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Diffstat (limited to 'g4f/Provider/ChatifyAI.py')
-rw-r--r-- | g4f/Provider/ChatifyAI.py | 75 |
1 files changed, 75 insertions, 0 deletions
diff --git a/g4f/Provider/ChatifyAI.py b/g4f/Provider/ChatifyAI.py new file mode 100644 index 00000000..c5b4a104 --- /dev/null +++ b/g4f/Provider/ChatifyAI.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +from aiohttp import ClientSession + +from ..typing import AsyncResult, Messages +from .base_provider import AsyncGeneratorProvider, ProviderModelMixin +from .helper import format_prompt + + +class ChatifyAI(AsyncGeneratorProvider, ProviderModelMixin): + url = "https://chatify-ai.vercel.app" + api_endpoint = "https://chatify-ai.vercel.app/api/chat" + working = True + supports_stream = False + supports_system_message = True + supports_message_history = True + + default_model = 'llama-3.1' + models = [default_model] + + @classmethod + def get_model(cls, model: str) -> str: + return cls.default_model + + @classmethod + async def create_async_generator( + cls, + model: str, + messages: Messages, + proxy: str = None, + **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", + "priority": "u=1, i", + "referer": f"{cls.url}/", + "sec-ch-ua": '"Chromium";v="129", "Not=A?Brand";v="8"', + "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/129.0.0.0 Safari/537.36" + } + async with ClientSession(headers=headers) as session: + data = { + "messages": [{"role": "user", "content": format_prompt(messages)}] + } + async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response: + response.raise_for_status() + response_text = await response.text() + + # Фільтруємо та форматуємо відповідь + filtered_response = cls.filter_response(response_text) + yield filtered_response + + @staticmethod + def filter_response(response_text: str) -> str: + # Розділяємо рядок на частини + parts = response_text.split('"') + + # Вибираємо лише текстові частини (кожна друга частина) + text_parts = parts[1::2] + + # Об'єднуємо текстові частини + clean_text = ''.join(text_parts) + + return clean_text |