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Blackbox.py
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from __future__ import annotations
from aiohttp import ClientSession, TCPConnector, ClientTimeout
from pathlib import Path
import re
import json
import random
import string
from ..typing import AsyncResult, Messages, ImagesType
from ..requests.raise_for_status import raise_for_status
from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
from ..image import ImageResponse, to_data_uri
from ..cookies import get_cookies_dir
from .helper import format_prompt
from ..providers.response import FinishReason, JsonConversation
class Conversation(JsonConversation):
validated_value: str = None
chat_id: str = None
message_history: Messages = []
def __init__(self, model: str):
self.model = model
class Blackbox(AsyncGeneratorProvider, ProviderModelMixin):
label = "Blackbox AI"
url = "https://www.blackbox.ai"
api_endpoint = "https://www.blackbox.ai/api/chat"
working = True
needs_auth = True
supports_stream = False
supports_system_message = False
supports_message_history = True
default_model = "blackboxai"
default_vision_model = default_model
default_image_model = 'ImageGeneration'
image_models = [default_image_model]
vision_models = [default_vision_model, 'gpt-4o', 'gemini-pro', 'gemini-1.5-flash', 'llama-3.1-8b', 'llama-3.1-70b', 'llama-3.1-405b']
userSelectedModel = ['gpt-4o', 'gemini-pro', 'claude-sonnet-3.5', 'blackboxai-pro']
agentMode = {
'ImageGeneration': {'mode': True, 'id': "ImageGenerationLV45LJp", 'name': "Image Generation"},
#
'meta-llama/Llama-3.3-70B-Instruct-Turbo': {'mode': True, 'id': "meta-llama/Llama-3.3-70B-Instruct-Turbo", 'name': "Meta-Llama-3.3-70B-Instruct-Turbo"},
'mistralai/Mistral-7B-Instruct-v0.2': {'mode': True, 'id': "mistralai/Mistral-7B-Instruct-v0.2", 'name': "Mistral-(7B)-Instruct-v0.2"},
'deepseek-ai/deepseek-llm-67b-chat': {'mode': True, 'id': "deepseek-ai/deepseek-llm-67b-chat", 'name': "DeepSeek-LLM-Chat-(67B)"},
'databricks/dbrx-instruct': {'mode': True, 'id': "databricks/dbrx-instruct", 'name': "DBRX-Instruct"},
'Qwen/QwQ-32B-Preview': {'mode': True, 'id': "Qwen/QwQ-32B-Preview", 'name': "Qwen-QwQ-32B-Preview"},
'NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO': {'mode': True, 'id': "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO", 'name': "Nous-Hermes-2-Mixtral-8x7B-DPO"}
}
trendingAgentMode = {
"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-3.1-405"},
#
'Python Agent': {'mode': True, 'id': "Python Agent"},
'Java Agent': {'mode': True, 'id': "Java Agent"},
'JavaScript Agent': {'mode': True, 'id': "JavaScript Agent"},
'HTML Agent': {'mode': True, 'id': "HTML Agent"},
'Google Cloud Agent': {'mode': True, 'id': "Google Cloud Agent"},
'Android Developer': {'mode': True, 'id': "Android Developer"},
'Swift Developer': {'mode': True, 'id': "Swift Developer"},
'Next.js Agent': {'mode': True, 'id': "Next.js Agent"},
