Spaces:

tech-daskalos
/

CyberSecChatbot

Paused

Andrew McCracken

Claude

Fix remaining f-string syntax errors

8f62d83 about 1 year ago

19.3 kB

from fastapi import FastAPI, HTTPException, WebSocket, WebSocketDisconnect, BackgroundTasks
from fastapi.responses import StreamingResponse, JSONResponse, FileResponse
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
from typing import Optional, List, Dict, Any
from datetime import datetime
import asyncio
import json
import uuid
import os
import sqlite3
from contextlib import asynccontextmanager
import queue
import threading

# Import our handlers
from llm_handler import CybersecurityLLM
from knowledge_base import RAGCybersecurityLLM
from optimisations import PerformanceOptimizer, MemoryManager


class ModelPool:
    """Thread-safe pool of model instances for concurrent request handling"""

    def __init__(self, pool_size: int, model_class, **model_kwargs):
        """
        Initialize a pool of model instances
        Args:
            pool_size: Number of model instances to create
            model_class: The model class to instantiate (CybersecurityLLM or RAGCybersecurityLLM)
            **model_kwargs: Arguments to pass to each model instance
        """
        self.pool_size = pool_size
        self.model_class = model_class
        self.model_kwargs = model_kwargs
        self.pool = queue.Queue(maxsize=pool_size)
        self.lock = threading.Lock()
        self._initialize_pool()

    def _initialize_pool(self):
        """Create and add model instances to the pool"""
        print(f"🔄 Initializing model pool with {self.pool_size} instances...")
        for i in range(self.pool_size):
            print(f"   Loading model instance {i + 1}/{self.pool_size}...")
            model = self.model_class(**self.model_kwargs)
            self.pool.put(model)
        print(f"✅ Model pool ready with {self.pool_size} instances")

    async def get_model(self, timeout: float = 30.0):
        """
        Get an available model from the pool (async)
        Args:
            timeout: Maximum time to wait for an available model
        Returns:
            Model instance
        Raises:
            HTTPException: If no model available within timeout
        """
        start_time = asyncio.get_event_loop().time()

        while True:
            try:
                # Try to get a model without blocking
                model = self.pool.get_nowait()
                return model
            except queue.Empty:
                # Check timeout
                if asyncio.get_event_loop().time() - start_time > timeout:
                    raise HTTPException(
                        status_code=503,
                        detail=f"All {self.pool_size} model instances are busy. Please try again later."
                    )

                # Wait a bit before trying again
                await asyncio.sleep(0.1)

    def return_model(self, model):
        """Return a model to the pool"""
        self.pool.put(model)

    def get_stats(self) -> Dict[str, Any]:
        """Get pool statistics"""
        return {
            "pool_size": self.pool_size,
            "available": self.pool.qsize(),
            "in_use": self.pool_size - self.pool.qsize()
        }

# Configuration from environment variables
MODEL_REPO = os.getenv("MODEL_REPO", "daskalos-apps/phi4-cybersec-Q4_K_M")
MODEL_FILENAME = os.getenv("MODEL_FILENAME", "phi4-mini-instruct-Q4_K_M.gguf")
USE_RAG = os.getenv("USE_RAG", "true").lower() == "true"
CACHE_ENABLED = os.getenv("CACHE_ENABLED", "true").lower() == "true"
MODEL_POOL_SIZE = int(os.getenv("MODEL_POOL_SIZE", "10"))  # Number of concurrent model instances

# Global instances
llm_instance = None
optimizer = None
memory_manager = None
model_pool = None  # Pool of model instances for concurrent processing

# Database setup
# Support multiple deployment platforms: /data (HF Spaces), /app/data (Render/Railway), or local
if os.path.exists("/data"):
    DB_PATH = "/data/interactions.db"
elif os.path.exists("/app/data"):
    DB_PATH = "/app/data/interactions.db"
else:
    DB_PATH = "interactions.db"

def init_db():
    """Initialize SQLite database for interaction tracking"""
    conn = sqlite3.connect(DB_PATH)
    cursor = conn.cursor()
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS interactions (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            timestamp TEXT NOT NULL,
            session_id TEXT,
            message TEXT,
            response_length INTEGER
        )
    """)
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS interaction_count (
            id INTEGER PRIMARY KEY CHECK (id = 1),
            count INTEGER DEFAULT 0
        )
    """)
    cursor.execute("INSERT OR IGNORE INTO interaction_count (id, count) VALUES (1, 0)")
    conn.commit()
    conn.close()

