main.py

import os
import re
import random
import string
import uuid
import json
import logging
import asyncio
import time
from collections import defaultdict
from typing import List, Dict, Any, Optional, Union, AsyncGenerator
from datetime import datetime

from aiohttp import ClientSession, ClientResponseError
from fastapi import FastAPI, HTTPException, Request, Depends, Header
from fastapi.responses import JSONResponse, StreamingResponse
from pydantic import BaseModel

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
    handlers=[logging.StreamHandler()]
)
logger = logging.getLogger(__name__)

# Load environment variables
API_KEYS = os.getenv('API_KEYS', '').split(',')  # Comma-separated API keys
RATE_LIMIT = int(os.getenv('RATE_LIMIT', '60'))  # Requests per minute

if not API_KEYS or API_KEYS == ['']:
    logger.error("No API keys found. Please set the API_KEYS environment variable.")
    raise Exception("API_KEYS environment variable not set.")

# Simple in-memory rate limiter based solely on IP addresses
rate_limit_store = defaultdict(lambda: {"count": 0, "timestamp": time.time()})

# Define cleanup interval and window
CLEANUP_INTERVAL = 60  # seconds
RATE_LIMIT_WINDOW = 60  # seconds

class ImageResponse:
    def __init__(self, images: Union[str, List[str]], alt: str = "Generated Image"):
        if isinstance(images, str):
            images = [images]
        self.images = images
        self.alt = alt

class Blackbox:
    label = "Blackbox AI"
    url = "https://www.blackbox.ai"
    api_endpoint = "https://www.blackbox.ai/api/chat"
    working = True
    supports_gpt_4 = True
    supports_stream = True
    supports_system_message = True
    supports_message_history = True

    default_model = 'blackboxai'
    image_models = ['ImageGeneration']
    models = [
        default_model,
        'blackboxai-pro',
        *image_models,
        "llama-3.1-8b",
        'llama-3.1-70b',
        'llama-3.1-405b',
        'gpt-4o',
        'gemini-pro',
        'gemini-1.5-flash',
        'claude-sonnet-3.5',
        'PythonAgent',
        'JavaAgent',
        'JavaScriptAgent',
        'HTMLAgent',
        'GoogleCloudAgent',
        'AndroidDeveloper',
        'SwiftDeveloper',
        'Next.jsAgent',
        'MongoDBAgent',
        'PyTorchAgent',
        'ReactAgent',
        'XcodeAgent',
        'AngularJSAgent',
    ]

    agentMode = {
        'ImageGeneration': {'mode': True, 'id': "ImageGenerationLV45LJp", 'name': "Image Generation"},
    }

    trendingAgentMode = {
        "blackboxai": {},
        "gemini-1.5-flash": {'mode': True, 'id': 'Gemini'},
        "llama-3.1-8b": {'mode': True, 'id': "llama-3.1-8b"},
        'llama-3.1-70b': {'mode': True, 'id': "llama-3.1-70b"},
        'llama-3.1-405b': {'mode': True, 'id': "llama-3.1-405b"},
        'blackboxai-pro': {'mode': True, 'id': "BLACKBOXAI-PRO"},
        'PythonAgent': {'mode': True, 'id': "Python Agent"},
        'JavaAgent': {'mode': True, 'id': "Java Agent"},
        'JavaScriptAgent': {'mode': True, 'id': "JavaScript Agent"},
        'HTMLAgent': {'mode': True, 'id': "HTML Agent"},
        'GoogleCloudAgent': {'mode': True, 'id': "Google Cloud Agent"},
        'AndroidDeveloper': {'mode': True, 'id': "Android Developer"},
        'SwiftDeveloper': {'mode': True, 'id': "Swift Developer"},
        'Next.jsAgent': {'mode': True, 'id': "Next.js Agent"},
        'MongoDBAgent': {'mode': True, 'id': "MongoDB Agent"},
        'PyTorchAgent': {'mode': True, 'id': "PyTorch Agent"},
        'ReactAgent': {'mode': True, 'id': "React Agent"},
        'XcodeAgent': {'mode': True, 'id': "Xcode Agent"},
        'AngularJSAgent': {'mode': True, 'id': "AngularJS Agent"},
    }

