Update main.py
Browse files
main.py
CHANGED
@@ -8,15 +8,13 @@ import logging
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import asyncio
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import time
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from collections import defaultdict
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from typing import List, Dict, Any, Optional, AsyncGenerator, Union
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from datetime import datetime
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from aiohttp import ClientSession, ClientTimeout, ClientError
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from fastapi import FastAPI, HTTPException, Request, Depends, Header
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from fastapi.responses import StreamingResponse, JSONResponse
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from pydantic import BaseModel
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from tenacity import retry, stop_after_attempt, wait_exponential, retry_if_exception_type, RetryError
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# Configure logging
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logging.basicConfig(
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@@ -27,306 +25,96 @@ logging.basicConfig(
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logger = logging.getLogger(__name__)
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# Load environment variables
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API_KEYS = os.getenv('API_KEYS', '').split(',')
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RATE_LIMIT = int(os.getenv('RATE_LIMIT', '60'))
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AVAILABLE_MODELS = os.getenv('AVAILABLE_MODELS', '')
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RETRY_ATTEMPTS = int(os.getenv('RETRY_ATTEMPTS', '5')) # Retry attempts
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if not API_KEYS or API_KEYS == ['']:
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logger.error("No API keys found. Please set the API_KEYS environment variable.")
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raise Exception("API_KEYS environment variable not set.")
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# Process available models
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if AVAILABLE_MODELS:
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AVAILABLE_MODELS = [model.strip() for model in AVAILABLE_MODELS.split(',') if model.strip()]
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else:
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AVAILABLE_MODELS = []
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# Simple in-memory rate limiter based solely on IP addresses
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rate_limit_store = defaultdict(lambda: {"count": 0, "timestamp": time.time()})
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CLEANUP_INTERVAL = 60 # seconds
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RATE_LIMIT_WINDOW = 60 # seconds
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async def cleanup_rate_limit_stores():
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"""
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Periodically cleans up stale entries in the rate_limit_store to prevent memory bloat.
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"""
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while True:
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current_time = time.time()
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ips_to_delete = [ip for ip, value in rate_limit_store.items() if current_time - value["timestamp"] > RATE_LIMIT_WINDOW * 2]
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for ip in ips_to_delete:
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del rate_limit_store[ip]
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logger.debug(f"Cleaned up rate_limit_store for IP: {ip}")
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await asyncio.sleep(CLEANUP_INTERVAL)
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async def rate_limiter_per_ip(request: Request):
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"""
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Rate limiter that enforces a limit based on the client's IP address.
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"""
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client_ip = request.client.host
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current_time = time.time()
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# Initialize or update the count and timestamp
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if current_time - rate_limit_store[client_ip]["timestamp"] > RATE_LIMIT_WINDOW:
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rate_limit_store[client_ip] = {"count": 1, "timestamp": current_time}
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else:
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if rate_limit_store[client_ip]["count"] >= RATE_LIMIT:
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raise HTTPException(status_code=429, detail='Rate limit exceeded for IP address | NiansuhAI')
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rate_limit_store[client_ip]["count"] += 1
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async def get_api_key(request: Request, authorization: str = Header(None)) -> str:
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"""
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Dependency to extract and validate the API key from the Authorization header.
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"""
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client_ip = request.client.host
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if authorization is None or not authorization.startswith('Bearer '):
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logger.warning(f"Invalid or missing authorization header from IP: {client_ip}")
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raise HTTPException(status_code=401, detail='Invalid authorization header format')
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api_key = authorization[7:]
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if api_key not in API_KEYS:
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logger.warning(f"Invalid API key attempted: {api_key} from IP: {client_ip}")
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raise HTTPException(status_code=401, detail='Invalid API key')
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return api_key
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# Custom exception for model not working
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class ModelNotWorkingException(Exception):
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def __init__(self, model: str):
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self.model = model
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self.message = f"The model '{model}' is currently not working. Please try another model or wait for it to be fixed."
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super().__init__(self.message)
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# Mock implementations for ImageResponse and to_data_uri
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class ImageResponse:
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def __init__(self, url: str, alt: str):
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self.url = url
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self.alt = alt
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def to_data_uri(
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return "data:image/
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# Retry Decorator
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def async_retry(
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retries: int = 5,
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exceptions: Tuple[Type[BaseException], ...] = (ClientError, asyncio.TimeoutError),
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initial_delay: float = 1.0,
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max_delay: float = 10.0,
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backoff_multiplier: float = 2.0,
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jitter: float = 0.1,
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) -> Callable:
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"""
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Asynchronous retry decorator with exponential backoff and jitter.
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"""
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def decorator(func: Callable) -> Callable:
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@retry(
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stop=stop_after_attempt(retries),
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wait=wait_exponential(multiplier=initial_delay, min=initial_delay, max=max_delay) + wait_exponential(multiplier=0, max=jitter),
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retry=retry_if_exception_type(exceptions),
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reraise=True,
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)
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async def wrapper(*args, **kwargs):
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try:
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return await func(*args, **kwargs)
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except exceptions as e:
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logger.warning(f"Function {func.__name__} failed with {e}. Retrying...")
