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, AsyncGenerator, Union from datetime import datetime from aiohttp import ClientSession, ClientTimeout, ClientError, ClientResponseError from fastapi import FastAPI, HTTPException, Request, Depends, Header from fastapi.responses import StreamingResponse, JSONResponse, RedirectResponse 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 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 # 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) # Mock implementations for ImageResponse and to_data_uri class ImageResponse: def __init__(self, images: str, alt: str): self.images = images self.alt = alt def to_data_uri(image: Any) -> str: return "data:image/png;base64,..." # Replace with actual base64 data # New Blackbox Class Integration 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, image: Any = None, image_name: Optional[str] = None, webSearchMode: 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. image (Any): Image data, if any. image_name (Optional[str]): Name of the image, if any. webSearchMode (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": webSearchMode, "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() text = await response_api_chat.text() cleaned_response = cls.clean_response(text) if model in cls.image_models: match = re.search(r'!\[.*?\]\((https?://[^\)]+)\)', cleaned_response) if match: image_url = match.group(1) image_response = ImageResponse(images=image_url, alt="Generated Image") yield image_response else: yield cleaned_response else: if webSearchMode: match = re.search(r'\$~~~\$(.*?)\$~~~\$', cleaned_response, re.DOTALL) if match: source_part = match.group(1).strip() answer_part = cleaned_response[match.end():].strip() try: sources = json.loads(source_part) source_formatted = "**Sources:**\n" for item in sources[:5]: title = item.get('title', 'No Title') link = item.get('link', '#') source_formatted += f"- [{title}]({link})\n" final_response = f"{answer_part}\n\n{source_formatted}" except json.JSONDecodeError: final_response = f"{answer_part}\n\nSource information is unavailable." else: final_response = cleaned_response else: if '$~~~$' in cleaned_response: final_response = cleaned_response.split('$~~~$')[0].strip() else: final_response = cleaned_response yield final_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 yield error_text except Exception as e: yield f"Unexpected error during /api/chat request: {str(e)}" 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() # Assuming some side-effect or logging is needed here 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)}" # 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 stream: Optional[bool] = False stop: Optional[Union[str, List[str]]] = None 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 webSearchMode: Optional[bool] = False # Custom parameter class TokenizerRequest(BaseModel): text: str def calculate_estimated_cost(prompt_tokens: int, completion_tokens: int) -> float: """ Calculate the estimated cost based on the number of tokens. Replace the pricing below with your actual pricing model. """ # Example pricing: $0.00000268 per token cost_per_token = 0.00000268 return round((prompt_tokens + completion_tokens) * cost_per_token, 8) def create_response(content: str, model: str, finish_reason: Optional[str] = None) -> Dict[str, Any]: return { "id": f"chatcmpl-{uuid.uuid4()}", "object": "chat.completion", "created": int(datetime.now().timestamp()), "model": model, "choices": [ { "index": 0, "message": { "role": "assistant", "content": content }, "finish_reason": finish_reason } ], "usage": None, # To be filled in non-streaming responses } @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 async_generator = Blackbox.create_async_generator( model=request.model, messages=[{"role": msg.role, "content": msg.content} for msg in request.messages], # Actual message content used here image=None, image_name=None, webSearchMode=request.webSearchMode ) if request.stream: async def generate(): try: assistant_content = "" async for chunk in async_generator: if isinstance(chunk, ImageResponse): # Handle image responses if necessary image_markdown = f"![image]({chunk.images})\n" assistant_content += image_markdown response_chunk = create_response(image_markdown, request.model, finish_reason=None) else: assistant_content += chunk # Yield the chunk as a partial choice response_chunk = { "id": f"chatcmpl-{uuid.uuid4()}", "object": "chat.completion.chunk", "created": int(datetime.now().timestamp()), "model": request.model, "choices": [ { "index": 0, "delta": {"content": chunk, "role": "assistant"}, "finish_reason": None, } ], "usage": None, # Usage can be updated if you track tokens in real-time } yield f"data: {json.dumps(response_chunk)}\n\n" # After all chunks are sent, send the final message with finish_reason prompt_tokens = sum(len(msg['content'].split()) for msg in request.messages) completion_tokens = len(assistant_content.split()) total_tokens = prompt_tokens + completion_tokens estimated_cost = calculate_estimated_cost(prompt_tokens, completion_tokens) final_response = { "id": f"chatcmpl-{uuid.uuid4()}", "object": "chat.completion", "created": int(datetime.now().timestamp()), "model": request.model, "choices": [ { "message": { "role": "assistant", "content": assistant_content }, "finish_reason": "stop", "index": 0 } ], "usage": { "prompt_tokens": prompt_tokens, "completion_tokens": completion_tokens, "total_tokens": total_tokens, "estimated_cost": estimated_cost }, } yield f"data: {json.dumps(final_response)}\n\n" yield "data: [DONE]\n\n" except HTTPException as he: error_response = {"error": he.detail} yield f"data: {json.dumps(error_response)}\n\n" except Exception as e: logger.exception(f"Error during streaming response generation from IP: {client_ip}.") error_response = {"error": str(e)} yield f"data: {json.dumps(error_response)}\n\n" return StreamingResponse(generate(), media_type="text/event-stream") else: response_content = "" async for chunk in async_generator: if isinstance(chunk, ImageResponse): response_content += f"![image]({chunk.images})\n" else: response_content += chunk prompt_tokens = sum(len(msg.content.split()) for msg in request.messages) completion_tokens = len(response_content.split()) total_tokens = prompt_tokens + completion_tokens estimated_cost = calculate_estimated_cost(prompt_tokens, completion_tokens) logger.info(f"Completed non-streaming 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": prompt_tokens, "completion_tokens": completion_tokens, "total_tokens": total_tokens, "estimated_cost": estimated_cost }, } 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: POST /v1/tokenizer @app.post("/v1/tokenizer", dependencies=[Depends(rate_limiter_per_ip)]) async def tokenizer(request: TokenizerRequest, req: Request): client_ip = req.client.host text = request.text token_count = len(text.split()) logger.info(f"Tokenizer requested from IP: {client_ip} | Text length: {len(text)}") return {"text": text, "tokens": token_count} # 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/models/{model}/status @app.get("/v1/models/{model}/status", dependencies=[Depends(rate_limiter_per_ip)]) async def model_status(model: str, req: Request): client_ip = req.client.host logger.info(f"Model status requested for '{model}' from IP: {client_ip}") if model in Blackbox.models: return {"model": model, "status": "available"} elif model in Blackbox.model_aliases and Blackbox.model_aliases[model] in Blackbox.models: actual_model = Blackbox.model_aliases[model] return {"model": actual_model, "status": "available via alias"} else: logger.warning(f"Model not found: {model} from IP: {client_ip}") raise HTTPException(status_code=404, detail="Model not found") # 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"} # Endpoint: GET /v1/chat/completions (GET method) @app.get("/v1/chat/completions") async def chat_completions_get(req: Request): client_ip = req.client.host logger.info(f"GET request made to /v1/chat/completions from IP: {client_ip}, redirecting to 'about:blank'") return RedirectResponse(url='about:blank') # 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 } }, ) # Run the application if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000)