    userSelectedModel = {
        "gpt-4o": "gpt-4o",
        "gemini-pro": "gemini-pro",
        'claude-sonnet-3.5': "claude-sonnet-3.5",
    }

    model_prefixes = {
        'gpt-4o': '@GPT-4o',
        'gemini-pro': '@Gemini-PRO',
        'claude-sonnet-3.5': '@Claude-Sonnet-3.5',
        'PythonAgent': '@Python Agent',
        'JavaAgent': '@Java Agent',
        'JavaScriptAgent': '@JavaScript Agent',
        'HTMLAgent': '@HTML Agent',
        'GoogleCloudAgent': '@Google Cloud Agent',
        'AndroidDeveloper': '@Android Developer',
        'SwiftDeveloper': '@Swift Developer',
        'Next.jsAgent': '@Next.js Agent',
        'MongoDBAgent': '@MongoDB Agent',
        'PyTorchAgent': '@PyTorch Agent',
        'ReactAgent': '@React Agent',
        'XcodeAgent': '@Xcode Agent',
        'AngularJSAgent': '@AngularJS Agent',
        'blackboxai-pro': '@BLACKBOXAI-PRO',
        'ImageGeneration': '@Image Generation',
    }

    model_referers = {
        "blackboxai": "/?model=blackboxai",
        "gpt-4o": "/?model=gpt-4o",
        "gemini-pro": "/?model=gemini-pro",
        "claude-sonnet-3.5": "/?model=claude-sonnet-3.5"
    }

    model_aliases = {
        "gemini-flash": "gemini-1.5-flash",
        "claude-3.5-sonnet": "claude-sonnet-3.5",
        "flux": "ImageGeneration",
    }

    @classmethod
    def get_model(cls, model: str) -> str:
        if model in cls.models:
            return model
        elif model in cls.model_aliases:
            return cls.model_aliases[model]
        else:
            return cls.default_model

    @staticmethod
    def generate_random_string(length: int = 7) -> str:
        characters = string.ascii_letters + string.digits
        return ''.join(random.choices(characters, k=length))

    @staticmethod
    def generate_next_action() -> str:
        return uuid.uuid4().hex

    @staticmethod
    def generate_next_router_state_tree() -> str:
        router_state = [
            "",
            {
                "children": [
                    "(chat)",
                    {
                        "children": [
                            "__PAGE__",
                            {}
                        ]
                    }
                ]
            },
            None,
            None,
            True
        ]
        return json.dumps(router_state)

    @staticmethod
    def clean_response(text: str) -> str:
        pattern = r'^\$\@\$v=undefined-rv1\$\@'
        cleaned_text = re.sub(pattern, '', text)
        return cleaned_text

    @classmethod
    async def create_async_generator(
        cls,
        model: str,
        messages: List[Dict[str, str]],
        proxy: Optional[str] = None,
        websearch: bool = False,
        **kwargs
    ) -> AsyncGenerator[Union[str, ImageResponse], None]:
        """
        Creates an asynchronous generator for streaming responses from Blackbox AI.

        Parameters:
            model (str): Model to use for generating responses.
            messages (List[Dict[str, str]]): Message history.
            proxy (Optional[str]): Proxy URL, if needed.
            websearch (bool): Enables or disables web search mode.
            **kwargs: Additional keyword arguments.

        Yields:
            Union[str, ImageResponse]: Segments of the generated response or ImageResponse objects.
        """
        model = cls.get_model(model)

        chat_id = cls.generate_random_string()
        next_action = cls.generate_next_action()
        next_router_state_tree = cls.generate_next_router_state_tree()

        agent_mode = cls.agentMode.get(model, {})
        trending_agent_mode = cls.trendingAgentMode.get(model, {})

        prefix = cls.model_prefixes.get(model, "")
        
        formatted_prompt = ""
        for message in messages:
            role = message.get('role', '').capitalize()
            content = message.get('content', '')
            if role and content:
                formatted_prompt += f"{role}: {content}\n"

        if prefix:
            formatted_prompt = f"{prefix} {formatted_prompt}".strip()

        referer_path = cls.model_referers.get(model, f"/?model={model}")
        referer_url = f"{cls.url}{referer_path}"

