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from __future__ import annotations
from aiohttp import ClientSession
import random
import string
import json
import re
from ..typing import AsyncResult, Messages, ImageType
from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
from ..image import ImageResponse, to_data_uri
class Blackbox(AsyncGeneratorProvider, ProviderModelMixin):
label = "Blackbox AI"
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 = ['Image Generation', 'repomap']
userSelectedModel = ['gpt-4o', 'gemini-pro', 'claude-sonnet-3.5', 'blackboxai-pro']
agentMode = {
'Image Generation': {'mode': True, 'id': "ImageGenerationLV45LJp", 'name': "Image Generation"},
}
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"},
#
'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"},
}
model_prefixes = {mode: f"@{value['id']}" for mode, value in trendingAgentMode.items() if mode not in ["gemini-1.5-flash", "llama-3.1-8b", "llama-3.1-70b", "repomap"]}
models = [default_model, *userSelectedModel, *list(agentMode.keys()), *list(trendingAgentMode.keys())]
model_aliases = {
"gemini-flash": "gemini-1.5-flash",
"claude-3.5-sonnet": "claude-sonnet-3.5",
"flux": "Image Generation",
}
@staticmethod
def generate_id(length=7):
characters = string.ascii_letters + string.digits
return ''.join(random.choice(characters) for _ in range(length))
@classmethod
def add_prefix_to_messages(cls, messages: Messages, model: str) -> Messages:
prefix = cls.model_prefixes.get(model, "")
if not prefix:
return messages
new_messages = []
for message in messages:
new_message = message.copy()
if message['role'] == 'user':
new_message['content'] = (prefix + " " + message['content']).strip()
new_messages.append(new_message)
return new_messages
@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
@classmethod
async def create_async_generator(
cls,
model: str,
messages: Messages,
proxy: str = None,
web_search: bool = False,
image: ImageType = None,
image_name: str = None,
**kwargs
) -> AsyncResult:
model = cls.get_model(model)
message_id = cls.generate_id()
messages_with_prefix = cls.add_prefix_to_messages(messages, model)
if image is not None:
messages_with_prefix[-1]['data'] = {
'fileText': '',
'imageBase64': to_data_uri(image),
'title': image_name
}
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': f'{cls.url}/',
'sec-ch-ua': '"Not?A_Brand";v="99", "Chromium";v="130"',
'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/130.0.0.0 Safari/537.36'
}
data = {
"messages": messages_with_prefix,
"id": message_id,
"previewToken": None,
"userId": None,
"codeModelMode": True,
"agentMode": cls.agentMode.get(model, {}) if model in cls.agentMode else {},
"trendingAgentMode": cls.trendingAgentMode.get(model, {}) if model in cls.trendingAgentMode else {},
"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,
"userSelectedModel": model if model in cls.userSelectedModel else None,
"webSearchMode": web_search,
"validated": "69783381-2ce4-4dbd-ac78-35e9063feabc"
}
async with ClientSession(headers=headers) as session:
async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
response.raise_for_status()
response_text = await response.text()
if model in cls.image_models:
image_matches = re.findall(r'!\[.*?\]\((https?://[^\)]+)\)', response_text)
if image_matches:
image_url = image_matches[0]
image_response = ImageResponse(images=[image_url], alt="Generated Image")
yield image_response
return
json_match = re.search(r'\$~~~\$(.*?)\$~~~\$', response_text, re.DOTALL)
if json_match:
search_results = json.loads(json_match.group(1))
answer = response_text.split('$~~~$')[-1].strip()
formatted_response = f"{answer}\n\n**Source:**"
for i, result in enumerate(search_results, 1):
formatted_response += f"\n{i}. {result['title']}: {result['link']}"
yield formatted_response
else:
yield response_text.strip()
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