app.py

from langfuse import Langfuse
from langfuse.decorators import observe, langfuse_context

from config.config import settings
import os

# Initialize Langfuse
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-9f2c32d2-266f-421d-9b87-51377f0a268c"
os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-229e10c5-6210-4a4b-a432-0f17bc66e56c"
os.environ["LANGFUSE_HOST"] = "https://chris4k-langfuse-template-space.hf.space"  # 🇪🇺 EU region

try:
    langfuse = Langfuse()
catch:
    print("Langfuse Offline")




# model_manager.py
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from llama_cpp import Llama
from typing import Optional, Dict
import logging
from functools import lru_cache
from config.config import GenerationConfig, ModelConfig


class ModelManager:
    def __init__(self, device: Optional[str] = None):
        self.logger = logging.getLogger(__name__)
        self.device = device or ("cuda" if torch.cuda.is_available() else "cpu")
        self.models: Dict[str, Any] = {}
        self.tokenizers: Dict[str, Any] = {}

    def load_model(self, model_id: str, model_path: str, model_type: str, config: ModelConfig) -> None:
        """Load a model with specified configuration."""
        try:
            ##could be differnt models, so we can use a factory pattern to load the correct model - textgen, llama, gguf, text2video, text2image etc.
            if model_type == "llama":
                self.tokenizers[model_id] = AutoTokenizer.from_pretrained(
                    model_path,
                    padding_side='left',
                    trust_remote_code=True,
                    **config.tokenizer_kwargs
                )
                if self.tokenizers[model_id].pad_token is None:
                    self.tokenizers[model_id].pad_token = self.tokenizers[model_id].eos_token
                
                self.models[model_id] = AutoModelForCausalLM.from_pretrained(
                    model_path,
                    device_map="auto",
                    trust_remote_code=True,
                    **config.model_kwargs
                )
            elif model_type == "gguf":
                #TODO load the model first from the cache, if not found load the model and save it in the cache
                #from huggingface_hub import hf_hub_download
                #prm_model_path = hf_hub_download(
                #    repo_id="tensorblock/Llama3.1-8B-PRM-Mistral-Data-GGUF",
                #    filename="Llama3.1-8B-PRM-Mistral-Data-Q4_K_M.gguf"
                #)

                
                self.models[model_id] = self._load_quantized_model(
                    model_path,
                    **config.quantization_kwargs
                )
        except Exception as e:
            self.logger.error(f"Failed to load model {model_id}: {str(e)}")
            raise

    def unload_model(self, model_id: str) -> None:
        """Unload a model and free resources."""
        if model_id in self.models:
            del self.models[model_id]
            if model_id in self.tokenizers:
                del self.tokenizers[model_id]
            torch.cuda.empty_cache()

    def _load_quantized_model(self, model_path: str, **kwargs) -> Llama:
        """Load a quantized GGUF model."""
        try:
            n_gpu_layers = -1 if torch.cuda.is_available() else 0
            model = Llama(
                model_path=model_path,
                n_ctx=kwargs.get('n_ctx', 2048),
                n_batch=kwargs.get('n_batch', 512),
                n_gpu_layers=kwargs.get('n_gpu_layers', n_gpu_layers),
                verbose=kwargs.get('verbose', False)
            )
            return model
        except Exception as e:
            self.logger.error(f"Failed to load GGUF model: {str(e)}")
            raise


# cache.py 
from functools import lru_cache
from typing import Tuple, Any

# TODO explain howto use the cache
class ResponseCache:
    def __init__(self, cache_size: int = 1000):
        self.cache_size = cache_size
        self._initialize_cache()

    def _initialize_cache(self):
        @lru_cache(maxsize=self.cache_size)
        def cached_response(prompt: str, config_hash: str) -> Tuple[str, float]:
            pass
        self.get_cached_response = cached_response

    def cache_response(self, prompt: str, config: GenerationConfig, response: str, score: float) -> None:
        config_hash = hash(str(config.__dict__))
        self.get_cached_response(prompt, str(config_hash))
        
    def get_response(self, prompt: str, config: GenerationConfig) -> Optional[Tuple[str, float]]:
        config_hash = hash(str(config.__dict__))
        return self.get_cached_response(prompt, str(config_hash))