        common_headers = {
            'accept': '*/*',
            'accept-language': 'en-US,en;q=0.9',
            'cache-control': 'no-cache',
            'origin': cls.url,
            'pragma': 'no-cache',
            'priority': 'u=1, i',
            'sec-ch-ua': '"Chromium";v="129", "Not=A?Brand";v="8"',
            'sec-ch-ua-mobile': '?0',
            'sec-ch-ua-platform': '"Linux"',
            'sec-fetch-dest': 'empty',
            'sec-fetch-mode': 'cors',
            'sec-fetch-site': 'same-origin',
            'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) '
                          'AppleWebKit/537.36 (KHTML, like Gecko) '
                          'Chrome/129.0.0.0 Safari/537.36'
        }

        headers_api_chat = {
            'Content-Type': 'application/json',
            'Referer': referer_url
        }
        headers_api_chat_combined = {**common_headers, **headers_api_chat}

        payload_api_chat = {
            "messages": [
                {
                    "id": chat_id,
                    "content": formatted_prompt,
                    "role": "user"
                }
            ],
            "id": chat_id,
            "previewToken": None,
            "userId": None,
            "codeModelMode": True,
            "agentMode": agent_mode,
            "trendingAgentMode": trending_agent_mode,
            "isMicMode": False,
            "userSystemPrompt": None,
            "maxTokens": 1024,
            "playgroundTopP": 0.9,
            "playgroundTemperature": 0.5,
            "isChromeExt": False,
            "githubToken": None,
            "clickedAnswer2": False,
            "clickedAnswer3": False,
            "clickedForceWebSearch": False,
            "visitFromDelta": False,
            "mobileClient": False,
            "webSearchMode": websearch,
            "userSelectedModel": cls.userSelectedModel.get(model, model)
        }

        headers_chat = {
            'Accept': 'text/x-component',
            'Content-Type': 'text/plain;charset=UTF-8',
            'Referer': f'{cls.url}/chat/{chat_id}?model={model}',
            'next-action': next_action,
            'next-router-state-tree': next_router_state_tree,
            'next-url': '/'
        }
        headers_chat_combined = {**common_headers, **headers_chat}

        data_chat = '[]'

        async with ClientSession(headers=common_headers) as session:
            try:
                async with session.post(
                    cls.api_endpoint,
                    headers=headers_api_chat_combined,
                    json=payload_api_chat,
                    proxy=proxy
                ) as response_api_chat:
                    response_api_chat.raise_for_status()
                    # We update this part to stream the response incrementally
                    # Instead of waiting for the full response, we read the response as it arrives
                    async for chunk in response_api_chat.content.iter_chunked(1024):
                        if not chunk:
                            continue
                        text = chunk.decode('utf-8', errors='ignore')
                        cleaned_chunk = cls.clean_response(text)
                        yield cleaned_chunk
            except ClientResponseError as e:
                error_text = f"Error {e.status}: {e.message}"
                try:
                    error_response = await e.response.text()
                    cleaned_error = cls.clean_response(error_response)
                    error_text += f" - {cleaned_error}"
                except Exception:
                    pass
                yield error_text
            except Exception as e:
                yield f"Unexpected error during /api/chat request: {str(e)}"

            # Not clear what to do with this second request; keeping it for compatibility
            chat_url = f'{cls.url}/chat/{chat_id}?model={model}'

            try:
                async with session.post(
                    chat_url,
                    headers=headers_chat_combined,
                    data=data_chat,
                    proxy=proxy
                ) as response_chat:
                    response_chat.raise_for_status()
                    pass
            except ClientResponseError as e:
                error_text = f"Error {e.status}: {e.message}"
                try:
                    error_response = await e.response.text()
                    cleaned_error = cls.clean_response(error_response)
                    error_text += f" - {cleaned_error}"
                except Exception:
                    pass
                yield error_text
            except Exception as e:
                yield f"Unexpected error during /chat/{chat_id} request: {str(e)}"

