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-rw-r--r--g4f/Provider/ChatifyAI.py83
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diff --git a/g4f/Provider/ChatifyAI.py b/g4f/Provider/ChatifyAI.py
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+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]
+ model_aliases = {
+ "llama-3.1-8b": "llama-3.1",
+ }
+
+ @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.get(model, cls.default_model)
+ else:
+ 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