# batch_processor.py
from typing import List, Dict
import asyncio

#TODO explain how to use the batch processor
class BatchProcessor:
    def __init__(self, max_batch_size: int = 32, max_wait_time: float = 0.1):
        self.max_batch_size = max_batch_size
        self.max_wait_time = max_wait_time
        self.pending_requests: List[Dict] = []
        self.lock = asyncio.Lock()

    async def add_request(self, request: Dict) -> Any:
        async with self.lock:
            self.pending_requests.append(request)
            if len(self.pending_requests) >= self.max_batch_size:
                return await self._process_batch()
            else:
                await asyncio.sleep(self.max_wait_time)
                if self.pending_requests:
                    return await self._process_batch()

    async def _process_batch(self) -> List[Any]:
        batch = self.pending_requests[:self.max_batch_size]
        self.pending_requests = self.pending_requests[self.max_batch_size:]
        # TODO implement the batch processing logic
        return batch



# base_generator.py
from abc import ABC, abstractmethod
from typing import AsyncGenerator, Dict, Any, Optional, List, Tuple
from dataclasses import dataclass
from logging import getLogger 


from config.config import GenerationConfig, ModelConfig

class BaseGenerator(ABC):
    """Base class for all generator implementations."""
    
    def __init__(
        self,
        model_name: str,
        device: Optional[str] = None,
        default_generation_config: Optional[GenerationConfig] = None,
        model_config: Optional[ModelConfig] = None,
        cache_size: int = 1000,
        max_batch_size: int = 32
    ):
        self.logger = getLogger(__name__)
        self.model_manager = ModelManager(device)
        self.cache = ResponseCache(cache_size)
        self.batch_processor = BatchProcessor(max_batch_size)
        self.health_check = HealthCheck()
        
        self.default_config = default_generation_config or GenerationConfig()
        self.model_config = model_config or ModelConfig()
        
    @abstractmethod
    async def generate_stream(
        self,
        prompt: str,
        config: Optional[GenerationConfig] = None
    ) -> AsyncGenerator[str, None]:
        pass
        
    @abstractmethod
    def _get_generation_kwargs(self, config: GenerationConfig) -> Dict[str, Any]:
        pass
        
    @abstractmethod
    def generate(self, prompt: str, model_kwargs: Dict[str, Any], **kwargs) -> str:
        pass

# strategy.py
#TODO UPDATE Paths
from abc import ABC, abstractmethod
from typing import List, Tuple

class GenerationStrategy(ABC):
    """Base class for generation strategies."""
    
    @abstractmethod
    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], **kwargs) -> str:
        pass

class DefaultStrategy(GenerationStrategy):
    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], **kwargs) -> str:
        input_ids = generator.tokenizer(prompt, return_tensors="pt").input_ids.to(generator.device)
        output = generator.model.generate(input_ids, **model_kwargs)
        return generator.tokenizer.decode(output[0], skip_special_tokens=True)

class MajorityVotingStrategy(GenerationStrategy):
    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], num_samples: int = 5, **kwargs) -> str:
        outputs = []
        for _ in range(num_samples):
            input_ids = generator.tokenizer(prompt, return_tensors="pt").input_ids.to(generator.device)
            output = generator.model.generate(input_ids, **model_kwargs)
            outputs.append(generator.tokenizer.decode(output[0], skip_special_tokens=True))
        return max(set(outputs), key=outputs.count)

class BestOfN(GenerationStrategy):
    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], num_samples: int = 5, **kwargs) -> str:
            scored_outputs = []
            for _ in range(num_samples):
                input_ids = self.llama_tokenizer(prompt, return_tensors="pt").input_ids.to(self.device)
                output = self.llama_model.generate(input_ids, **model_kwargs)
                response = self.llama_tokenizer.decode(output[0], skip_special_tokens=True)
                score = self.prm_model(**self.llama_tokenizer(response, return_tensors="pt").to(self.device)).logits.mean().item()
                scored_outputs.append((response, score))
            return max(scored_outputs, key=lambda x: x[1])[0]
 
class BeamSearch(GenerationStrategy):
    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], num_samples: int = 5, **kwargs) -> str:
            input_ids = self.llama_tokenizer(prompt, return_tensors="pt").input_ids.to(self.device)
            outputs = self.llama_model.generate(
                input_ids,
                num_beams=num_samples,
                num_return_sequences=num_samples,
                **model_kwargs
            )
            return [self.llama_tokenizer.decode(output, skip_special_tokens=True) for output in outputs]

class DVT(GenerationStrategy):
    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], num_samples: int = 5, **kwargs) -> str:
            results = []
            for _ in range(breadth):
                input_ids = self.llama_tokenizer(prompt, return_tensors="pt").input_ids.to(self.device)
                output = self.llama_model.generate(input_ids, **model_kwargs)
                response = self.llama_tokenizer.decode(output[0], skip_special_tokens=True)
                score = self.prm_model(**self.llama_tokenizer(response, return_tensors="pt").to(self.device)).logits.mean().item()
                results.append((response, score))
            
