from __future__ import annotations import re import random import string import json from aiohttp import ClientSession from ..typing import AsyncResult, Messages, ImageType from ..image import ImageResponse, to_data_uri from .base_provider import AsyncGeneratorProvider, ProviderModelMixin class Blackbox(AsyncGeneratorProvider, ProviderModelMixin): 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' 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', 'ImageGeneration', ] 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", 'blackboxai-pro': "blackboxai-pro", } 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': '@Java 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": f"{url}/?model=blackboxai", "blackboxai-pro": f"{url}/?model=blackboxai-pro", "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", } @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: Messages, proxy: str = None, image: ImageType = None, image_name: str = None, webSearchMode: bool = False, **kwargs ) -> AsyncResult: model = cls.get_model(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", "referer": cls.model_referers.get(model, cls.url), "sec-ch-ua": '"Not;A=Brand";v="24", "Chromium";v="128"', "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/128.0.0.0 Safari/537.36" } if model in cls.model_prefixes: prefix = cls.model_prefixes[model] if not messages[0]['content'].startswith(prefix): messages[0]['content'] = f"{prefix} {messages[0]['content']}" async with ClientSession(headers=headers) as session: if image is not None: messages[-1]["data"] = { "fileText": image_name, "imageBase64": to_data_uri(image) } random_id = ''.join(random.choices(string.ascii_letters + string.digits, k=7)) data = { "messages": messages, "id": random_id, "previewToken": None, "userId": None, "codeModelMode": True, "agentMode": {}, "trendingAgentMode": {}, "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": 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] async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response: response.raise_for_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) yield ImageResponse(image_url, alt=messages[-1]['content']) else: raise Exception("Image URL not found in the response") else: full_response = "" search_results_json = "" async for chunk in response.content.iter_any(): if chunk: decoded_chunk = chunk.decode() 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 if data["webSearchMode"] and search_results_json: match = re.search(r'\$~~~\$(.*?)\$~~~\$', search_results_json, re.DOTALL) if match: 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" yield formatted_results