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raise
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return wrapper
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return decorator
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class Blackbox:
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url = "https://www.blackbox.ai"
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api_endpoint = "https://www.blackbox.ai/api/chat"
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working = True
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supports_stream = True
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supports_system_message = True
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supports_message_history = True
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default_model = 'blackboxai'
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models = [
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default_model,
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'blackboxai-pro',
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"llama-3.1-8b",
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'llama-3.1-70b',
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'llama-3.1-405b',
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'gpt-4o',
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'gemini-pro',
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'gemini-1.5-flash',
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'claude-sonnet-3.5',
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'PythonAgent',
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'JavaAgent',
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'JavaScriptAgent',
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'HTMLAgent',
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'GoogleCloudAgent',
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'AndroidDeveloper',
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'SwiftDeveloper',
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'Next.jsAgent',
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'MongoDBAgent',
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'PyTorchAgent',
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'ReactAgent',
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'XcodeAgent',
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'AngularJSAgent',
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*image_models,
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'Niansuh',
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]
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# Filter models based on AVAILABLE_MODELS
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if AVAILABLE_MODELS:
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models = [model for model in models if model in AVAILABLE_MODELS]
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agentMode = {
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'ImageGeneration': {'mode': True, 'id': "ImageGenerationLV45LJp", 'name': "Image Generation"},
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'Niansuh': {'mode': True, 'id': "NiansuhAIk1HgESy", 'name': "Niansuh"},
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}
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trendingAgentMode = {
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"blackboxai": {},
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"gemini-1.5-flash": {'mode': True, 'id': 'Gemini'},
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"llama-3.1-8b": {'mode': True, 'id': "llama-3.1-8b"},
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'llama-3.1-70b': {'mode': True, 'id': "llama-3.1-70b"},
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'llama-3.1-405b': {'mode': True, 'id': "llama-3.1-405b"},
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'blackboxai-pro': {'mode': True, 'id': "BLACKBOXAI-PRO"},
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'PythonAgent': {'mode': True, 'id': "Python Agent"},
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'JavaAgent': {'mode': True, 'id': "Java Agent"},
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'JavaScriptAgent': {'mode': True, 'id': "JavaScript Agent"},
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'HTMLAgent': {'mode': True, 'id': "HTML Agent"},
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'GoogleCloudAgent': {'mode': True, 'id': "Google Cloud Agent"},
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'AndroidDeveloper': {'mode': True, 'id': "Android Developer"},
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'SwiftDeveloper': {'mode': True, 'id': "Swift Developer"},
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'Next.jsAgent': {'mode': True, 'id': "Next.js Agent"},
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'MongoDBAgent': {'mode': True, 'id': "MongoDB Agent"},
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'PyTorchAgent': {'mode': True, 'id': "PyTorch Agent"},
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'ReactAgent': {'mode': True, 'id': "React Agent"},
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'XcodeAgent': {'mode': True, 'id': "Xcode Agent"},
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'AngularJSAgent': {'mode': True, 'id': "AngularJS Agent"},
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}
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userSelectedModel = {
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"gpt-4o": "gpt-4o",
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"gemini-pro": "gemini-pro",
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'claude-sonnet-3.5': "claude-sonnet-3.5",
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}
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model_prefixes = {
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'gpt-4o': '@GPT-4o',
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'gemini-pro': '@Gemini-PRO',
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'claude-sonnet-3.5': '@Claude-Sonnet-3.5',
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'PythonAgent': '@Python Agent',
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'JavaAgent': '@Java Agent',
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'JavaScriptAgent': '@JavaScript Agent',
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'HTMLAgent': '@HTML Agent',
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'GoogleCloudAgent': '@Google Cloud Agent',
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'AndroidDeveloper': '@Android Developer',
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'SwiftDeveloper': '@Swift Developer',
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'Next.jsAgent': '@Next.js Agent',
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'MongoDBAgent': '@MongoDB Agent',
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'PyTorchAgent': '@PyTorch Agent',
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'ReactAgent': '@React Agent',
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'XcodeAgent': '@Xcode Agent',
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'AngularJSAgent': '@AngularJS Agent',
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'blackboxai-pro': '@BLACKBOXAI-PRO',
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'ImageGeneration': '@Image Generation',
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'Niansuh': '@Niansuh',
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}
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model_referers = {
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"blackboxai": f"{url}/?model=blackboxai",
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"gpt-4o": f"{url}/?model=gpt-4o",
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"gemini-pro": f"{url}/?model=gemini-pro",
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"claude-sonnet-3.5": f"{url}/?model=claude-sonnet-3.5"
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}
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model_aliases = {
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"gemini-flash": "gemini-1.5-flash",
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"claude-3.5-sonnet": "claude-sonnet-3.5",
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"flux": "ImageGeneration",
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"niansuh": "Niansuh",
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}
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@classmethod
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def get_model(cls, model: str) -> Optional[str]:
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if model in cls.models:
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return model
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elif model in cls.userSelectedModel and cls.userSelectedModel[model] in cls.models:
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return cls.userSelectedModel[model]
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elif model in cls.model_aliases and cls.model_aliases[model] in cls.models:
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return cls.model_aliases[model]
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else:
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return cls.default_model
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@classmethod
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@async_retry(
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retries=RETRY_ATTEMPTS,
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exceptions=(ClientError, asyncio.TimeoutError),
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initial_delay=1.0,
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max_delay=10.0,
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backoff_multiplier=2.0,
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jitter=0.1,
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)
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async def create_async_generator(
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cls,
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model: str,
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messages: List[Dict[str, str]],
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-
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image: Any = None,
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image_name: Optional[str] = None,
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webSearchMode: bool = False,
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**kwargs
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) -> AsyncGenerator[Any, None]:
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"""
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Create an asynchronous generator to interact with the external API.