# Database lock for thread-safe operations
db_lock = threading.Lock()

def increment_interaction():
    """Increment interaction count and return new count (thread-safe)"""
    with db_lock:
        conn = sqlite3.connect(DB_PATH, check_same_thread=False, timeout=10.0)
        cursor = conn.cursor()
        cursor.execute("UPDATE interaction_count SET count = count + 1 WHERE id = 1")
        cursor.execute("SELECT count FROM interaction_count WHERE id = 1")
        count = cursor.fetchone()[0]
        conn.commit()
        conn.close()
        return count

def get_interaction_count():
    """Get current interaction count (thread-safe)"""
    with db_lock:
        conn = sqlite3.connect(DB_PATH, check_same_thread=False, timeout=10.0)
        cursor = conn.cursor()
        cursor.execute("SELECT count FROM interaction_count WHERE id = 1")
        count = cursor.fetchone()[0]
        conn.close()
        return count

def log_interaction(session_id: str, message: str, response_length: int):
    """Log interaction details (thread-safe)"""
    with db_lock:
        conn = sqlite3.connect(DB_PATH, check_same_thread=False, timeout=10.0)
        cursor = conn.cursor()
        cursor.execute(
            "INSERT INTO interactions (timestamp, session_id, message, response_length) VALUES (?, ?, ?, ?)",
            (datetime.now().isoformat(), session_id, message, response_length)
        )
        conn.commit()
        conn.close()


@asynccontextmanager
async def lifespan(app: FastAPI):
    """Startup and shutdown events"""
    global llm_instance, optimizer, memory_manager, model_pool

    # Startup
    print(f"🚀 Loading model from Hugging Face: {MODEL_REPO}")
    print(f"📊 Concurrent instances: {MODEL_POOL_SIZE}")

    # Initialize database
    init_db()
    print("✅ Database initialized")

    try:
        # Initialize model pool for concurrent requests
        model_class = RAGCybersecurityLLM if USE_RAG else CybersecurityLLM
        model_pool = ModelPool(
            pool_size=MODEL_POOL_SIZE,
            model_class=model_class,
            repo_id=MODEL_REPO,
            filename=MODEL_FILENAME
        )

        # Keep one instance for backward compatibility (health checks, etc.)
        llm_instance = model_class(
            repo_id=MODEL_REPO,
            filename=MODEL_FILENAME
        )

        if CACHE_ENABLED:
            optimizer = PerformanceOptimizer()

        memory_manager = MemoryManager()

        print("✅ Cybersecurity Chatbot ready!")
        print(f"📦 Model: {MODEL_REPO}")
        print(f"💾 Size: {llm_instance.get_model_info()['size_mb']:.2f} MB")
        print(f"🔧 RAG: {'Enabled' if USE_RAG else 'Disabled'}")
        print(f"⚡ Cache: {'Enabled' if CACHE_ENABLED else 'Disabled'}")
        print(f"👥 Concurrent capacity: {MODEL_POOL_SIZE} users")

    except Exception as e:
        print(f"❌ Failed to load model: {e}")
        raise

    yield

    # Shutdown
    print("👋 Shutting down...")


# Initialize FastAPI with lifespan
app = FastAPI(
    title="Cybersecurity Training Chatbot API",
    description="AI-powered cybersecurity guidance using Phi-4 from Hugging Face",
    version="2.0.0",
    lifespan=lifespan
)

# CORS for web interface
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)


# Request/Response models
class ChatRequest(BaseModel):
    message: str = Field(..., description="User's security question")
    session_id: Optional[str] = Field(None, description="Session ID for conversation continuity")
    max_tokens: Optional[int] = Field(256, description="Maximum response length")
    temperature: Optional[float] = Field(0.7, description="Response creativity (0-1)")
    use_rag: Optional[bool] = Field(True, description="Use RAG for enhanced accuracy")
    use_cache: Optional[bool] = Field(True, description="Use cached responses if available")


class ChatResponse(BaseModel):
    response: str
    session_id: str
    timestamp: str
    model: str
    tokens_used: Optional[int] = None
    cached: bool = False
    sources: Optional[List[str]] = None


class ModelInfo(BaseModel):
    repo_id: str
    filename: str
    size_mb: float
    rag_enabled: bool
    cache_enabled: bool