# Custom exception for model not working
class ModelNotWorkingException(Exception):
    def __init__(self, model: str):
        self.model = model
        self.message = f"The model '{model}' is currently not working. Please try another model or wait for it to be fixed."
        super().__init__(self.message)

async def cleanup_rate_limit_stores():
    """
    Periodically cleans up stale entries in the rate_limit_store to prevent memory bloat.
    """
    while True:
        current_time = time.time()
        ips_to_delete = [ip for ip, value in rate_limit_store.items() if current_time - value["timestamp"] > RATE_LIMIT_WINDOW * 2]
        for ip in ips_to_delete:
            del rate_limit_store[ip]
            logger.debug(f"Cleaned up rate_limit_store for IP: {ip}")
        await asyncio.sleep(CLEANUP_INTERVAL)

async def rate_limiter_per_ip(request: Request):
    """
    Rate limiter that enforces a limit based on the client's IP address.
    """
    client_ip = request.client.host
    current_time = time.time()

    # Initialize or update the count and timestamp
    if current_time - rate_limit_store[client_ip]["timest(由Dream 提供Free https://opus.gptuu.com)amp"] > RATE_LIMIT_WINDOW:
        rate_limit_store[client_ip] = {"count": 1, "timestamp": current_time}
    else:
        if rate_limit_store[client_ip]["count"] >= RATE_LIMIT:
            logger.warning(f"Rate limit exceeded for IP address: {client_ip}")
            raise HTTPException(status_code=429, detail='Rate limit exceeded for IP address | NiansuhAI')
        rate_limit_store[client_ip]["count"] += 1

async def get_api_key(request: Request, authorization: str = Header(None)) -> str:
    """
    Dependency to extract and validate the API key from the Authorization header.
    """
    client_ip = request.client.host
    if authorization is None or not authorization.startswith('Bearer '):
        logger.warning(f"Invalid or missing authorization header from IP: {client_ip}")
        raise HTTPException(status_code=401, detail='Invalid authorization header format')
    api_key = authorization[7:]
    if api_key not in API_KEYS:
        logger.warning(f"Invalid API key attempted: {api_key} from IP: {client_ip}")
        raise HTTPException(status_code=401, detail='Invalid API key')
    return api_key

# FastAPI app setup
app = FastAPI()

# Add the cleanup task when the app starts
@app.on_event("startup")
async def startup_event():
    asyncio.create_task(cleanup_rate_limit_stores())
    logger.info("Started rate limit store cleanup task.")

# Middleware to enhance security and enforce Content-Type for specific endpoints
@app.middleware("http")
async def security_middleware(request: Request, call_next):
    client_ip = request.client.host
    # Enforce that POST requests to /v1/chat/completions must have Content-Type: application/json
    if request.method == "POST" and request.url.path == "/v1/chat/completions":
        content_type = request.headers.get("Content-Type")
        if content_type != "application/json":
            logger.warning(f"Invalid Content-Type from IP: {client_ip} for path: {request.url.path}")
            return JSONResponse(
                status_code=400,
                content={
                    "error": {
                        "message": "Content-Type must be application/json",
                        "type": "invalid_request_error",
                        "param": None,
                        "code": None
                    }
                },
            )
    response = await call_next(request)
    return response

# Request Models
class Message(BaseModel):
    role: str
    content: str

class ChatRequest(BaseModel):
    model: str
    messages: List[Message]
    stream: Optional[bool] = False  # Added for streaming support
    temperature: Optional[float] = 1.0
    top_p: Optional[float] = 1.0
    n: Optional[int] = 1
    max_tokens: Optional[int] = None
    presence_penalty: Optional[float] = 0.0
    frequency_penalty: Optional[float] = 0.0
    logit_bias: Optional[Dict[str, float]] = None
    user: Optional[str] = None

# Helper function to collect responses from async generator
async def collect_response_content(generator: AsyncGenerator[Union[str, ImageResponse], None]) -> str:
    response_content = ''
    async for chunk in generator:
        if isinstance(chunk, str):
            response_content += chunk
        elif isinstance(chunk, ImageResponse):
            # Handle image response if needed
            response_content += f"[Image: {chunk.alt}] {', '.join(chunk.images)}\n"
    return response_content