            for _ in range(depth - 1):
                best_responses = sorted(results, key=lambda x: x[1], reverse=True)[:breadth]
                for response, _ in best_responses:
                    input_ids = self.llama_tokenizer(response, return_tensors="pt").input_ids.to(self.device)
                    output = self.llama_model.generate(input_ids, **model_kwargs)
                    extended_response = self.llama_tokenizer.decode(output[0], skip_special_tokens=True)
                    score = self.prm_model(**self.llama_tokenizer(extended_response, return_tensors="pt").to(self.device)).logits.mean().item()
                    results.append((extended_response, score))
            return max(results, key=lambda x: x[1])[0]
        
class COT(GenerationStrategy):
    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], num_samples: int = 5, **kwargs) -> str:
        #TODO implement the chain of thought strategy
        
        return "Not implemented yet"       
     
class ReAct(GenerationStrategy):
    def generate(self, generator: 'BaseGenerator', prompt: str, model_kwargs: Dict[str, Any], num_samples: int = 5, **kwargs) -> str:
        #TODO implement the ReAct framework       
        return "Not implemented yet"
#  Add other strategy implementations...

# prompt_builder.py
from typing import Protocol, List, Tuple
from transformers import AutoTokenizer

class PromptTemplate(Protocol):
    """Protocol for prompt templates."""
    def format(self, context: str, user_input: str, chat_history: List[Tuple[str, str]], **kwargs) -> str:
        pass

class LlamaPromptTemplate:
    def format(self, context: str, user_input: str, chat_history: List[Tuple[str, str]], max_history_turns: int = 1) -> str:
        system_message = f"Please assist based on the following context: {context}"
        prompt = f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system_message}<|eot_id|>"
        
        for user_msg, assistant_msg in chat_history[-max_history_turns:]:
            prompt += f"<|start_header_id|>user<|end_header_id|>\n\n{user_msg}<|eot_id|>"
            prompt += f"<|start_header_id|>assistant<|end_header_id|>\n\n{assistant_msg}<|eot_id|>"
            
        prompt += f"<|start_header_id|>user<|end_header_id|>\n\n{user_input}<|eot_id|>"
        prompt += "<|start_header_id|>assistant<|end_header_id|>\n\n"
        return prompt

class TransformersPromptTemplate:
    def __init__(self, model_path: str):
        self.tokenizer = AutoTokenizer.from_pretrained(model_path)
        
    def format(self, context: str, user_input: str, chat_history: List[Tuple[str, str]], **kwargs) -> str:
        messages = [
            {
                "role": "system",
                "content": f"Please assist based on the following context: {context}",
            }
        ]
        
        for user_msg, assistant_msg in chat_history:
            messages.extend([
                {"role": "user", "content": user_msg},
                {"role": "assistant", "content": assistant_msg}
            ])
            
        messages.append({"role": "user", "content": user_input})
        
        tokenized_chat = self.tokenizer.apply_chat_template(
            messages,
            tokenize=False,
            add_generation_prompt=True
        )
        return tokenized_chat

# health_check.py
import psutil
from dataclasses import dataclass
from typing import Dict, Any

@dataclass
class HealthStatus:
    status: str
    gpu_memory: Dict[str, float]
    cpu_usage: float
    ram_usage: float
    model_status: Dict[str, str]

class HealthCheck:
    @staticmethod
    def check_gpu_memory() -> Dict[str, float]:
        if torch.cuda.is_available():
            return {
                f"gpu_{i}": torch.cuda.memory_allocated(i) / 1024**3
                for i in range(torch.cuda.device_count())
            }
        return {}

    @staticmethod
    def check_system_resources() -> HealthStatus:
        return HealthStatus(
            status="healthy",
            gpu_memory=HealthCheck.check_gpu_memory(),
            cpu_usage=psutil.cpu_percent(),
            ram_usage=psutil.virtual_memory().percent,
            #TODO add more system resources like disk, network, etc.
            model_status={}  # To be filled by the model manager
        )


# llama_generator.py
from config.config import GenerationConfig, ModelConfig

class LlamaGenerator(BaseGenerator):
    def __init__(
        self,
        llama_model_name: str,
        prm_model_path: str,
        device: Optional[str] = None,
        default_generation_config: Optional[GenerationConfig] = None,
        model_config: Optional[ModelConfig] = None,
        cache_size: int = 1000,
        max_batch_size: int = 32
    ):
        super().__init__(
            llama_model_name,
            device,
            default_generation_config,
            model_config,
            cache_size,
            max_batch_size
        )
        
