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

# =====================
# 1. Configure Logging
# =====================
logging.basicConfig(
    level=logging.DEBUG,  # Set to DEBUG for detailed logs during development
    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
    handlers=[logging.StreamHandler()]
)
logger = logging.getLogger(__name__)

# ============================
# 2. 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.")

# ====================================
# 3. Define Rate Limiting Structures
# ====================================
rate_limit_store = defaultdict(lambda: {"count": 0, "timestamp": time.time()})

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

# ========================
# 4. Define Pydantic Models
# ========================
class ImageResponseModel(BaseModel):
    images: str
    alt: str

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

# ===============================
# 5. Define Custom Exceptions
# ===============================
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)

# =======================
# 6. Define the Blackbox
# =======================
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)
        try:
            response_json = json.loads(cleaned_text)
            # Adjust based on actual response structure
            return response_json.get("response", response_json.get("data", cleaned_text))
        except json.JSONDecodeError:
            return cleaned_text.strip()

    @classmethod
    async def generate_response(
        cls,
        model: str,
        messages: List[Dict[str, str]],
        proxy: Optional[str] = None,
        **kwargs
    ) -> str:
        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, "")
        
        # Construct the prompt
        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"
                }
            ],
            "model": model  # Simplified payload for testing
        }

        async with ClientSession(headers=common_headers) as session:
            try:
                logger.debug(f"Payload sent to Blackbox API: {json.dumps(payload_api_chat)}")
                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()
                    text = await response_api_chat.text()
                    logger.debug(f"Raw response from Blackbox API: {text}")  # Log raw response
                    cleaned_response = cls.clean_response(text)
                    logger.debug(f"Cleaned response: {cleaned_response}")  # Log cleaned response
                    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}"
                    logger.error(f"Blackbox API ClientResponseError: {error_text}")
                except Exception:
                    pass
                return error_text
            except Exception as e:
                logger.exception(f"Unexpected error during /api/chat request: {str(e)}")
                return f"Unexpected error during /api/chat request: {str(e)}"

    # ===============================
    # 7. Initialize FastAPI App
    # ===============================
    app = FastAPI()

    # ====================================
    # 8. Define Middleware and Dependencies
    # ====================================
    @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

    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 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

    # =====================================
    # 9. Define FastAPI Event Handlers
    # =====================================
    @app.on_event("startup")
    async def startup_event():
        asyncio.create_task(cleanup_rate_limit_stores())
        logger.info("Started rate limit store cleanup task.")

    # ==========================================
    # 10. Define FastAPI Endpoints
    # ==========================================
    @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.generate_response(
                model=request.model,
                messages=[{"role": msg.role, "content": msg.content} for msg in request.messages],
                temperature=request.temperature,
                max_tokens=request.max_tokens
            )

            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))

    @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]}

    @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"}

    # ========================================
    # 11. Define Custom Exception Handler
    # ========================================
    @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
                }
            },
        )

    # ============================
    # 12. Optional: Streaming Endpoint
    # ============================
    @app.post("/v1/chat/completions/stream", dependencies=[Depends(rate_limiter_per_ip)])
    async def chat_completions_stream(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 streaming 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.")

            # Create an asynchronous generator for the response
            async_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
            )

            logger.info(f"Started streaming response for API key: {api_key} | IP: {client_ip}")
            return StreamingResponse(async_generator, media_type="text/event-stream")
        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 streaming chat completions request from IP: {client_ip}.")
            raise HTTPException(status_code=500, detail=str(e))

    # ========================================
    # 13. Run the Application with Uvicorn
    # ========================================
    if __name__ == "__main__":
        import uvicorn
        uvicorn.run(app, host="0.0.0.0", port=8000)