@app.post("/v1/chat/completions", dependencies=[Depends(rate_limiter_per_ip)])
async def chat_completions(request: ChatRequest, req: Request, api_key: str = Depends(get_api_key)):
    client_ip = req.client.host
    # Redact user messages only for logging purposes
    redacted_messages = [{"role": msg.role, "content": "[redacted]"} for msg in request.messages]

    logger.info(f"Received chat completions request from API key: {api_key} | IP: {client_ip} | Model: {request.model} | Messages: {redacted_messages}")

    try:
        # Validate that the requested model is available
        if request.model not in Blackbox.models and request.model not in Blackbox.model_aliases:
            logger.warning(f"Attempt to use unavailable model: {request.model} from IP: {client_ip}")
            raise HTTPException(status_code=400, detail="Requested model is not available.")

        # Process the request with actual message content, but don't log it
        generator = Blackbox.create_async_generator(
            model=request.model,
            messages=[{"role": msg.role, "content": msg.content} for msg in request.messages],
            temperature=request.temperature,
            max_tokens=request.max_tokens
        )

        if request.stream:
            async def stream_response():
                async for chunk in generator:
                    if isinstance(chunk, str):
                        data = json.dumps({
                            "choices": [{
                                "delta": {"content": chunk},
                                "index": 0,
                                "finish_reason": None
                            }],
                            "model": request.model,
                            "id": f"chatcmpl-{uuid.uuid4()}",
                            "object": "chat.completion.chunk",
                            "created": int(datetime.now().timestamp()),
                        })
                        # Ensure that each chunk is sent immediately
                        yield f"data: {data}\n\n"
                    elif isinstance(chunk, ImageResponse):
                        # Handle image responses here if needed
                        pass  # For now, we skip image handling in streaming
                # Send the termination message
                yield "data: [DONE]\n\n"

            logger.info(f"Streaming response enabled for API key: {api_key} | IP: {client_ip}")

            return StreamingResponse(stream_response(), media_type="text/event-stream")
        else:
            response_content = await collect_response_content(generator)
            logger.info(f"Completed response generation for API key: {api_key} | IP: {client_ip}")
            return {
                "id": f"chatcmpl-{uuid.uuid4()}",
                "object": "chat.completion",
                "created": int(datetime.now().timestamp()),
                "model": request.model,
                "choices": [
                    {
                        "index": 0,
                        "message": {
                            "role": "assistant",
                            "content": response_content
                        },
                        "finish_reason": "stop"
                    }
                ],
                "usage": {
                    "prompt_tokens": sum(len(msg.content.split()) for msg in request.messages),
                    "completion_tokens": len(response_content.split()),
                    "total_tokens": sum(len(msg.content.split()) for msg in request.messages) + len(response_content.split())
                },
            }
    except ModelNotWorkingException as e:
        logger.warning(f"Model not working: {e} | IP: {client_ip}")
        raise HTTPException(status_code=503, detail=str(e))
    except HTTPException as he:
        logger.warning(f"HTTPException: {he.detail} | IP: {client_ip}")
        raise he
    except Exception as e:
        logger.exception(f"An unexpected error occurred while processing the chat completions request from IP: {client_ip}.")
        raise HTTPException(status_code=500, detail=str(e))

# Endpoint: GET /v1/models
@app.get("/v1/models", dependencies=[Depends(rate_limiter_per_ip)])
async def get_models(req: Request):
    client_ip = req.client.host
    logger.info(f"Fetching available models from IP: {client_ip}")
    return {"data": [{"id": model, "object": "model"} for model in Blackbox.models]}

# Endpoint: GET /v1/health
@app.get("/v1/health", dependencies=[Depends(rate_limiter_per_ip)])
async def health_check(req: Request):
    client_ip = req.client.host
    logger.info(f"Health check requested from IP: {client_ip}")
    return {"status": "ok"}

# Custom exception handler to match OpenAI's error format
@app.exception_handler(HTTPException)
async def http_exception_handler(request: Request, exc: HTTPException):
    client_ip = request.client.host
    logger.error(f"HTTPException: {exc.detail} | Path: {request.url.path} | IP: {client_ip}")
    return JSONResponse(
        status_code=exc.status_code,
        content={
            "error": {
                "message": exc.detail,
                "type": "invalid_request_error",
                "param": None,
                "code": None
            }
        },
    )

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