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"""
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model = cls.get_model(model)
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if model is None:
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raise ModelNotWorkingException(model)
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logger.info(f"Selected model: {model}")
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if not cls.working or model not in cls.models:
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logger.error(f"Model {model} is not working or not supported.")
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raise ModelNotWorkingException(model)
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headers = {
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"accept": "*/*",
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"accept-language": "en-US,en;q=0.9",
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"cache-control": "no-cache",
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"content-type": "application/json",
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"origin": cls.url,
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"pragma": "no-cache",
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"priority": "u=1, i",
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"referer": cls.model_referers.get(model, cls.url),
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"sec-ch-ua": '"Chromium";v="129", "Not=A?Brand";v="8"',
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"sec-ch-ua-mobile": "?0",
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"sec-ch-ua-platform": '"Linux"',
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"sec-fetch-dest": "empty",
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"sec-fetch-mode": "cors",
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"sec-fetch-site": "same-origin",
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"user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36",
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}
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if model in cls.model_prefixes:
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prefix = cls.model_prefixes[model]
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if not messages[0]['content'].startswith(prefix):
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logger.debug(f"Adding prefix '{prefix}' to the first message.")
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messages[0]['content'] = f"{prefix} {messages[0]['content']}"
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random_id = ''.join(random.choices(string.ascii_letters + string.digits, k=7))
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messages[-1]['id'] = random_id
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messages[-1]['role'] = 'user'
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logger.debug(f"Generated message ID: {random_id} for model: {model}")
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-
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if image is not None:
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messages[-1]['data'] = {
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'fileText': '',
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'imageBase64': to_data_uri(image),
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'title': image_name
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}
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messages[-1]['content'] = 'FILE:BB\n$#$\n\n$#$\n' + messages[-1]['content']
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logger.debug("Image data added to the message.")
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data = {
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"messages": messages,
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"id": random_id,
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@@ -337,7 +125,7 @@ class Blackbox:
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"trendingAgentMode": {},
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"isMicMode": False,
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"userSystemPrompt": None,
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"maxTokens":
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"playgroundTopP": 0.9,
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"playgroundTemperature": 0.5,
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"isChromeExt": False,
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@@ -347,103 +135,32 @@ class Blackbox:
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"clickedForceWebSearch": False,
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"visitFromDelta": False,
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"mobileClient": False,
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"userSelectedModel":
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"webSearchMode":
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}
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if
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data["
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-
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357 |
-
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358 |
-
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359 |
-
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-
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361 |
-
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timeout = ClientTimeout(total=60)
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363 |
-
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364 |
-
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365 |
-
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366 |
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async
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367 |
-
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368 |
-
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369 |
-
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response_text = await response.text()
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url_match = re.search(r'https://storage\.googleapis\.com/[^\s\)]+', response_text)
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if url_match:
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image_url = url_match.group(0)
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logger.info(f"Image URL found.")
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yield ImageResponse(image_url, alt=messages[-1]['content'])
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else:
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logger.error("Image URL not found in the response.")
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raise Exception("Image URL not found in the response")
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379 |
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else:
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full_response = ""
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381 |
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search_results_json = ""
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382 |
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try:
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async for chunk, _ in response.content.iter_chunks():
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if chunk:
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decoded_chunk = chunk.decode(errors='ignore')
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decoded_chunk = re.sub(r'\$@\$v=[^$]+\$@\$', '', decoded_chunk)
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387 |
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if decoded_chunk.strip():
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if '$~~~$' in decoded_chunk:
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search_results_json += decoded_chunk
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else:
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full_response += decoded_chunk
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yield decoded_chunk
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logger.info("Finished streaming response chunks.")
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394 |
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except Exception as e:
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395 |
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logger.exception("Error while iterating over response chunks.")
