from __future__ import annotations from aiohttp import ClientSession import os import re import json import random import string from pathlib import Path from typing import Optional from datetime import datetime, timedelta from ..typing import AsyncResult, Messages, MediaListType from ..requests.raise_for_status import raise_for_status from .base_provider import AsyncGeneratorProvider, ProviderModelMixin from ..image import to_data_uri from .helper import render_messages from ..providers.response import JsonConversation from ..tools.media import merge_media from .. import debug 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 active_by_default = True supports_stream = True supports_system_message = True supports_message_history = True default_model = "blackboxai" default_vision_model = default_model models = [ default_model, "gpt-4.1-mini", "gpt-4.1-nano", "gpt-4", "gpt-4o", "gpt-4o-mini", # Trending agent modes 'Python Agent', 'HTML Agent', 'Builder Agent', 'Java Agent', 'JavaScript Agent', 'React Agent', 'Android Agent', 'Flutter Agent', 'Next.js Agent', 'AngularJS Agent', 'Swift Agent', 'MongoDB Agent', 'PyTorch Agent', 'Xcode Agent', 'Azure Agent', 'Bitbucket Agent', 'DigitalOcean Agent', 'Docker Agent', 'Electron Agent', 'Erlang Agent', 'FastAPI Agent', 'Firebase Agent', 'Flask Agent', 'Git Agent', 'Gitlab Agent', 'Go Agent', 'Godot Agent', 'Google Cloud Agent', 'Heroku Agent' ] vision_models = [default_vision_model] # Trending agent modes trendingAgentMode = { 'Python Agent': {'mode': True, 'id': "python"}, 'HTML Agent': {'mode': True, 'id': "html"}, 'Builder Agent': {'mode': True, 'id': "builder"}, 'Java Agent': {'mode': True, 'id': "java"}, 'JavaScript Agent': {'mode': True, 'id': "javascript"}, 'React Agent': {'mode': True, 'id': "react"}, 'Android Agent': {'mode': True, 'id': "android"}, 'Flutter Agent': {'mode': True, 'id': "flutter"}, 'Next.js Agent': {'mode': True, 'id': "next.js"}, 'AngularJS Agent': {'mode': True, 'id': "angularjs"}, 'Swift Agent': {'mode': True, 'id': "swift"}, 'MongoDB Agent': {'mode': True, 'id': "mongodb"}, 'PyTorch Agent': {'mode': True, 'id': "pytorch"}, 'Xcode Agent': {'mode': True, 'id': "xcode"}, 'Azure Agent': {'mode': True, 'id': "azure"}, 'Bitbucket Agent': {'mode': True, 'id': "bitbucket"}, 'DigitalOcean Agent': {'mode': True, 'id': "digitalocean"}, 'Docker Agent': {'mode': True, 'id': "docker"}, 'Electron Agent': {'mode': True, 'id': "electron"}, 'Erlang Agent': {'mode': True, 'id': "erlang"}, 'FastAPI Agent': {'mode': True, 'id': "fastapi"}, 'Firebase Agent': {'mode': True, 'id': "firebase"}, 'Flask Agent': {'mode': True, 'id': "flask"}, 'Git Agent': {'mode': True, 'id': "git"}, 'Gitlab Agent': {'mode': True, 'id': "gitlab"}, 'Go Agent': {'mode': True, 'id': "go"}, 'Godot Agent': {'mode': True, 'id': "godot"}, 'Google Cloud Agent': {'mode': True, 'id': "googlecloud"}, 'Heroku Agent': {'mode': True, 'id': "heroku"}, } # Complete list of all models (for authorized users) _all_models = list(dict.fromkeys([ *models, *list(trendingAgentMode.keys()) ])) @classmethod async def fetch_validated(cls, url: str = "https://www.blackbox.ai", force_refresh: bool = False) -> Optional[str]: cache_path = Path(os.path.expanduser("~")) / ".g4f" / "cache" cache_file = cache_path / '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: debug.log(f"Blackbox: 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: 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: 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) cache_file.parent.mkdir(exist_ok=True, parents=True) try: with open(cache_file, 'w') as f: json.dump({'validated_value': validated_value}, f) except Exception as e: debug.log(f"Blackbox: Error writing cache: {e}") return validated_value except Exception as e: debug.log(f"Blackbox: Error retrieving validated_value: {e}") return None @classmethod def generate_id(cls, length: int = 7) -> str: chars = string.ascii_letters + string.digits return ''.join(random.choice(chars) for _ in range(length)) @classmethod async def create_async_generator( cls, model: str, messages: Messages, prompt: str = None, proxy: str = None, media: MediaListType = None, top_p: float = None, temperature: float = None, max_tokens: int = None, conversation: Conversation = None, return_conversation: bool = True, **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' } async with ClientSession(headers=headers) as session: if conversation is None or not hasattr(conversation, "chat_id"): conversation = Conversation(model) conversation.validated_value = await cls.fetch_validated() conversation.chat_id = cls.generate_id() conversation.message_history = [] current_messages = [] for i, msg in enumerate(render_messages(messages)): msg_id = conversation.chat_id if i == 0 and msg["role"] == "user" else cls.generate_id() current_msg = { "id": msg_id, "content": msg["content"], "role": msg["role"] } current_messages.append(current_msg) media = list(merge_media(media, messages)) if media: current_messages[-1]['data'] = { "imagesData": [ { "filePath": f"/{image_name}", "contents": to_data_uri(image) } for image, image_name in media ], "fileText": "", "title": "" } data = { "messages": current_messages, "agentMode": {}, "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, "isMemoryEnabled": False, "mobileClient": False, "userSelectedModel": None, "validated": conversation.validated_value, "imageGenerationMode": False, "webSearchModePrompt": False, "deepSearchMode": False, "designerMode": False, "domains": None, "vscodeClient": False, "codeInterpreterMode": False, "customProfile": { "additionalInfo": "", "enableNewChats": False, "name": "", "occupation": "", "traits": [] }, "webSearchModeOption": { "autoMode": False, "webMode": False, "offlineMode": False }, "session": None, "isPremium": True, "subscriptionCache": None, "beastMode": False, "reasoningMode": False, "workspaceId": "", "asyncMode": False, "webSearchMode": False } # Continue with the API request and async generator behavior async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response: await raise_for_status(response) # Collect the full response full_response = [] async for chunk in response.content.iter_any(): if chunk: chunk_text = chunk.decode() if chunk_text != "Login to continue using": full_response.append(chunk_text) yield chunk_text full_response_text = ''.join(full_response) # Handle conversation history if return_conversation: conversation.message_history.append({"role": "assistant", "content": full_response_text}) yield conversation