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-rw-r--r--g4f/Provider/nexra/NexraChatGptV2.py116
1 files changed, 57 insertions, 59 deletions
diff --git a/g4f/Provider/nexra/NexraChatGptV2.py b/g4f/Provider/nexra/NexraChatGptV2.py
index dcfbc910..4ba21b28 100644
--- a/g4f/Provider/nexra/NexraChatGptV2.py
+++ b/g4f/Provider/nexra/NexraChatGptV2.py
@@ -1,26 +1,22 @@
from __future__ import annotations
-from aiohttp import ClientSession
import json
+import requests
-from ...typing import AsyncResult, Messages
-from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ...typing import CreateResult, Messages
+from ..base_provider import ProviderModelMixin, AbstractProvider
from ..helper import format_prompt
-
-class NexraChatGptV2(AsyncGeneratorProvider, ProviderModelMixin):
+class NexraChatGptV2(AbstractProvider, ProviderModelMixin):
label = "Nexra ChatGPT v2"
url = "https://nexra.aryahcr.cc/documentation/chatgpt/en"
api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements"
- working = False
+ working = True
supports_stream = True
default_model = 'chatgpt'
models = [default_model]
-
- model_aliases = {
- "gpt-4": "chatgpt",
- }
+ model_aliases = {"gpt-4": "chatgpt"}
@classmethod
def get_model(cls, model: str) -> str:
@@ -30,63 +26,65 @@ class NexraChatGptV2(AsyncGeneratorProvider, ProviderModelMixin):
return cls.model_aliases[model]
else:
return cls.default_model
-
+
@classmethod
- async def create_async_generator(
+ def create_completion(
cls,
model: str,
messages: Messages,
- proxy: str = None,
- stream: bool = False,
- markdown: bool = False,
+ stream: bool,
**kwargs
- ) -> AsyncResult:
+ ) -> CreateResult:
model = cls.get_model(model)
-
+
headers = {
- "Content-Type": "application/json"
+ 'Content-Type': 'application/json'
+ }
+
+ data = {
+ "messages": [
+ {
+ "role": "user",
+ "content": format_prompt(messages)
+ }
+ ],
+ "stream": stream,
+ "markdown": False,
+ "model": model
}
+
+ response = requests.post(cls.api_endpoint, headers=headers, json=data, stream=stream)
- async with ClientSession(headers=headers) as session:
- prompt = format_prompt(messages)
- data = {
- "messages": [
- {
- "role": "user",
- "content": prompt
- }
- ],
- "stream": stream,
- "markdown": markdown,
- "model": model
- }
+ if stream:
+ return cls.process_streaming_response(response)
+ else:
+ return cls.process_non_streaming_response(response)
- async with session.post(f"{cls.api_endpoint}", json=data, proxy=proxy) as response:
- response.raise_for_status()
+ @classmethod
+ def process_non_streaming_response(cls, response):
+ if response.status_code == 200:
+ try:
+ content = response.text.lstrip('`')
+ data = json.loads(content)
+ return data.get('message', '')
+ except json.JSONDecodeError:
+ return "Error: Unable to decode JSON response"
+ else:
+ return f"Error: {response.status_code}"
- if stream:
- # Streamed response handling (stream=True)
- collected_message = ""
- async for chunk in response.content.iter_any():
- if chunk:
- decoded_chunk = chunk.decode().strip().split("\x1e")
- for part in decoded_chunk:
- if part:
- message_data = json.loads(part)
-
- # Collect messages until 'finish': true
- if 'message' in message_data and message_data['message']:
- collected_message = message_data['message']
-
- # When finish is true, yield the final collected message
- if message_data.get('finish', False):
- yield collected_message
- return
- else:
- # Non-streamed response handling (stream=False)
- response_data = await response.json(content_type=None)
-
- # Yield the message directly from the response
- if 'message' in response_data and response_data['message']:
- yield response_data['message']
- return
+ @classmethod
+ def process_streaming_response(cls, response):
+ full_message = ""
+ for line in response.iter_lines(decode_unicode=True):
+ if line:
+ try:
+ line = line.lstrip('`')
+ data = json.loads(line)
+ if data.get('finish'):
+ break
+ message = data.get('message', '')
+ if message:
+ yield message[len(full_message):]
+ full_message = message
+ except json.JSONDecodeError:
+ pass