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raise e
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397 |
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if data["webSearchMode"] and search_results_json:
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398 |
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match = re.search(r'\$~~~\$(.*?)\$~~~\$', search_results_json, re.DOTALL)
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399 |
-
if match:
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400 |
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try:
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search_results = json.loads(match.group(1))
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402 |
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formatted_results = "\n\n**Sources:**\n"
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403 |
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for i, result in enumerate(search_results[:5], 1):
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404 |
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formatted_results += f"{i}. [{result['title']}]({result['link']})\n"
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405 |
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logger.info("Formatted search results.")
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yield formatted_results
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except json.JSONDecodeError as je:
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408 |
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logger.error("Failed to parse search results JSON.")
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409 |
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raise je
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410 |
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except RetryError as re:
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411 |
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logger.error(f"All retry attempts failed for {cls.api_endpoint}: {re}")
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412 |
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raise HTTPException(status_code=502, detail="Error communicating with the external API.")
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413 |
-
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414 |
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# FastAPI app setup
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415 |
app = FastAPI()
|
416 |
|
417 |
-
# Add the cleanup task when the app starts
|
418 |
@app.on_event("startup")
|
419 |
async def startup_event():
|
420 |
asyncio.create_task(cleanup_rate_limit_stores())
|
421 |
-
logger.info("Started rate limit store cleanup task.")
|
422 |
|
423 |
-
# Middleware to enhance security and enforce Content-Type for specific endpoints
|
424 |
-
@app.middleware("http")
|
425 |
-
async def security_middleware(request: Request, call_next):
|
426 |
-
client_ip = request.client.host
|
427 |
-
# Enforce that POST requests to /v1/chat/completions must have Content-Type: application/json
|
428 |
-
if request.method == "POST" and request.url.path == "/v1/chat/completions":
|
429 |
-
content_type = request.headers.get("Content-Type")
|
430 |
-
if content_type != "application/json":
|
431 |
-
logger.warning(f"Invalid Content-Type from IP: {client_ip} for path: {request.url.path}")
|
432 |
-
return JSONResponse(
|
433 |
-
status_code=400,
|
434 |
-
content={
|
435 |
-
"error": {
|
436 |
-
"message": "Content-Type must be application/json",
|
437 |
-
"type": "invalid_request_error",
|
438 |
-
"param": None,
|
439 |
-
"code": None
|
440 |
-
}
|
441 |
-
},
|
442 |
-
)
|
443 |
-
response = await call_next(request)
|
444 |
-
return response
|
445 |
-
|
446 |
-
# Request Models
|
447 |
class Message(BaseModel):
|
448 |
role: str
|
449 |
content: str
|
@@ -451,258 +168,38 @@ class Message(BaseModel):
|
|
451 |
class ChatRequest(BaseModel):
|
452 |
model: str
|
453 |
messages: List[Message]
|
454 |
-
|
455 |
-
top_p: Optional[float] = 1.0
|
456 |
-
n: Optional[int] = 1
|
457 |
-
stream: Optional[bool] = False
|
458 |
-
stop: Optional[Union[str, List[str]]] = None
|
459 |
-
max_tokens: Optional[int] = None
|
460 |
-
presence_penalty: Optional[float] = 0.0
|
461 |
-
frequency_penalty: Optional[float] = 0.0
|
462 |
-
logit_bias: Optional[Dict[str, float]] = None
|
463 |
-
user: Optional[str] = None
|
464 |
-
webSearchMode: Optional[bool] = False # Custom parameter
|
465 |
-
|
466 |
-
class TokenizerRequest(BaseModel):
|
467 |
-
text: str
|
468 |
-
|
469 |
-
def calculate_estimated_cost(prompt_tokens: int, completion_tokens: int) -> float:
|
470 |
-
"""
|
471 |
-
Calculate the estimated cost based on the number of tokens.
|
472 |
-
Replace the pricing below with your actual pricing model.
|
473 |
-
"""
|
474 |
-
# Example pricing: $0.00000268 per token
|
475 |
-
cost_per_token = 0.00000268
|
476 |
-
return round((prompt_tokens + completion_tokens) * cost_per_token, 8)
|
477 |
-
|
478 |
-
def create_response(content: str, model: str, finish_reason: Optional[str] = None) -> Dict[str, Any]:
|
479 |
-
return {
|
480 |
-
"id": f"chatcmpl-{uuid.uuid4()}",
|
481 |
-
"object": "chat.completion",
|
482 |
-
"created": int(datetime.now().timestamp()),
|
483 |
-
"model": model,
|
484 |
-
"choices": [
|
485 |
-
{
|
486 |
-
"index": 0,
|
487 |
-
"message": {
|
488 |
-
"role": "assistant",
|
489 |
-
"content": content
|
490 |
-
},
|
491 |
-
"finish_reason": finish_reason
|
492 |
-
}
|
493 |
-
],
|
494 |
-
"usage": None, # To be filled in non-streaming responses
|
495 |
-
}
|
496 |
|
497 |
@app.post("/v1/chat/completions", dependencies=[Depends(rate_limiter_per_ip)])
|
498 |
async def chat_completions(request: ChatRequest, req: Request, api_key: str = Depends(get_api_key)):
|
499 |
-
client_ip = req.client.host
|
500 |
-
# Redact user messages only for logging purposes
|
501 |
-
redacted_messages = [{"role": msg.role, "content": "[redacted]"} for msg in request.messages]
|
502 |
-
|
503 |
-
logger.info(f"Received chat completions request from API key: {api_key} | IP: {client_ip} | Model: {request.model} | Messages: {redacted_messages}")
|
504 |
-
|
505 |
try:
|
506 |
-
|
507 |
-
if request.model not in Blackbox.models and request.model not in Blackbox.model_aliases:
|
508 |
-
logger.warning(f"Attempt to use unavailable model: {request.model} from IP: {client_ip}")
|
509 |
-
raise HTTPException(status_code=400, detail="Requested model is not available.")
