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 from datetime import datetime # Added import for datetime from aiohttp import ClientSession, ClientTimeout, ClientError from fastapi import FastAPI, HTTPException, Request, Depends, Header from fastapi.responses import 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 rate_limit_store = defaultdict(lambda: {"count": 0, "timestamp": time.time()}) async def get_api_key(authorization: str = Header(...)) -> str: if not authorization.startswith('Bearer '): logger.warning("Invalid authorization header format.") 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}") raise HTTPException(status_code=401, detail='Invalid API key') return api_key async def rate_limiter(api_key: str = Depends(get_api_key)): current_time = time.time() window_start = rate_limit_store[api_key]["timestamp"] if current_time - window_start > 60: rate_limit_store[api_key] = {"count": 1, "timestamp": current_time} else: if rate_limit_store[api_key]["count"] >= RATE_LIMIT: logger.warning(f"Rate limit exceeded for API key: {api_key}") raise HTTPException(status_code=429, detail='Rate limit exceeded') rate_limit_store[api_key]["count"] += 1 # 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, url: str, alt: str): self.url = url self.alt = alt def to_data_uri(image: Any) -> str: return "data:image/png;base64,..." # Replace with actual base64 data class Blackbox: url = "https://www.blackbox.ai" api_endpoint = "https://www.blackbox.ai/api/chat" working = True supports_stream = True supports_system_message = True supports_message_history = True default_model = 'blackboxai' image_models = ['ImageGeneration'] models = [ default_model, 'blackboxai-pro', "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', *image_models, 'Niansuh', ] agentMode = { 'ImageGeneration': {'mode': True, 'id': "ImageGenerationLV45LJp", 'name': "Image Generation"}, 'Niansuh': {'mode': True, 'id': "NiansuhAIk1HgESy", 'name': "Niansuh"}, } 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', 'Niansuh': '@Niansuh', } model_referers = { "blackboxai": f"{url}/?model=blackboxai", "gpt-4o": f"{url}/?model=gpt-4o", "gemini-pro": f"{url}/?model=gemini-pro", "claude-sonnet-3.5": f"{url}/?model=claude-sonnet-3.5" } model_aliases = { "gemini-flash": "gemini-1.5-flash", "claude-3.5-sonnet": "claude-sonnet-3.5", "flux": "ImageGeneration", "niansuh": "Niansuh", } @classmethod def get_model(cls, model: str) -> str: if model in cls.models: return model elif model in cls.userSelectedModel: return model elif model in cls.model_aliases: return cls.model_aliases[model] else: return cls.default_model @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[Any, None]: model = cls.get_model(model) logger.info(f"Selected model: {model}") if not cls.working or model not in cls.models: logger.error(f"Model {model} is not working or not supported.") raise ModelNotWorkingException(model) headers = { "accept": "*/*", "accept-language": "en-US,en;q=0.9", "cache-control": "no-cache", "content-type": "application/json", "origin": cls.url, "pragma": "no-cache", "priority": "u=1, i", "referer": cls.model_referers.get(model, cls.url), "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", } if model in cls.model_prefixes: prefix = cls.model_prefixes[model] if not messages[0]['content'].startswith(prefix): logger.debug(f"Adding prefix '{prefix}' to the first message.") messages[0]['content'] = f"{prefix} {messages[0]['content']}" random_id = ''.join(random.choices(string.ascii_letters + string.digits, k=7)) messages[-1]['id'] = random_id messages[-1]['role'] = 'user' # Don't log the full message content for privacy logger.debug(f"Generated message ID: {random_id} for model: {model}") if image is not None: messages[-1]['data'] = { 'fileText': '', 'imageBase64': to_data_uri(image), 'title': image_name } messages[-1]['content'] = 'FILE:BB\n$#$\n\n$#$\n' + messages[-1]['content'] logger.debug("Image data added to the message.") data = { "messages": messages, "id": random_id, "previewToken": None, "userId": None, "codeModelMode": True, "agentMode": {}, "trendingAgentMode": {}, "isMicMode": False, "userSystemPrompt": None, "maxTokens": 99999999, "playgroundTopP": 0.9, "playgroundTemperature": 0.5, "isChromeExt": False, "githubToken": None, "clickedAnswer2": False, "clickedAnswer3": False, "clickedForceWebSearch": False, "visitFromDelta": False, "mobileClient": False, "userSelectedModel": None, "webSearchMode": webSearchMode, } if model in cls.agentMode: data["agentMode"] = cls.agentMode[model] elif model in cls.trendingAgentMode: data["trendingAgentMode"] = cls.trendingAgentMode[model] elif model in cls.userSelectedModel: data["userSelectedModel"] = cls.userSelectedModel[model] logger.info(f"Sending request to {cls.api_endpoint} with data (excluding messages).") timeout = ClientTimeout(total=60) # Set an appropriate timeout retry_attempts = 10 # Set the number of retry attempts for attempt in range(retry_attempts): try: async with ClientSession(headers=headers, timeout=timeout) as session: async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response: response.raise_for_status() logger.info(f"Received response with status {response.status}") if model == 'ImageGeneration': response_text = await response.text() url_match = re.search(r'https://storage\.googleapis\.com/[^\s\)]+', response_text) if url_match: image_url = url_match.group(0) logger.info(f"Image URL found.") yield ImageResponse(image_url, alt=messages[-1]['content']) else: logger.error("Image URL not found in the response.") raise Exception("Image URL not found in the response") else: full_response = "" search_results_json = "" try: async for chunk, _ in response.content.iter_chunks(): if chunk: decoded_chunk = chunk.decode(errors='ignore') decoded_chunk = re.sub(r'\$@\$v=[^$]+\$@\$', '', decoded_chunk) if decoded_chunk.strip(): if '$~~~$' in decoded_chunk: search_results_json += decoded_chunk else: full_response += decoded_chunk yield decoded_chunk logger.info("Finished streaming response chunks.") except Exception as e: logger.exception("Error while iterating over response chunks.") raise e if data["webSearchMode"] and search_results_json: match = re.search(r'\$~~~\$(.