        # Initialize models
        self.model_manager.load_model(
            "llama",
            llama_model_name,
            "llama",
            self.model_config
        )
        self.model_manager.load_model(
            "prm",
            prm_model_path,
            "gguf",
            self.model_config
        )
        
        self.prompt_builder = LlamaPromptTemplate()
        self._init_strategies()
        
    def _init_strategies(self):
        self.strategies = {
            "default": DefaultStrategy(),
            "majority_voting": MajorityVotingStrategy(),
            "best_of_n": BestOfN(),
            "beam_search": BeamSearch(),
            "dvts": DVT(),
        }
        
    def _get_generation_kwargs(self, config: GenerationConfig) -> Dict[str, Any]:
        """Get generation kwargs based on config."""
        return {
            key: getattr(config, key)
            for key in [
                "max_new_tokens",
                "temperature",
                "top_p",
                "top_k",
                "repetition_penalty",
                "length_penalty",
                "do_sample"
            ]
            if hasattr(config, key)
        }
        
    def generate(
        self,
        prompt: str,
        model_kwargs: Dict[str, Any],
        strategy: str = "default",
        **kwargs
    ) -> str:
        if strategy not in self.strategies:
            raise ValueError(f"Unknown strategy: {strategy}")
            
        return self.strategies[strategy].generate(
            self,
            prompt,
            model_kwargs,
            **kwargs
        )
    
    def check_health(self) -> HealthStatus:
        """Check the health status of the generator."""
        return self.health_check.check_system_resources() # TODO add model status
		
	
###################
################# 
 
from fastapi import FastAPI, HTTPException, BackgroundTasks, WebSocket, Depends
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import StreamingResponse
from pydantic import BaseModel, Field, ConfigDict
from typing import List, Optional, Dict, Any, AsyncGenerator
import asyncio
import uuid
from datetime import datetime
import json
from huggingface_hub import hf_hub_download
from contextlib import asynccontextmanager

 

class ChatMessage(BaseModel):
    """A single message in the chat history."""
    role: str = Field(
        ..., 
        description="Role of the message sender", 
        examples=["user", "assistant"]
    )
    content: str = Field(..., description="Content of the message")
    
    model_config = ConfigDict(
        json_schema_extra={
            "example": {
                "role": "user",
                "content": "What is the capital of France?"
            }
        }
    )
	
class GenerationConfig(BaseModel):
    """Configuration for text generation."""
    temperature: float = Field(
        0.7,
        ge=0.0,
        le=2.0,
        description="Controls randomness in the output. Higher values (e.g., 0.8) make the output more random, lower values (e.g., 0.2) make it more focused and deterministic."
    )
    max_new_tokens: int = Field(
        100,
        ge=1,
        le=2048,
        description="Maximum number of tokens to generate"
    )
    top_p: float = Field(
        0.9,
        ge=0.0,
        le=1.0,
        description="Nucleus sampling parameter. Only tokens with cumulative probability < top_p are considered."
    )
    top_k: int = Field(
        50,
        ge=0,
        description="Only consider the top k tokens for text generation"
    )
    strategy: str = Field(
        "default",
        description="Generation strategy to use",
        examples=["default", "majority_voting", "best_of_n", "beam_search", "dvts"]
    )
    num_samples: int = Field(
        5,
        ge=1,
        le=10,
        description="Number of samples to generate (used in majority_voting and best_of_n strategies)"
    )

class GenerationRequest(BaseModel):
    """Request model for text generation."""
    context: Optional[str] = Field(
        None,
        description="Additional context to guide the generation",
        examples=["You are a helpful assistant skilled in Python programming"]
    )
    messages: List[ChatMessage] = Field(
        ...,
        description="Chat history including the current message",
        min_items=1
    )
    config: Optional[GenerationConfig] = Field(
        None,
        description="Generation configuration parameters"
    )
    stream: bool = Field(
        False,
        description="Whether to stream the response token by token"
    )
    
    model_config = ConfigDict(
        json_schema_extra={
            "example": {
                "context": "You are a helpful assistant",
                "messages": [
                    {"role": "user", "content": "What is the capital of France?"}
                ],
                "config": {
                    "temperature": 0.7,
                    "max_new_tokens": 100
                },
                "stream": False
            }
        }
    )

class GenerationResponse(BaseModel):
    """Response model for text generation."""
    id: str = Field(..., description="Unique generation ID")
    content: str = Field(..., description="Generated text content")
    created_at: datetime = Field(
        default_factory=datetime.now,
        description="Timestamp of generation"
    )
	