# Session management (thread-safe for concurrent users)
sessions: Dict[str, List[Dict[str, Any]]] = {}
sessions_lock = threading.Lock()  # Protect sessions dict from concurrent modifications


@app.get("/", response_model=Dict[str, str])
async def root():
    """API root endpoint"""
    return {
        "message": "Cybersecurity Training Chatbot API",
        "model": MODEL_REPO,
        "documentation": "/docs",
        "health": "/health"
    }


@app.get("/health")
async def health_check():
    """Check API and model health"""
    if llm_instance is None:
        raise HTTPException(status_code=503, detail="Model not loaded")

    memory_status = memory_manager.check_memory() if memory_manager else {}
    pool_status = model_pool.get_stats() if model_pool else {"pool_size": 0, "available": 0, "in_use": 0}

    return {
        "status": "healthy",
        "model": MODEL_REPO,
        "version": "2.0.0",
        "memory": memory_status,
        "cache_enabled": CACHE_ENABLED,
        "rag_enabled": USE_RAG,
        "concurrent_capacity": pool_status
    }


@app.get("/model/info", response_model=ModelInfo)
async def model_info():
    """Get information about the loaded model"""
    if llm_instance is None:
        raise HTTPException(status_code=503, detail="Model not loaded")

    info = llm_instance.get_model_info()

    return ModelInfo(
        repo_id=info['repo_id'],
        filename=info['filename'],
        size_mb=info['size_mb'],
        rag_enabled=USE_RAG,
        cache_enabled=CACHE_ENABLED
    )


@app.post("/chat", response_model=ChatResponse)
async def chat(request: ChatRequest):
    """Main chat endpoint"""
    if llm_instance is None:
        raise HTTPException(status_code=503, detail="Model not loaded")

    try:
        # Generate or get session ID
        session_id = request.session_id or str(uuid.uuid4())

        # Initialize session if new (thread-safe)
        with sessions_lock:
            if session_id not in sessions:
                sessions[session_id] = []

            # Store user message
            sessions[session_id].append({
                "role": "user",
                "content": request.message,
                "timestamp": datetime.now().isoformat()
            })

        # Check cache if enabled
        cached = False
        response_text = None
        sources = None

        if CACHE_ENABLED and request.use_cache and optimizer:
            cached_response = optimizer.get_cached_response(request.message)
            if cached_response:
                response_text = cached_response
                cached = True

        # Generate response if not cached
        if response_text is None:
            if USE_RAG and hasattr(llm_instance, 'generate_with_rag'):
                result = llm_instance.generate_with_rag(
                    request.message,
                    max_tokens=request.max_tokens,
                    use_rag=request.use_rag
                )
                sources = result.get('sources', [])
            else:
                result = llm_instance.generate(
                    request.message,
                    max_tokens=request.max_tokens,
                    temperature=request.temperature
                )

            response_text = result["response"]

            # Cache the response
            if CACHE_ENABLED and optimizer and request.use_cache:
                optimizer.cache_response(request.message, response_text)

        # Store assistant response (thread-safe)
        with sessions_lock:
            sessions[session_id].append({
                "role": "assistant",
                "content": response_text,
                "timestamp": datetime.now().isoformat()
            })

            # Limit session history
            if len(sessions[session_id]) > 20:
                sessions[session_id] = sessions[session_id][-20:]

        # Check memory usage
        if memory_manager:
            memory_manager.optimize_if_needed()

        return ChatResponse(
            response=response_text,
            session_id=session_id,
            timestamp=datetime.now().isoformat(),
            model=MODEL_REPO,
            cached=cached,
            sources=sources
        )

    except Exception as e:
        logger.error(f"Chat error: {e}")
        raise HTTPException(status_code=500, detail=str(e))


@app.post("/chat/stream")
async def chat_stream(request: ChatRequest):
    """Streaming chat endpoint with concurrent request support"""
    if model_pool is None:
        raise HTTPException(status_code=503, detail="Model pool not initialized")

    # Track interaction
    count = increment_interaction()
    session_id = request.session_id or str(uuid.uuid4())

    async def generate():
        model = None
        try:
            full_response = ""

            # Get a model from the pool (will wait if all busy)
            model = await model_pool.get_model(timeout=60.0)

            # Send initial metadata with pool stats
            pool_stats = model_pool.get_stats()
            start_data = {
                'type': 'start',
                'session_id': session_id,
                'model': MODEL_REPO,
                'interaction_count': count,
                'pool_available': pool_stats['available']
            }
            yield f"data: {json.dumps(start_data)}\n\n"