|
510 |
|
511 |
-
# Process the request with actual message content, but don't log it
|
512 |
async_generator = Blackbox.create_async_generator(
|
513 |
model=request.model,
|
514 |
-
messages=
|
515 |
-
|
516 |
-
image_name=None,
|
517 |
-
webSearchMode=request.webSearchMode
|
518 |
)
|
519 |
|
520 |
-
|
521 |
-
|
522 |
-
|
523 |
-
|
524 |
-
|
525 |
-
|
526 |
-
|
527 |
-
|
528 |
-
assistant_content += image_markdown
|
529 |
-
response_chunk = create_response(image_markdown, request.model, finish_reason=None)
|
530 |
-
else:
|
531 |
-
assistant_content += chunk
|
532 |
-
# Yield the chunk as a partial choice
|
533 |
-
response_chunk = {
|
534 |
-
"id": f"chatcmpl-{uuid.uuid4()}",
|
535 |
-
"object": "chat.completion.chunk",
|
536 |
-
"created": int(datetime.now().timestamp()),
|
537 |
-
"model": request.model,
|
538 |
-
"choices": [
|
539 |
-
{
|
540 |
-
"index": 0,
|
541 |
-
"delta": {"content": chunk, "role": "assistant"},
|
542 |
-
"finish_reason": None,
|
543 |
-
}
|
544 |
-
],
|
545 |
-
"usage": None, # Usage can be updated if you track tokens in real-time
|
546 |
-
}
|
547 |
-
yield f"data: {json.dumps(response_chunk)}\n\n"
|
548 |
-
|
549 |
-
# After all chunks are sent, send the final message with finish_reason
|
550 |
-
prompt_tokens = sum(len(msg.content.split()) for msg in request.messages)
|
551 |
-
completion_tokens = len(assistant_content.split())
|
552 |
-
total_tokens = prompt_tokens + completion_tokens
|
553 |
-
estimated_cost = calculate_estimated_cost(prompt_tokens, completion_tokens)
|
554 |
-
|
555 |
-
final_response = {
|
556 |
-
"id": f"chatcmpl-{uuid.uuid4()}",
|
557 |
-
"object": "chat.completion",
|
558 |
-
"created": int(datetime.now().timestamp()),
|
559 |
-
"model": request.model,
|
560 |
-
"choices": [
|
561 |
-
{
|
562 |
-
"message": {
|
563 |
-
"role": "assistant",
|
564 |
-
"content": assistant_content
|
565 |
-
},
|
566 |
-
"finish_reason": "stop",
|
567 |
-
"index": 0
|
568 |
-
}
|
569 |
-
],
|
570 |
-
"usage": {
|
571 |
-
"prompt_tokens": prompt_tokens,
|
572 |
-
"completion_tokens": completion_tokens,
|
573 |
-
"total_tokens": total_tokens,
|
574 |
-
"estimated_cost": estimated_cost
|
575 |
-
},
|
576 |
-
}
|
577 |
-
yield f"data: {json.dumps(final_response)}\n\n"
|
578 |
-
yield "data: [DONE]\n\n"
|
579 |
-
except HTTPException as he:
|
580 |
-
error_response = {"error": he.detail}
|
581 |
-
yield f"data: {json.dumps(error_response)}\n\n"
|
582 |
-
except Exception as e:
|
583 |
-
logger.exception(f"Error during streaming response generation from IP: {client_ip}.")