'MongoDB Agent': {'mode': True, 'id': "MongoDB Agent"},
'PyTorch Agent': {'mode': True, 'id': "PyTorch Agent"},
'React Agent': {'mode': True, 'id': "React Agent"},
'Xcode Agent': {'mode': True, 'id': "Xcode Agent"},
'AngularJS Agent': {'mode': True, 'id': "AngularJS Agent"},
#
'blackboxai-pro': {'mode': True, 'id': "BLACKBOXAI-PRO"},
#
'repomap': {'mode': True, 'id': "repomap"},
#
'Heroku Agent': {'mode': True, 'id': "Heroku Agent"},
'Godot Agent': {'mode': True, 'id': "Godot Agent"},
'Go Agent': {'mode': True, 'id': "Go Agent"},
'Gitlab Agent': {'mode': True, 'id': "Gitlab Agent"},
'Git Agent': {'mode': True, 'id': "Git Agent"},
'Flask Agent': {'mode': True, 'id': "Flask Agent"},
'Firebase Agent': {'mode': True, 'id': "Firebase Agent"},
'FastAPI Agent': {'mode': True, 'id': "FastAPI Agent"},
'Erlang Agent': {'mode': True, 'id': "Erlang Agent"},
'Electron Agent': {'mode': True, 'id': "Electron Agent"},
'Docker Agent': {'mode': True, 'id': "Docker Agent"},
'DigitalOcean Agent': {'mode': True, 'id': "DigitalOcean Agent"},
'Bitbucket Agent': {'mode': True, 'id': "Bitbucket Agent"},
'Azure Agent': {'mode': True, 'id': "Azure Agent"},
'Flutter Agent': {'mode': True, 'id': "Flutter Agent"},
'Youtube Agent': {'mode': True, 'id': "Youtube Agent"},
'builder Agent': {'mode': True, 'id': "builder Agent"},
}
models = list(dict.fromkeys([default_model, *userSelectedModel, *list(agentMode.keys()), *list(trendingAgentMode.keys())]))
model_aliases = {
### chat ###
"gpt-4": "gpt-4o",
"gemini-1.5-flash": "gemini-1.5-flash",
"gemini-1.5-pro": "gemini-pro",
"claude-3.5-sonnet": "claude-sonnet-3.5",
"llama-3.3-70b": "meta-llama/Llama-3.3-70B-Instruct-Turbo",
"mixtral-7b": "mistralai/Mistral-7B-Instruct-v0.2",
"deepseek-chat": "deepseek-ai/deepseek-llm-67b-chat",
"dbrx-instruct": "databricks/dbrx-instruct",
"qwq-32b": "Qwen/QwQ-32B-Preview",
"hermes-2-dpo": "NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO",
### image ###
"flux": "ImageGeneration",
}
@classmethod
async def fetch_validated(
cls,
url: str = "https://www.blackbox.ai",
force_refresh: bool = False
) -> Optional[str]:
"""
Asynchronously retrieves the validated_value from the specified URL.
"""
cache_file = Path(get_cookies_dir()) / 'blackbox.json'
if not force_refresh and cache_file.exists():
try:
with open(cache_file, 'r') as f:
data = json.load(f)
if data.get('validated_value'):
return data['validated_value']
except Exception as e:
print(f"Error reading cache: {e}")
js_file_pattern = r'static/chunks/\d{4}-[a-fA-F0-9]+\.js'
uuid_pattern = r'["\']([0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12})["\']'
def is_valid_context(text: str) -> bool:
"""Checks if the context is valid."""
return any(char + '=' in text for char in 'abcdefghijklmnopqrstuvwxyz')
async with ClientSession() as session:
try:
async with session.get(url) as response:
if response.status != 200:
print("Failed to load the page.")