            # Stream tokens
            for token in model.generate_stream(
                    request.message,
                    max_tokens=request.max_tokens
            ):
                full_response += token
                token_data = {'type': 'token', 'content': token}
                yield f"data: {json.dumps(token_data)}\n\n"
                await asyncio.sleep(0)

            # Log interaction
            log_interaction(session_id, request.message, len(full_response))

            end_data = {'type': 'end'}
            yield f"data: {json.dumps(end_data)}\n\n"

        except Exception as e:
            error_data = {'type': 'error', 'message': str(e)}
            yield f"data: {json.dumps(error_data)}\n\n"
        finally:
            # Always return the model to the pool
            if model is not None:
                model_pool.return_model(model)

    return StreamingResponse(generate(), media_type="text/event-stream")


@app.websocket("/ws/chat")
async def websocket_chat(websocket: WebSocket):
    """WebSocket endpoint for real-time chat"""
    await websocket.accept()

    if llm_instance is None:
        await websocket.send_json({"type": "error", "message": "Model not loaded"})
        await websocket.close()
        return

    session_id = str(uuid.uuid4())

    try:
        await websocket.send_json({
            "type": "connected",
            "session_id": session_id,
            "model": MODEL_REPO
        })

        while True:
            # Receive message
            data = await websocket.receive_text()
            request = json.loads(data)

            # Send acknowledgment
            await websocket.send_json({
                "type": "acknowledged",
                "session_id": session_id
            })

            # Generate and stream response
            full_response = ""

            for token in llm_instance.generate_stream(request.get('message', '')):
                full_response += token
                await websocket.send_json({
                    "type": "token",
                    "content": token
                })
                await asyncio.sleep(0)

            # Send completion
            await websocket.send_json({
                "type": "complete",
                "full_response": full_response
            })

    except WebSocketDisconnect:
        with sessions_lock:
            if session_id in sessions:
                del sessions[session_id]


@app.get("/sessions/{session_id}")
async def get_session(session_id: str):
    """Retrieve session history"""
    with sessions_lock:
        if session_id not in sessions:
            raise HTTPException(status_code=404, detail="Session not found")

        return {
            "session_id": session_id,
            "messages": sessions[session_id].copy(),  # Return copy to avoid race conditions
            "model": MODEL_REPO
        }


@app.delete("/sessions/{session_id}")
async def clear_session(session_id: str):
    """Clear session history"""
    with sessions_lock:
        if session_id in sessions:
            del sessions[session_id]

    return {"message": "Session cleared"}


@app.get("/interactions/count")
async def get_interactions_count():
    """Get total interaction count"""
    count = get_interaction_count()
    return {"count": count}


@app.get("/metrics")
async def get_metrics():
    """Get performance metrics"""
    metrics = {
        "model": MODEL_REPO,
        "sessions_active": len(sessions),
        "total_messages": sum(len(s) for s in sessions.values()),
        "total_interactions": get_interaction_count()
    }

    if optimizer:
        metrics["cache"] = optimizer.get_metrics()

    if memory_manager:
        metrics["memory"] = memory_manager.check_memory()

    return metrics


@app.post("/cache/clear")
async def clear_cache():
    """Clear response cache"""
    if not CACHE_ENABLED or not optimizer:
        raise HTTPException(status_code=400, detail="Cache not enabled")

    optimizer.clear_cache()
    return {"message": "Cache cleared"}


@app.get("/test")
async def serve_test_interface():
    """Serve the test interface HTML"""
    return FileResponse("test_interface.html")


if __name__ == "__main__":
    import uvicorn

    # Configure uvicorn for concurrent request handling
    config = uvicorn.Config(
        app,
        host="0.0.0.0",
        port=8000,
        log_level="info",
        access_log=True,
        workers=1,  # Single worker to share model pool across all requests
        limit_concurrency=100,  # Allow up to 100 concurrent connections
        timeout_keep_alive=120,  # Keep connections alive for streaming
        backlog=2048,  # Queue up to 2048 pending connections
        loop="asyncio"  # Use asyncio event loop for best async performance
    )

    server = uvicorn.Server(config)
    server.run()

	from fastapi import FastAPI, HTTPException, WebSocket, WebSocketDisconnect, BackgroundTasks
	from fastapi.responses import StreamingResponse, JSONResponse, FileResponse
	from fastapi.middleware.cors import CORSMiddleware
	from pydantic import BaseModel, Field
	from typing import Optional, List, Dict, Any
	from datetime import datetime
	import asyncio
	import json
	import uuid
	import os
	import sqlite3
	from contextlib import asynccontextmanager
	import queue
	import threading