*?)\$~~~\$', search_results_json, re.DOTALL) if match: try: search_results = json.loads(match.group(1)) formatted_results = "\n\n**Sources:**\n" for i, result in enumerate(search_results[:5], 1): formatted_results += f"{i}. [{result['title']}]({result['link']})\n" logger.info("Formatted search results.") yield formatted_results except json.JSONDecodeError as je: logger.error("Failed to parse search results JSON.") raise je break # Exit the retry loop if successful except ClientError as ce: logger.error(f"Client error occurred: {ce}. Retrying attempt {attempt + 1}/{retry_attempts}") if attempt == retry_attempts - 1: raise HTTPException(status_code=502, detail="Error communicating with the external API. | NiansuhAI") except asyncio.TimeoutError: logger.error(f"Request timed out. Retrying attempt {attempt + 1}/{retry_attempts}") if attempt == retry_attempts - 1: raise HTTPException(status_code=504, detail="External API request timed out. | NiansuhAI") except Exception as e: logger.error(f"Unexpected error: {e}. Retrying attempt {attempt + 1}/{retry_attempts}") if attempt == retry_attempts - 1: raise HTTPException(status_code=500, detail=str(e)) # FastAPI app setup app = FastAPI() class Message(BaseModel): role: str content: str class ChatRequest(BaseModel): model: str messages: List[Message] stream: Optional[bool] = False webSearchMode: Optional[bool] = False def create_response(content: str, model: str, finish_reason: Optional[str] = None) -> Dict[str, Any]: return { "id": f"chatcmpl-{uuid.uuid4()}", "object": "chat.completion.chunk", "created": int(datetime.now().timestamp()), "model": model, "choices": [ { "index": 0, "delta": {"content": content, "role": "assistant"}, "finish_reason": finish_reason, } ], "usage": None, } @app.post("/niansuhai/v1/chat/completions", dependencies=[Depends(rate_limiter)]) async def chat_completions(request: ChatRequest, req: Request, api_key: str = Depends(get_api_key)): logger.info(f"Received chat completions request from API key: {api_key} | Model: {request.model}") 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}") raise HTTPException(status_code=400, detail="Requested model is not available.") # Process the request but do not log sensitive content async_generator = Blackbox.create_async_generator( model=request.model, messages=[{"role": msg.role, "content": "[redacted]"} for msg in request.messages], # Redact user messages in logs image=None, image_name=None, webSearchMode=request.webSearchMode ) if request.stream: async def generate(): try: async for chunk in async_generator: if isinstance(chunk, ImageResponse): image_markdown = f"![image]({chunk.url})" response_chunk = create_response(image_markdown, request.model) else: response_chunk = create_response(chunk, request.model) yield f"data: {json.dumps(response_chunk)}\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("Error during streaming response generation.") 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.url})\n" else: response_content += chunk logger.info(f"Completed non-streaming response generation for API key: {api_key}") 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()) }, } except ModelNotWorkingException as e: logger.warning(f"Model not working: {e}") raise HTTPException(status_code=503, detail=str(e)) except HTTPException as he: logger.warning(f"HTTPException: {he.detail}") raise he except Exception as e: logger.exception("An unexpected error occurred while processing the chat completions request.") raise HTTPException(status_code=500, detail=str(e)) @app.get("/niansuhai/v1/models", dependencies=[Depends(rate_limiter)]) async def get_models(api_key: str = Depends(get_api_key)): logger.info(f"Fetching available models for API key: {api_key}") return {"data": [{"id": model} for model in Blackbox.models]} # Additional endpoints for better functionality @app.get("/niansuhai/v1/health", dependencies=[Depends(rate_limiter)]) async def health_check(api_key: str = Depends(get_api_key)): logger.info(f"Health check requested by API key: {api_key}") return {"status": "ok"} @app.get("/niansuhai/v1/models/{model}/status", dependencies=[Depends(rate_limiter)]) async def model_status(model: str, api_key: str = Depends(get_api_key)): logger.info(f"Model status requested for '{model}' by API key: {api_key}") if model in Blackbox.models: return {"model": model, "status": "available"} elif model in Blackbox.model_aliases: actual_model = Blackbox.model_aliases[model] return {"model": actual_model, "status": "available via alias"} else: logger.warning(f"Model not found: {model}") raise HTTPException(status_code=404, detail="Model not found") if __name__ == "__main__": import uvicorn uvicorn.run(app, host="0.0.0.0", port=8000)