	
# Model and cache management
async def get_prm_model_path():
    """Download and cache the PRM model."""
    return await asyncio.to_thread(
        hf_hub_download,
        repo_id="tensorblock/Llama3.1-8B-PRM-Mistral-Data-GGUF",
        filename="Llama3.1-8B-PRM-Mistral-Data-Q4_K_M.gguf"
    )

@asynccontextmanager
async def lifespan(app: FastAPI):
    """Lifecycle management for the FastAPI application."""
    # Startup: Initialize generator
    global generator
    try:
        prm_model_path = await get_prm_model_path()
        generator = LlamaGenerator(
            llama_model_name="meta-llama/Llama-3.2-1B-Instruct",
            prm_model_path=prm_model_path,
            default_generation_config=GenerationConfig(
                max_new_tokens=100,
                temperature=0.7
            )
        )
        yield
    finally:
        # Shutdown: Clean up resources
        if generator:
            await asyncio.to_thread(generator.cleanup)

# FastAPI application
app = FastAPI(
    title="Inference Deluxe Service",
    description="""
    A service for generating text using LLaMA models with various generation strategies.
    
    Generation Strategies:
    - default: Standard autoregressive generation
    - majority_voting: Generates multiple responses and selects the most common one
    - best_of_n: Generates multiple responses and selects the best based on a scoring metric
    - beam_search: Uses beam search for more coherent text generation
    - dvts: Dynamic vocabulary tree search for efficient generation
    """,
    version="1.0.0",
    lifespan=lifespan
)

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

async def get_generator():
    """Dependency to get the generator instance."""
    if not generator:
        raise HTTPException(
            status_code=503,
            detail="Generator not initialized"
        )
    return generator

@app.post(
    "/generate",
    response_model=GenerationResponse,
    tags=["generation"],
    summary="Generate text response",
    response_description="Generated text with unique identifier"
)
async def generate(
    request: GenerationRequest,
    generator: Any = Depends(get_generator)
):
    """
    Generate a text response based on the provided context and chat history.
    
    The generation process can be customized using various parameters in the config:
    - temperature: Controls randomness (0.0 to 2.0)
    - max_new_tokens: Maximum length of generated text
    - top_p: Nucleus sampling parameter
    - top_k: Top-k sampling parameter
    - strategy: Generation strategy to use
    - num_samples: Number of samples for applicable strategies
    
    Generation Strategies:
    - default: Standard generation
    - majority_voting: Generates multiple responses and uses the most common one
    - best_of_n: Generates multiple responses and picks the best
    - beam_search: Uses beam search for coherent generation
    - dvts: Dynamic vocabulary tree search
    """
    try:
        chat_history = [(msg.role, msg.content) for msg in request.messages[:-1]]
        user_input = request.messages[-1].content
        
        config = request.config or GenerationConfig()
        
        response = await asyncio.to_thread(
            generator.generate_with_context,
            context=request.context or "",
            user_input=user_input,
            chat_history=chat_history,
            config=config
        )
        
        return GenerationResponse(
            id=str(uuid.uuid4()),
            content=response
        )
    except Exception as e:
        raise HTTPException(status_code=500, detail=str(e))

@app.websocket("/generate/stream")
async def generate_stream(
    websocket: WebSocket,
    generator: Any = Depends(get_generator)
):
    """
    Stream generated text tokens over a WebSocket connection.
    
    The stream sends JSON messages with the following format:
    - During generation: {"token": "generated_token", "finished": false}
    - End of generation: {"token": "", "finished": true}
    - Error: {"error": "error_message"}
    """
    await websocket.accept()
    
    try:
        while True:
            request_data = await websocket.receive_text()
            request = GenerationRequest.parse_raw(request_data)
            
            chat_history = [(msg.role, msg.content) for msg in request.messages[:-1]]
            user_input = request.messages[-1].content
            
            config = request.config or GenerationConfig()
            
            async for token in generator.generate_stream(
                prompt=generator._construct_prompt(
                    context=request.context or "",
                    user_input=user_input,
                    chat_history=chat_history
                ),
                config=config
            ):
                await websocket.send_text(json.dumps({
                    "token": token,
                    "finished": False
                }))
            
            await websocket.send_text(json.dumps({
                "token": "",
                "finished": True
            }))
            
    except Exception as e:
        await websocket.send_text(json.dumps({
            "error": str(e)
        }))
    finally:
        await websocket.close()

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000)