	# Import our handlers
	from llm_handler import CybersecurityLLM
	from knowledge_base import RAGCybersecurityLLM
	from optimisations import PerformanceOptimizer, MemoryManager


	class ModelPool:
	"""Thread-safe pool of model instances for concurrent request handling"""

	def __init__(self, pool_size: int, model_class, **model_kwargs):
	"""
	Initialize a pool of model instances

	Args:
	pool_size: Number of model instances to create
	model_class: The model class to instantiate (CybersecurityLLM or RAGCybersecurityLLM)
	**model_kwargs: Arguments to pass to each model instance
	"""
	self.pool_size = pool_size
	self.model_class = model_class
	self.model_kwargs = model_kwargs
	self.pool = queue.Queue(maxsize=pool_size)
	self.lock = threading.Lock()
	self._initialize_pool()

	def _initialize_pool(self):
	"""Create and add model instances to the pool"""
	print(f"🔄 Initializing model pool with {self.pool_size} instances...")
	for i in range(self.pool_size):
	print(f" Loading model instance {i + 1}/{self.pool_size}...")
	model = self.model_class(**self.model_kwargs)
	self.pool.put(model)
	print(f"✅ Model pool ready with {self.pool_size} instances")

	async def get_model(self, timeout: float = 30.0):
	"""
	Get an available model from the pool (async)

	Args:
	timeout: Maximum time to wait for an available model

	Returns:
	Model instance

	Raises:
	HTTPException: If no model available within timeout
	"""
	start_time = asyncio.get_event_loop().time()

	while True:
	try:
	# Try to get a model without blocking
	model = self.pool.get_nowait()
	return model
	except queue.Empty:
	# Check timeout
	if asyncio.get_event_loop().time() - start_time > timeout:
	raise HTTPException(
	status_code=503,
	detail=f"All {self.pool_size} model instances are busy. Please try again later."
	)

	# Wait a bit before trying again
	await asyncio.sleep(0.1)

	def return_model(self, model):
	"""Return a model to the pool"""
	self.pool.put(model)

	def get_stats(self) -> Dict[str, Any]:
	"""Get pool statistics"""
	return {
	"pool_size": self.pool_size,
	"available": self.pool.qsize(),
	"in_use": self.pool_size - self.pool.qsize()
	}

	# Configuration from environment variables
	MODEL_REPO = os.getenv("MODEL_REPO", "daskalos-apps/phi4-cybersec-Q4_K_M")
	MODEL_FILENAME = os.getenv("MODEL_FILENAME", "phi4-mini-instruct-Q4_K_M.gguf")
	USE_RAG = os.getenv("USE_RAG", "true").lower() == "true"
	CACHE_ENABLED = os.getenv("CACHE_ENABLED", "true").lower() == "true"
	MODEL_POOL_SIZE = int(os.getenv("MODEL_POOL_SIZE", "10")) # Number of concurrent model instances

	# Global instances
	llm_instance = None
	optimizer = None
	memory_manager = None
	model_pool = None # Pool of model instances for concurrent processing

	# Database setup
	# Support multiple deployment platforms: /data (HF Spaces), /app/data (Render/Railway), or local
	if os.path.exists("/data"):
	DB_PATH = "/data/interactions.db"
	elif os.path.exists("/app/data"):
	DB_PATH = "/app/data/interactions.db"
	else:
	DB_PATH = "interactions.db"

	def init_db():
	"""Initialize SQLite database for interaction tracking"""
	conn = sqlite3.connect(DB_PATH)
	cursor = conn.cursor()
	cursor.execute("""
	CREATE TABLE IF NOT EXISTS interactions (
	id INTEGER PRIMARY KEY AUTOINCREMENT,
	timestamp TEXT NOT NULL,
	session_id TEXT,
	message TEXT,
	response_length INTEGER
	)
	""")
	cursor.execute("""
	CREATE TABLE IF NOT EXISTS interaction_count (
	id INTEGER PRIMARY KEY CHECK (id = 1),
	count INTEGER DEFAULT 0
	)
	""")
	cursor.execute("INSERT OR IGNORE INTO interaction_count (id, count) VALUES (1, 0)")
	conn.commit()
	conn.close()