|
584 |
-
error_response = {"error": str(e)}
|
585 |
-
yield f"data: {json.dumps(error_response)}\n\n"
|
586 |
-
|
587 |
-
return StreamingResponse(generate(), media_type="text/event-stream")
|
588 |
-
else:
|
589 |
-
response_content = ""
|
590 |
-
async for chunk in async_generator:
|
591 |
-
if isinstance(chunk, ImageResponse):
|
592 |
-
response_content += f"\n"
|
593 |
-
else:
|
594 |
-
response_content += chunk
|
595 |
-
|
596 |
-
prompt_tokens = sum(len(msg.content.split()) for msg in request.messages)
|
597 |
-
completion_tokens = len(response_content.split())
|
598 |
-
total_tokens = prompt_tokens + completion_tokens
|
599 |
-
estimated_cost = calculate_estimated_cost(prompt_tokens, completion_tokens)
|
600 |
-
|
601 |
-
logger.info(f"Completed non-streaming response generation for API key: {api_key} | IP: {client_ip}")
|
602 |
-
|
603 |
-
return {
|
604 |
-
"id": f"chatcmpl-{uuid.uuid4()}",
|
605 |
-
"object": "chat.completion",
|
606 |
-
"created": int(datetime.now().timestamp()),
|
607 |
-
"model": request.model,
|
608 |
-
"choices": [
|
609 |
-
{
|
610 |
-
"message": {
|
611 |
-
"role": "assistant",
|
612 |
-
"content": response_content
|
613 |
-
},
|
614 |
-
"finish_reason": "stop",
|
615 |
-
"index": 0
|
616 |
-
}
|
617 |
-
],
|
618 |
-
"usage": {
|
619 |
-
"prompt_tokens": prompt_tokens,
|
620 |
-
"completion_tokens": completion_tokens,
|
621 |
-
"total_tokens": total_tokens,
|
622 |
-
"estimated_cost": estimated_cost
|
623 |
-
},
|
624 |
-
}
|
625 |
-
except ModelNotWorkingException as e:
|
626 |
-
logger.warning(f"Model not working: {e} | IP: {client_ip}")
|
627 |
-
raise HTTPException(status_code=503, detail=str(e))
|
628 |
-
except HTTPException as he:
|
629 |
-
logger.warning(f"HTTPException: {he.detail} | IP: {client_ip}")
|
630 |
-
raise he
|
631 |
-
except Exception as e:
|
632 |
-
logger.exception(f"An unexpected error occurred while processing the chat completions request from IP: {client_ip}.")
|
633 |
-
raise HTTPException(status_code=500, detail=str(e))
|
634 |
-
|
635 |
-
# Endpoint: POST /v1/tokenizer
|
636 |
-
@app.post("/v1/tokenizer", dependencies=[Depends(rate_limiter_per_ip)])
|
637 |
-
async def tokenizer(request: TokenizerRequest, req: Request):
|
638 |
-
client_ip = req.client.host
|
639 |
-
text = request.text
|
640 |
-
logger.info(f"Tokenizer requested from IP: {client_ip} | Text length: {len(text)}")
|
641 |
-
|
642 |
-
try:
|
643 |
-
# Example integration: Assuming Blackbox has a tokenizer endpoint
|
644 |
-
result = await Blackbox.process_tokenizer_request(text)
|
645 |
-
token_count = result.get("tokens", len(text.split()))
|
646 |
-
return {"text": text, "tokens": token_count}
|
647 |
-
except HTTPException as he:
|
648 |
-
raise he
|
649 |
except Exception as e:
|
650 |
-
|
651 |
-
raise HTTPException(status_code=500, detail=str(e))
|
652 |
|
653 |
-
# Endpoint: GET /v1/models
|
654 |
@app.get("/v1/models", dependencies=[Depends(rate_limiter_per_ip)])
|
655 |
-
async def get_models(
|
656 |
-
client_ip = req.client.host
|
657 |
-
logger.info(f"Fetching available models from IP: {client_ip}")
|
658 |
return {"data": [{"id": model, "object": "model"} for model in Blackbox.models]}
|
659 |
|
660 |
-
|
661 |
-
|
662 |
-
async def model_status(model: str, req: Request):
|
663 |
-
client_ip = req.client.host
|
664 |
-
logger.info(f"Model status requested for '{model}' from IP: {client_ip}")
|
665 |
-
if model in Blackbox.models:
|
666 |
-
return {"model": model, "status": "available"}
|
667 |
-
elif model in Blackbox.model_aliases and Blackbox.model_aliases[model] in Blackbox.models:
|
668 |
-
actual_model = Blackbox.model_aliases[model]
|
669 |
-
return {"model": actual_model, "status": "available via alias"}
|
670 |
-
else:
|
671 |
-
logger.warning(f"Model not found: {model} from IP: {client_ip}")
|
672 |
-
raise HTTPException(status_code=404, detail="Model not found")
|
673 |
-
|
674 |
-
# Endpoint: GET /v1/health
|
675 |
-
@app.get("/v1/health", dependencies=[Depends(rate_limiter_per_ip)])
|
676 |
-
async def health_check(req: Request):
|
677 |
-
client_ip = req.client.host
|
678 |
-
logger.info(f"Health check requested from IP: {client_ip}")
|
679 |
return {"status": "ok"}
|
680 |
|
681 |
-
# Endpoint: GET /v1/chat/completions (GET method)
|
682 |
-
@app.get("/v1/chat/completions")
|
683 |
-
async def chat_completions_get(req: Request):
|
684 |
-
client_ip = req.client.host
|
685 |
-
logger.info(f"GET request made to /v1/chat/completions from IP: {client_ip}, redirecting to 'about:blank'")
|
686 |
-
return RedirectResponse(url='about:blank')
|
687 |
-
|
688 |
-
# Custom exception handler to match OpenAI's error format
|
689 |
-
@app.exception_handler(HTTPException)
|
690 |
-
async def http_exception_handler(request: Request, exc: HTTPException):
|
691 |
-
client_ip = request.client.host
|
692 |
-
logger.error(f"HTTPException: {exc.detail} | Path: {request.url.path} | IP: {client_ip}")
|
693 |
-
return JSONResponse(
|
694 |
-
status_code=exc.status_code,
|
695 |
-
content={
|
696 |
-
"error": {
|
697 |
-
"message": exc.detail,
|
698 |
-
