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()