	# Database lock for thread-safe operations
	db_lock = threading.Lock()

	def increment_interaction():
	"""Increment interaction count and return new count (thread-safe)"""
	with db_lock:
	conn = sqlite3.connect(DB_PATH, check_same_thread=False, timeout=10.0)
	cursor = conn.cursor()
	cursor.execute("UPDATE interaction_count SET count = count + 1 WHERE id = 1")
	cursor.execute("SELECT count FROM interaction_count WHERE id = 1")
	count = cursor.fetchone()[0]
	conn.commit()
	conn.close()
	return count

	def get_interaction_count():
	"""Get current interaction count (thread-safe)"""
	with db_lock:
	conn = sqlite3.connect(DB_PATH, check_same_thread=False, timeout=10.0)
	cursor = conn.cursor()
	cursor.execute("SELECT count FROM interaction_count WHERE id = 1")
	count = cursor.fetchone()[0]
	conn.close()
	return count

	def log_interaction(session_id: str, message: str, response_length: int):
	"""Log interaction details (thread-safe)"""
	with db_lock:
	conn = sqlite3.connect(DB_PATH, check_same_thread=False, timeout=10.0)
	cursor = conn.cursor()
	cursor.execute(
	"INSERT INTO interactions (timestamp, session_id, message, response_length) VALUES (?, ?, ?, ?)",
	(datetime.now().isoformat(), session_id, message, response_length)
	)
	conn.commit()
	conn.close()


	@asynccontextmanager
	async def lifespan(app: FastAPI):
	"""Startup and shutdown events"""
	global llm_instance, optimizer, memory_manager, model_pool

	# Startup
	print(f"🚀 Loading model from Hugging Face: {MODEL_REPO}")
	print(f"📊 Concurrent instances: {MODEL_POOL_SIZE}")

	# Initialize database
	init_db()
	print("✅ Database initialized")

	try:
	# Initialize model pool for concurrent requests
	model_class = RAGCybersecurityLLM if USE_RAG else CybersecurityLLM
	model_pool = ModelPool(
	pool_size=MODEL_POOL_SIZE,
	model_class=model_class,
	repo_id=MODEL_REPO,
	filename=MODEL_FILENAME
	)

	# Keep one instance for backward compatibility (health checks, etc.)
	llm_instance = model_class(
	repo_id=MODEL_REPO,
	filename=MODEL_FILENAME
	)

	if CACHE_ENABLED:
	optimizer = PerformanceOptimizer()

	memory_manager = MemoryManager()

	print("✅ Cybersecurity Chatbot ready!")
	print(f"📦 Model: {MODEL_REPO}")
	print(f"💾 Size: {llm_instance.get_model_info()['size_mb']:.2f} MB")
	print(f"🔧 RAG: {'Enabled' if USE_RAG else 'Disabled'}")
	print(f"⚡ Cache: {'Enabled' if CACHE_ENABLED else 'Disabled'}")
	print(f"👥 Concurrent capacity: {MODEL_POOL_SIZE} users")

	except Exception as e:
	print(f"❌ Failed to load model: {e}")
	raise

	yield

	# Shutdown
	print("👋 Shutting down...")


	# Initialize FastAPI with lifespan
	app = FastAPI(
	title="Cybersecurity Training Chatbot API",
	description="AI-powered cybersecurity guidance using Phi-4 from Hugging Face",
	version="2.0.0",
	lifespan=lifespan
	)

	# CORS for web interface
	app.add_middleware(
	CORSMiddleware,
	allow_origins=["*"],
	allow_credentials=True,
	allow_methods=["*"],
	allow_headers=["*"],
	)


	# Request/Response models
	class ChatRequest(BaseModel):
	message: str = Field(..., description="User's security question")
	session_id: Optional[str] = Field(None, description="Session ID for conversation continuity")
	max_tokens: Optional[int] = Field(256, description="Maximum response length")
	temperature: Optional[float] = Field(0.7, description="Response creativity (0-1)")
	use_rag: Optional[bool] = Field(True, description="Use RAG for enhanced accuracy")
	use_cache: Optional[bool] = Field(True, description="Use cached responses if available")


	class ChatResponse(BaseModel):
	response: str
	session_id: str
	timestamp: str
	model: str
	tokens_used: Optional[int] = None
	cached: bool = False
	sources: Optional[List[str]] = None


	class ModelInfo(BaseModel):
	repo_id: str
	filename: str
	size_mb: float
	rag_enabled: bool
	cache_enabled: bool


