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
from datetime import datetime

from aiohttp import ClientSession, ClientTimeout, ClientError, ClientResponseError
from fastapi import FastAPI, HTTPException, Request, Depends, Header
from fastapi.responses import JSONResponse
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
AVAILABLE_MODELS = os.getenv('AVAILABLE_MODELS', '')  # Comma-separated available models

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.")

# Process available models
if AVAILABLE_MODELS:
    AVAILABLE_MODELS = [model.strip() for model in AVAILABLE_MODELS.split(',') if model.strip()]
else:
    AVAILABLE_MODELS = []  # If empty, all models are available

# 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 Blackbox:
    label = "Blackbox AI"
    url = "https://www.blackbox.ai"
    api_endpoint = "https://www.blackbox.ai/api/chat"
    working = True
    supports_gpt_4 = 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_aliases = {
        "gpt-3.5-turbo": "blackboxai",
        "gpt-4": "gpt-4o",
        "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 clean_response(text: str) -> str:
        pattern = r'^\$\@\$v=undefined-rv1\$\@\$'
        cleaned_text = re.sub(pattern, '', text)
        return cleaned_text

    @classmethod
    async def create_completion(
        cls,
        model: str,
        messages: List[Dict[str, str]],
        **kwargs
    ) -> str:
        """
        Creates a completion using the Blackbox AI API.
        """
        model = cls.get_model(model)
        if model is None:
            logger.error(f"Model {model} is not available.")
            raise ModelNotWorkingException(model)

        chat_id = cls.generate_random_string()

        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"

        headers = {
            'Content-Type': 'application/json',
            'accept': '*/*',
            'accept-language': 'en-US,en;q=0.9',
            'origin': cls.url,
            'referer': f"{cls.url}/?model={model}",
            'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36'
        }

        payload = {
            "messages": [
                {
                    "id": chat_id,
                    "content": formatted_prompt,
                    "role": "user"
                }
            ],
            "id": chat_id,
            "previewToken": None,
            "userId": None,
            "codeModelMode": True,
            "agentMode": cls.agentMode.get(model, {}),
            "trendingAgentMode": cls.trendingAgentMode.get(model, {}),
            "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": False,
            "userSelectedModel": cls.userSelectedModel.get(model, model)
        }

        async with ClientSession() as session:
            try:
                async with session.post(
                    cls.api_endpoint,
                    headers=headers,
                    json=payload
                ) as response:
                    response.raise_for_status()
                    text = await response.text()
                    cleaned_response = cls.clean_response(text)
                    return cleaned_response
            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
                raise HTTPException(status_code=e.status, detail=error_text)
            except Exception as e:
                raise HTTPException(status_code=500, detail=f"Unexpected error: {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]["timestamp"] > 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')
        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]
    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

@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
    response_content = await Blackbox.create_completion(
        model=request.model,
        messages=[{"role": msg.role, "content": msg.content} for msg in request.messages],  # Actual message content used here
    )

    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": [
            {
                "message": {
                    "role": "assistant",
                    "content": response_content
                },
                "finish_reason": "stop",
                "index": 0
            }
        ],
        "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())
        }
    }  # Closing the dictionary here
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)