'MB', 'GB']:
            if size < 1024:
                return f"{size:.2f} {unit}"
            size /= 1024
        return f"{size:.2f} TB"

    html = ['<!DOCTYPE html><html><head>',
            '<style>',
            'body { font-family: Arial, sans-serif; margin: 20px; }',
            'table { border-collapse: collapse; width: 100%; }',
            'th, td { padding: 12px; text-align: left; border-bottom: 1px solid #ddd; }',
            'th { background-color: #f2f2f2; }',
            '</style></head><body>',
            '<h2>Server Files</h2>',
            '<table><tr><th>Path</th><th>Size</th><th>Last Modified</th></tr>']

    for root, _, files in os.walk(server.base_path):
        for file in files:
            full_path = Path(root) / file
            rel_path = full_path.relative_to(server.base_path)
            size = format_size(os.path.getsize(full_path))
            mtime = datetime.fromtimestamp(os.path.getmtime(full_path))
            html.append(f'<tr><td>{rel_path}</td><td>{size}</td><td>{mtime}</td></tr>')

    html.extend(['</table></body></html>'])
    return HTMLResponse(content='\n'.join(html))

# WebSocket endpoint (preserved for backward compatibility)
@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()
    session_id = str(uuid.uuid4())
    server.active_connections[session_id] = websocket
    server.active_sessions[session_id] = {
        'start_time': time.time(),
        'ops_count': 0
    }

    try:
        while True:
            message = await websocket.receive_json()
            
            # Route operation to appropriate handler
            operation_type = message.get('operation')
            if operation_type == 'vram':
                response = await server.handle_vram_operation(message)
            elif operation_type == 'state':
                response = await server.handle_state_operation(message)
            elif operation_type == 'cache':
                response = await server.handle_cache_operation(message)
            else:
                response = {
                    'status': 'error',
                    'message': 'Unknown operation type'
                }
            
            # Update statistics
            server.ops_counter += 1
            server.active_sessions[session_id]['ops_count'] += 1
            
            # Send response
            await websocket.send_json(response)
                
    except Exception as e:
        print(f"WebSocket error: {e}")
    finally:
        # Cleanup on disconnect
        if session_id in server.active_connections:
            del server.active_connections[session_id]
        if session_id in server.active_sessions:
            del server.active_sessions[session_id]

# For running directly (development)
if __name__ == "__main__":
    import uvicorn
    uvicorn.run("virtual_gpu_server_http:app", host="0.0.0.0", port=7860, reload=True)

@app.get("/api/v1/status")
async def get_status():
    """Get server status"""
    return {"status": "ok", "message": "Virtual GPU Server is running"}