	# Session management (thread-safe for concurrent users)
	sessions: Dict[str, List[Dict[str, Any]]] = {}
	sessions_lock = threading.Lock() # Protect sessions dict from concurrent modifications


	@app.get("/", response_model=Dict[str, str])
	async def root():
	"""API root endpoint"""
	return {
	"message": "Cybersecurity Training Chatbot API",
	"model": MODEL_REPO,
	"documentation": "/docs",
	"health": "/health"
	}


	@app.get("/health")
	async def health_check():
	"""Check API and model health"""
	if llm_instance is None:
	raise HTTPException(status_code=503, detail="Model not loaded")

	memory_status = memory_manager.check_memory() if memory_manager else {}
	pool_status = model_pool.get_stats() if model_pool else {"pool_size": 0, "available": 0, "in_use": 0}

	return {
	"status": "healthy",
	"model": MODEL_REPO,
	"version": "2.0.0",
	"memory": memory_status,
	"cache_enabled": CACHE_ENABLED,
	"rag_enabled": USE_RAG,
	"concurrent_capacity": pool_status
	}


	@app.get("/model/info", response_model=ModelInfo)
	async def model_info():
	"""Get information about the loaded model"""
	if llm_instance is None:
	raise HTTPException(status_code=503, detail="Model not loaded")

	info = llm_instance.get_model_info()

	return ModelInfo(
	repo_id=info['repo_id'],
	filename=info['filename'],
	size_mb=info['size_mb'],
	rag_enabled=USE_RAG,
	cache_enabled=CACHE_ENABLED
	)


	@app.post("/chat", response_model=ChatResponse)
	async def chat(request: ChatRequest):
	"""Main chat endpoint"""
	if llm_instance is None:
	raise HTTPException(status_code=503, detail="Model not loaded")

	try:
	# Generate or get session ID
	session_id = request.session_id or str(uuid.uuid4())

	# Initialize session if new (thread-safe)
	with sessions_lock:
	if session_id not in sessions:
	sessions[session_id] = []

	# Store user message
	sessions[session_id].append({
	"role": "user",
	"content": request.message,
	"timestamp": datetime.now().isoformat()
	})

	# Check cache if enabled
	cached = False
	response_text = None
	sources = None

	if CACHE_ENABLED and request.use_cache and optimizer:
	cached_response = optimizer.get_cached_response(request.message)
	if cached_response:
	response_text = cached_response
	cached = True

	# Generate response if not cached
	if response_text is None:
	if USE_RAG and hasattr(llm_instance, 'generate_with_rag'):
	result = llm_instance.generate_with_rag(
	request.message,
	max_tokens=request.max_tokens,
	use_rag=request.use_rag
	)
	sources = result.get('sources', [])
	else:
	result = llm_instance.generate(
	request.message,
	max_tokens=request.max_tokens,
	temperature=request.temperature
	)

	response_text = result["response"]

	# Cache the response
	if CACHE_ENABLED and optimizer and request.use_cache:
	optimizer.cache_response(request.message, response_text)

	# Store assistant response (thread-safe)
	with sessions_lock:
	sessions[session_id].append({
	"role": "assistant",
	"content": response_text,
	"timestamp": datetime.now().isoformat()
	})

	# Limit session history
	if len(sessions[session_id]) > 20:
	sessions[session_id] = sessions[session_id][-20:]

	# Check memory usage
	if memory_manager:
	memory_manager.optimize_if_needed()

	return ChatResponse(
	response=response_text,
	session_id=session_id,
	timestamp=datetime.now().isoformat(),
	model=MODEL_REPO,
	cached=cached,
	sources=sources
	)

	except Exception as e:
	logger.error(f"Chat error: {e}")
	raise HTTPException(status_code=500, detail=str(e))


	@app.post("/chat/stream")
	async def chat_stream(request: ChatRequest):
	"""Streaming chat endpoint with concurrent request support"""
	if model_pool is None:
	raise HTTPException(status_code=503, detail="Model pool not initialized")

	# Track interaction
	count = increment_interaction()
	session_id = request.session_id or str(uuid.uuid4())

	async def generate():
	model = None
	try:
	full_response = ""