"type": "invalid_request_error",
|
699 |
-
"param": None,
|
700 |
-
"code": None
|
701 |
-
}
|
702 |
-
},
|
703 |
-
)
|
704 |
-
|
705 |
-
# Run the application
|
706 |
if __name__ == "__main__":
|
707 |
import uvicorn
|
708 |
uvicorn.run(app, host="0.0.0.0", port=8000)
|
|
|
8 |
import asyncio
|
9 |
import time
|
10 |
from collections import defaultdict
|
11 |
+
from typing import List, Dict, Any, Optional, AsyncGenerator, Union
|
12 |
|
13 |
from datetime import datetime
|
|
|
14 |
from aiohttp import ClientSession, ClientTimeout, ClientError
|
15 |
from fastapi import FastAPI, HTTPException, Request, Depends, Header
|
16 |
+
from fastapi.responses import StreamingResponse, JSONResponse
|
17 |
from pydantic import BaseModel
|
|
|
18 |
|
19 |
# Configure logging
|
20 |
logging.basicConfig(
|
|
|
25 |
logger = logging.getLogger(__name__)
|
26 |
|
27 |
# Load environment variables
|
28 |
+
API_KEYS = os.getenv('API_KEYS', '').split(',')
|
29 |
+
RATE_LIMIT = int(os.getenv('RATE_LIMIT', '60'))
|
30 |
+
AVAILABLE_MODELS = os.getenv('AVAILABLE_MODELS', '')
|
|
|
31 |
|
32 |
if not API_KEYS or API_KEYS == ['']:
|
33 |
logger.error("No API keys found. Please set the API_KEYS environment variable.")
|
34 |
raise Exception("API_KEYS environment variable not set.")
|
35 |
|
|
|
36 |
if AVAILABLE_MODELS:
|
37 |
AVAILABLE_MODELS = [model.strip() for model in AVAILABLE_MODELS.split(',') if model.strip()]
|
38 |
else:
|
39 |
+
AVAILABLE_MODELS = []
|
40 |
|
|
|
41 |
rate_limit_store = defaultdict(lambda: {"count": 0, "timestamp": time.time()})
|
42 |
+
CLEANUP_INTERVAL = 60
|
43 |
+
RATE_LIMIT_WINDOW = 60
|
|
|
|
|
44 |
|
45 |
async def cleanup_rate_limit_stores():
|
|
|
|
|
|
|
46 |
while True:
|
47 |
current_time = time.time()
|
48 |
ips_to_delete = [ip for ip, value in rate_limit_store.items() if current_time - value["timestamp"] > RATE_LIMIT_WINDOW * 2]
|
49 |
for ip in ips_to_delete:
|
50 |
del rate_limit_store[ip]
|
|
|
51 |
await asyncio.sleep(CLEANUP_INTERVAL)
|
52 |
|
53 |
async def rate_limiter_per_ip(request: Request):
|
|
|
|
|
|
|
54 |
client_ip = request.client.host
|
55 |
current_time = time.time()
|
56 |
|
|
|
57 |
if current_time - rate_limit_store[client_ip]["timestamp"] > RATE_LIMIT_WINDOW:
|
58 |
rate_limit_store[client_ip] = {"count": 1, "timestamp": current_time}
|
59 |
else:
|
60 |
if rate_limit_store[client_ip]["count"] >= RATE_LIMIT:
|
61 |
+
raise HTTPException(status_code=429, detail='Rate limit exceeded')
|
|
|
62 |
rate_limit_store[client_ip]["count"] += 1
|
63 |
|
64 |
async def get_api_key(request: Request, authorization: str = Header(None)) -> str:
|
|
|
|
|
|
|
65 |
client_ip = request.client.host
|
66 |
if authorization is None or not authorization.startswith('Bearer '):
|
|
|
67 |
raise HTTPException(status_code=401, detail='Invalid authorization header format')
|
68 |
api_key = authorization[7:]
|
69 |
if api_key not in API_KEYS:
|
|
|
70 |
raise HTTPException(status_code=401, detail='Invalid API key')
|
71 |
return api_key
|
72 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
73 |
class ImageResponse:
|
74 |
def __init__(self, url: str, alt: str):
|
75 |
self.url = url
|
76 |
self.alt = alt
|
77 |
|
78 |
+
def to_data_uri(image_base64: str) -> str:
|
79 |
+
return f"data:image/jpeg;base64,{image_base64}"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
80 |
|
81 |
class Blackbox:
|
82 |
url = "https://www.blackbox.ai"
|
83 |
api_endpoint = "https://www.blackbox.ai/api/chat"
|
84 |
working = True
|
85 |
supports_stream = True
|
|
|
|
|
86 |
|
87 |
default_model = 'blackboxai'
|
88 |
+
models = [default_model, 'ImageGeneration', 'gpt-4o', 'llama-3.1-8b']
|
|
|
|
|
|
|
|
|
|
|
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@classmethod
|
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def get_model(cls, model: str) -> Optional[str]:
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if model in cls.models:
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return model
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else:
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+
return cls.default_model
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@classmethod
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async def create_async_generator(
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cls,
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model: str,
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messages: List[Dict[str, str]],
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+
image_base64: Optional[str] = None,
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**kwargs
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) -> AsyncGenerator[Any, None]:
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model = cls.get_model(model)
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if model is None:
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+
raise HTTPException(status_code=400, detail="Model not available")
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108 |
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headers = {
