mirror of
https://github.com/xtekky/gpt4free.git
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311 lines
12 KiB
Python
311 lines
12 KiB
Python
from __future__ import annotations
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from aiohttp import ClientSession
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import os
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import re
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import json
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import random
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import string
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from pathlib import Path
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from typing import Optional
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from ..typing import AsyncResult, Messages, MediaListType
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from ..requests.raise_for_status import raise_for_status
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from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
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from ..image import to_data_uri
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from .helper import render_messages
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from ..providers.response import JsonConversation
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from ..tools.media import merge_media
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from .. import debug
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class Conversation(JsonConversation):
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validated_value: str = None
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chat_id: str = None
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message_history: Messages = []
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def __init__(self, model: str):
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self.model = model
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class Blackbox(AsyncGeneratorProvider, ProviderModelMixin):
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label = "Blackbox AI"
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url = "https://www.blackbox.ai"
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api_endpoint = "https://www.blackbox.ai/api/chat"
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working = True
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active_by_default = True
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supports_stream = True
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supports_system_message = True
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supports_message_history = True
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default_model = "blackboxai"
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default_vision_model = default_model
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models = [
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default_model,
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"gpt-4.1-mini",
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"gpt-4.1-nano",
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"gpt-4",
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"gpt-4o",
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"gpt-4o-mini",
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# Trending agent modes
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'Python Agent',
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'HTML Agent',
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'Builder Agent',
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'Java Agent',
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'JavaScript Agent',
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'React Agent',
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'Android Agent',
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'Flutter Agent',
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'Next.js Agent',
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'AngularJS Agent',
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'Swift Agent',
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'MongoDB Agent',
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'PyTorch Agent',
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'Xcode Agent',
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'Azure Agent',
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'Bitbucket Agent',
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'DigitalOcean Agent',
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'Docker Agent',
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'Electron Agent',
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'Erlang Agent',
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'FastAPI Agent',
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'Firebase Agent',
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'Flask Agent',
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'Git Agent',
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'Gitlab Agent',
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'Go Agent',
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'Godot Agent',
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'Google Cloud Agent',
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'Heroku Agent'
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]
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vision_models = [default_vision_model]
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# Trending agent modes
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trendingAgentMode = {
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'Python Agent': {'mode': True, 'id': "python"},
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'HTML Agent': {'mode': True, 'id': "html"},
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'Builder Agent': {'mode': True, 'id': "builder"},
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'Java Agent': {'mode': True, 'id': "java"},
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'JavaScript Agent': {'mode': True, 'id': "javascript"},
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'React Agent': {'mode': True, 'id': "react"},
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'Android Agent': {'mode': True, 'id': "android"},
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'Flutter Agent': {'mode': True, 'id': "flutter"},
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'Next.js Agent': {'mode': True, 'id': "next.js"},
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'AngularJS Agent': {'mode': True, 'id': "angularjs"},
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'Swift Agent': {'mode': True, 'id': "swift"},
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'MongoDB Agent': {'mode': True, 'id': "mongodb"},
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'PyTorch Agent': {'mode': True, 'id': "pytorch"},
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'Xcode Agent': {'mode': True, 'id': "xcode"},
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'Azure Agent': {'mode': True, 'id': "azure"},
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'Bitbucket Agent': {'mode': True, 'id': "bitbucket"},
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'DigitalOcean Agent': {'mode': True, 'id': "digitalocean"},
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'Docker Agent': {'mode': True, 'id': "docker"},
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'Electron Agent': {'mode': True, 'id': "electron"},
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'Erlang Agent': {'mode': True, 'id': "erlang"},
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'FastAPI Agent': {'mode': True, 'id': "fastapi"},
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'Firebase Agent': {'mode': True, 'id': "firebase"},
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'Flask Agent': {'mode': True, 'id': "flask"},
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'Git Agent': {'mode': True, 'id': "git"},
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'Gitlab Agent': {'mode': True, 'id': "gitlab"},
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'Go Agent': {'mode': True, 'id': "go"},
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'Godot Agent': {'mode': True, 'id': "godot"},
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'Google Cloud Agent': {'mode': True, 'id': "googlecloud"},
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'Heroku Agent': {'mode': True, 'id': "heroku"},
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}
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# Complete list of all models (for authorized users)
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_all_models = list(dict.fromkeys([
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*models,
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*list(trendingAgentMode.keys())
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]))
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@classmethod
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async def fetch_validated(cls, url: str = "https://www.blackbox.ai", force_refresh: bool = False) -> Optional[str]:
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cache_path = Path(os.path.expanduser("~")) / ".g4f" / "cache"
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cache_file = cache_path / 'blackbox.json'
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if not force_refresh and cache_file.exists():
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try:
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with open(cache_file, 'r') as f:
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data = json.load(f)
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if data.get('validated_value'):
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return data['validated_value']
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except Exception as e:
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debug.log(f"Blackbox: Error reading cache: {e}")
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js_file_pattern = r'static/chunks/\d{4}-[a-fA-F0-9]+\.js'
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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})["\']'
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def is_valid_context(text: str) -> bool:
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return any(char + '=' in text for char in 'abcdefghijklmnopqrstuvwxyz')
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async with ClientSession() as session:
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try:
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async with session.get(url) as response:
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if response.status != 200:
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return None
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page_content = await response.text()
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js_files = re.findall(js_file_pattern, page_content)
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for js_file in js_files:
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js_url = f"{url}/_next/{js_file}"
