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 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 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, Dict[str, Any]], None]: """ Creates an asynchronous generator for streaming responses from Blackbox AI. """ 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) } 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) yield {"type": "image", "url": image_url, "alt": "Generated Image"} else: yield cleaned_response else: if websearch: 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 = "**Source:**\n" for item in sources: title = item.get('title', 'No Title') link = item.get('link', '#') position = item.get('position', '') source_formatted += f"{position}. [{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)}" # 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_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"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 } }, )