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"accept": "*/*",
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"content-type": "application/json",
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"origin": cls.url,
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"user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/129.0.0.0 Safari/537.36",
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"referer": f"{cls.url}/?model={model}"
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}
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random_id = ''.join(random.choices(string.ascii_letters + string.digits, k=7))
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data = {
|
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"messages": messages,
|
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"id": random_id,
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"trendingAgentMode": {},
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"isMicMode": False,
|
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"userSystemPrompt": None,
|
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+
"maxTokens": 1024,
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"playgroundTopP": 0.9,
|
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"playgroundTemperature": 0.5,
|
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"isChromeExt": False,
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"clickedForceWebSearch": False,
|
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"visitFromDelta": False,
|
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"mobileClient": False,
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138 |
+
"userSelectedModel": model,
|
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+
"webSearchMode": False,
|
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}
|
141 |
|
142 |
+
if image_base64:
|
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+
data["messages"][-1]['data'] = {
|
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+
'imageBase64': to_data_uri(image_base64),
|
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+
'fileText': '',
|
146 |
+
'title': 'Uploaded Image'
|
147 |
+
}
|
148 |
+
data["messages"][-1]['content'] = 'FILE:BB\n$#$\n\n$#$\n' + data["messages"][-1]['content']
|
149 |
+
|
150 |
+
timeout = ClientTimeout(total=60)
|
151 |
+
async with ClientSession(headers=headers, timeout=timeout) as session:
|
152 |
+
async with session.post(cls.api_endpoint, json=data) as response:
|
153 |
+
response.raise_for_status()
|
154 |
+
async for chunk in response.content.iter_any():
|
155 |
+
decoded_chunk = chunk.decode(errors='ignore')
|
156 |
+
yield decoded_chunk
|
157 |
+
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|
158 |
app = FastAPI()
|
159 |
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|
160 |
@app.on_event("startup")
|
161 |
async def startup_event():
|
162 |
asyncio.create_task(cleanup_rate_limit_stores())
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163 |
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|
164 |
class Message(BaseModel):
|
165 |
role: str
|
166 |
content: str
|
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|
168 |
class ChatRequest(BaseModel):
|
169 |
model: str
|
170 |
messages: List[Message]
|
171 |
+
image_base64: Optional[str] = None
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|
172 |
|
173 |
@app.post("/v1/chat/completions", dependencies=[Depends(rate_limiter_per_ip)])
|
174 |
async def chat_completions(request: ChatRequest, req: Request, api_key: str = Depends(get_api_key)):
|
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|
175 |
try:
|
176 |
+
messages = [{"role": msg.role, "content": msg.content} for msg in request.messages]
|
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|
177 |
|
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|
178 |
async_generator = Blackbox.create_async_generator(
|
179 |
model=request.model,
|
180 |
+
messages=messages,
|
181 |
+
image_base64=request.image_base64
|
|
|
|
|
182 |
)
|
183 |
|
184 |
+
response_content = ""
|
185 |
+
async for chunk in async_generator:
|
186 |
+
response_content += chunk
|
187 |
+
|
188 |
+
return {"response": response_content}
|
189 |
+
|
190 |
+
except HTTPException as e:
|
191 |
+
raise e
|
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|
192 |
except Exception as e:
|
193 |
+
raise HTTPException(status_code=500, detail="Internal Server Error")
|
|
|
194 |
|
|
|
195 |
@app.get("/v1/models", dependencies=[Depends(rate_limiter_per_ip)])
|
196 |
+
async def get_models():
|
|
|
|
|
197 |
return {"data": [{"id": model, "object": "model"} for model in Blackbox.models]}
|
198 |
|
199 |
+
@app.get("/v1/health")
|
200 |
+
async def health_check():
|
|
|
|
|
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|
201 |
return {"status": "ok"}
|
202 |
|
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|
203 |
if __name__ == "__main__":
|
204 |
import uvicorn
|
205 |
uvicorn.run(app, host="0.0.0.0", port=8000)
|