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async with session.get(js_url) as js_response:
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if js_response.status == 200:
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js_content = await js_response.text()
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for match in re.finditer(uuid_pattern, js_content):
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start = max(0, match.start() - 10)
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end = min(len(js_content), match.end() + 10)
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context = js_content[start:end]
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if is_valid_context(context):
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validated_value = match.group(1)
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cache_file.parent.mkdir(exist_ok=True, parents=True)
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try:
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with open(cache_file, 'w') as f:
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json.dump({'validated_value': validated_value}, f)
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except Exception as e:
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debug.log(f"Blackbox: Error writing cache: {e}")
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return validated_value
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except Exception as e:
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debug.log(f"Blackbox: Error retrieving validated_value: {e}")
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return None
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@classmethod
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def generate_id(cls, length: int = 7) -> str:
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chars = string.ascii_letters + string.digits
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return ''.join(random.choice(chars) for _ in range(length))
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@classmethod
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async def create_async_generator(
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cls,
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model: str,
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messages: Messages,
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prompt: str = None,
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proxy: str = None,
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media: MediaListType = None,
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top_p: float = None,
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temperature: float = None,
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max_tokens: int = None,
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conversation: Conversation = None,
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return_conversation: bool = True,
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**kwargs
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) -> AsyncResult:
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model = cls.get_model(model)
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headers = {
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'accept': '*/*',
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'accept-language': 'en-US,en;q=0.9',
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'content-type': 'application/json',
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'origin': 'https://www.blackbox.ai',
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'referer': 'https://www.blackbox.ai/',
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'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/131.0.0.0 Safari/537.36'
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}
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async with ClientSession(headers=headers) as session:
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if conversation is None or not hasattr(conversation, "chat_id"):
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conversation = Conversation(model)
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conversation.validated_value = await cls.fetch_validated()
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conversation.chat_id = cls.generate_id()
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conversation.message_history = []
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current_messages = []
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for i, msg in enumerate(render_messages(messages)):
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msg_id = conversation.chat_id if i == 0 and msg["role"] == "user" else cls.generate_id()
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current_msg = {
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"id": msg_id,
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"content": msg["content"],
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"role": msg["role"]
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}
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current_messages.append(current_msg)
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media = list(merge_media(media, messages))
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if media:
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current_messages[-1]['data'] = {
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"imagesData": [
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{
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"filePath": f"/{image_name}",
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"contents": to_data_uri(image)
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}
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for image, image_name in media
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],
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"fileText": "",
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"title": ""
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}
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data = {
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"messages": current_messages,
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"agentMode": {},
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"id": conversation.chat_id,
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"previewToken": None,
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"userId": None,
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"codeModelMode": True,
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"trendingAgentMode": cls.trendingAgentMode.get(model, {}) if model in cls.trendingAgentMode else {},
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"isMicMode": False,
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"userSystemPrompt": None,
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"maxTokens": max_tokens,
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"playgroundTopP": top_p,
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"playgroundTemperature": temperature,
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"isChromeExt": False,
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"githubToken": "",
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"clickedAnswer2": False,
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"clickedAnswer3": False,
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"clickedForceWebSearch": False,
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"visitFromDelta": False,
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"isMemoryEnabled": False,
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"mobileClient": False,
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"userSelectedModel": None,
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"validated": conversation.validated_value,
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"imageGenerationMode": False,
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"webSearchModePrompt": False,
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"deepSearchMode": False,
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"designerMode": False,
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"domains": None,
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"vscodeClient": False,
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"codeInterpreterMode": False,
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"customProfile": {
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"additionalInfo": "",
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"enableNewChats": False,
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"name": "",
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"occupation": "",
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"traits": []
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},
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"webSearchModeOption": {
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"autoMode": False,
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"webMode": False,
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"offlineMode": False
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},
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"session": None,
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"isPremium": True,
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"subscriptionCache": None,
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"beastMode": False,
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"reasoningMode": False,
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"workspaceId": "",
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"asyncMode": False,
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"webSearchMode": False
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}
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# Continue with the API request and async generator behavior
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async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
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await raise_for_status(response)
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# Collect the full response
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full_response = []
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async for chunk in response.content.iter_any():
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if chunk:
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chunk_text = chunk.decode()
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if chunk_text != "Login to continue using":
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full_response.append(chunk_text)
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yield chunk_text
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full_response_text = ''.join(full_response)
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# Handle conversation history
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if return_conversation:
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conversation.message_history.append({"role": "assistant", "content": full_response_text})
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yield conversation
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