return None
page_content = await response.text()
js_files = re.findall(js_file_pattern, page_content)
for js_file in js_files:
js_url = f"{url}/_next/{js_file}"
async with session.get(js_url) as js_response:
if js_response.status == 200:
js_content = await js_response.text()
for match in re.finditer(uuid_pattern, js_content):
start = max(0, match.start() - 10)
end = min(len(js_content), match.end() + 10)
context = js_content[start:end]
if is_valid_context(context):
validated_value = match.group(1)
# Save to cache
cache_file.parent.mkdir(exist_ok=True)
try:
with open(cache_file, 'w') as f:
json.dump({'validated_value': validated_value}, f)
except Exception as e:
print(f"Error writing cache: {e}")
return validated_value
except Exception as e:
print(f"Error retrieving validated_value: {e}")
return None
@classmethod
def generate_chat_id(cls) -> str:
"""Generate a random chat ID"""
chars = string.ascii_letters + string.digits
return ''.join(random.choice(chars) for _ in range(7))
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
prompt: str = None,
proxy: str = None,
web_search: bool = False,
images: ImagesType = None,
top_p: float = None,
temperature: float = None,
max_tokens: int = None,
conversation: Conversation = None,
return_conversation: bool = False,
**kwargs
) -> AsyncResult:
model = cls.get_model(model)
headers = {
'accept': '*/*',
'accept-language': 'en-US,en;q=0.9',
'content-type': 'application/json',
'origin': 'https://www.blackbox.ai',
'referer': 'https://www.blackbox.ai/',
'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36'
}
connector = TCPConnector(limit=10, ttl_dns_cache=300)
timeout = ClientTimeout(total=30)
async with ClientSession(headers=headers, connector=connector, timeout=timeout) as session:
if conversation is None:
conversation = Conversation(model)
conversation.validated_value = await cls.fetch_validated()
conversation.chat_id = cls.generate_chat_id()
conversation.message_history = []
current_messages = [{"id": conversation.chat_id, "content": format_prompt(messages), "role": "user"}]
conversation.message_history.extend(messages)
if images is not None:
current_messages[-1]['data'] = {
"imagesData": [
{
"filePath": f"/{image_name}",
"contents": to_data_uri(image)
}
for image, image_name in images
],
"fileText": "",
"title": ""
}
data = {
"messages": current_messages,
"agentMode": cls.agentMode.get(model, {}) if model in cls.agentMode else {},
"id": conversation.chat_id,
"previewToken": None,
"userId": None,
"codeModelMode": True,
"trendingAgentMode": cls.trendingAgentMode.get(model, {}) if model in cls.trendingAgentMode else {},
"isMicMode": False,
"userSystemPrompt": None,
"maxTokens": max_tokens,
"playgroundTopP": top_p,
"playgroundTemperature": temperature,
"isChromeExt": False,
"githubToken": "",
"clickedAnswer2": False,
"clickedAnswer3": False,
"clickedForceWebSearch": False,
"visitFromDelta": False,
"mobileClient": False,
"userSelectedModel": model if model in cls.userSelectedModel else None,
"validated": conversation.validated_value,
"imageGenerationMode": False,
"webSearchModePrompt": False,
"deepSearchMode": False,
"domains": None,
"vscodeClient": False,
"codeInterpreterMode": False,
"webSearchMode": web_search
}
async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
await raise_for_status(response)
response_text = await response.text()
parts = response_text.split('$~~~$')
text_to_yield = parts[2] if len(parts) >= 3 else response_text
if not text_to_yield or text_to_yield.isspace():
return
full_response = ""
if model in cls.image_models:
image_url_match = re.search(r'!\[.*?\]\((.*?)\)', text_to_yield)
if image_url_match:
image_url = image_url_match.group(1)
prompt = messages[-1]["content"]
yield ImageResponse(images=[image_url], alt=prompt)
else:
if "Generated by BLACKBOX.AI" in text_to_yield:
conversation.validated_value = await cls.fetch_validated(force_refresh=True)
if conversation.validated_value:
data["validated"] = conversation.validated_value
async with session.post(cls.api_endpoint, json=data, proxy=proxy) as new_response:
await raise_for_status(new_response)
new_response_text = await new_response.text()
new_parts = new_response_text.split('$~~~$')
new_text = new_parts[2] if len(new_parts) >= 3 else new_response_text
if new_text and not new_text.isspace():
yield new_text
full_response = new_text
else:
if text_to_yield and not text_to_yield.isspace():
yield text_to_yield
full_response = text_to_yield
else:
if text_to_yield and not text_to_yield.isspace():
yield text_to_yield
full_response = text_to_yield
if return_conversation:
conversation.message_history.append({"role": "assistant", "content": full_response})
yield conversation
yield FinishReason("stop")