	# Get a model from the pool (will wait if all busy)
	model = await model_pool.get_model(timeout=60.0)

	# Send initial metadata with pool stats
	pool_stats = model_pool.get_stats()
	start_data = {
	'type': 'start',
	'session_id': session_id,
	'model': MODEL_REPO,
	'interaction_count': count,
	'pool_available': pool_stats['available']
	}
	yield f"data: {json.dumps(start_data)}\n\n"

	# Stream tokens
	for token in model.generate_stream(
	request.message,
	max_tokens=request.max_tokens
	):
	full_response += token
	token_data = {'type': 'token', 'content': token}
	yield f"data: {json.dumps(token_data)}\n\n"
	await asyncio.sleep(0)

	# Log interaction
	log_interaction(session_id, request.message, len(full_response))

	end_data = {'type': 'end'}
	yield f"data: {json.dumps(end_data)}\n\n"

	except Exception as e:
	error_data = {'type': 'error', 'message': str(e)}
	yield f"data: {json.dumps(error_data)}\n\n"
	finally:
	# Always return the model to the pool
	if model is not None:
	model_pool.return_model(model)

	return StreamingResponse(generate(), media_type="text/event-stream")


	@app.websocket("/ws/chat")
	async def websocket_chat(websocket: WebSocket):
	"""WebSocket endpoint for real-time chat"""
	await websocket.accept()

	if llm_instance is None:
	await websocket.send_json({"type": "error", "message": "Model not loaded"})
	await websocket.close()
	return

	session_id = str(uuid.uuid4())

	try:
	await websocket.send_json({
	"type": "connected",
	"session_id": session_id,
	"model": MODEL_REPO
	})

	while True:
	# Receive message
	data = await websocket.receive_text()
	request = json.loads(data)

	# Send acknowledgment
	await websocket.send_json({
	"type": "acknowledged",
	"session_id": session_id
	})

	# Generate and stream response
	full_response = ""

	for token in llm_instance.generate_stream(request.get('message', '')):
	full_response += token
	await websocket.send_json({
	"type": "token",
	"content": token
	})
	await asyncio.sleep(0)

	# Send completion
	await websocket.send_json({
	"type": "complete",
	"full_response": full_response
	})

	except WebSocketDisconnect:
	with sessions_lock:
	if session_id in sessions:
	del sessions[session_id]


	@app.get("/sessions/{session_id}")
	async def get_session(session_id: str):
	"""Retrieve session history"""
	with sessions_lock:
	if session_id not in sessions:
	raise HTTPException(status_code=404, detail="Session not found")

	return {
	"session_id": session_id,
	"messages": sessions[session_id].copy(), # Return copy to avoid race conditions
	"model": MODEL_REPO
	}


	@app.delete("/sessions/{session_id}")
	async def clear_session(session_id: str):
	"""Clear session history"""
	with sessions_lock:
	if session_id in sessions:
	del sessions[session_id]

	return {"message": "Session cleared"}


	@app.get("/interactions/count")
	async def get_interactions_count():
	"""Get total interaction count"""
	count = get_interaction_count()
	return {"count": count}


	@app.get("/metrics")
	async def get_metrics():
	"""Get performance metrics"""
	metrics = {
	"model": MODEL_REPO,
	"sessions_active": len(sessions),
	"total_messages": sum(len(s) for s in sessions.values()),
	"total_interactions": get_interaction_count()
	}

	if optimizer:
	metrics["cache"] = optimizer.get_metrics()

	if memory_manager:
	metrics["memory"] = memory_manager.check_memory()

	return metrics


	@app.post("/cache/clear")
	async def clear_cache():
	"""Clear response cache"""
	if not CACHE_ENABLED or not optimizer:
	raise HTTPException(status_code=400, detail="Cache not enabled")

	optimizer.clear_cache()
	return {"message": "Cache cleared"}


	@app.get("/test")
	async def serve_test_interface():
	"""Serve the test interface HTML"""
	return FileResponse("test_interface.html")


	if __name__ == "__main__":
	import uvicorn

	# Configure uvicorn for concurrent request handling
	config = uvicorn.Config(
	app,
	host="0.0.0.0",
	port=8000,
	log_level="info",
	access_log=True,
	workers=1, # Single worker to share model pool across all requests
	limit_concurrency=100, # Allow up to 100 concurrent connections
	timeout_keep_alive=120, # Keep connections alive for streaming
	backlog=2048, # Queue up to 2048 pending connections
	loop="asyncio" # Use asyncio event loop for best async performance
	)

	server = uvicorn.Server(config)
	server.run()