10 files changed, 390 insertions, 293 deletions

diff --git a/g4f/Provider/AI365VIP.py b/g4f/Provider/AI365VIP.py
index c7ebf6b5..511ad568 100644
--- a/g4f/Provider/AI365VIP.py
+++ b/g4f/Provider/AI365VIP.py
@@ -10,7 +10,7 @@ from .helper import format_prompt
 class AI365VIP(AsyncGeneratorProvider, ProviderModelMixin):
     url = "https://chat.ai365vip.com"
     api_endpoint = "/api/chat"
-    working = True
+    working = False
     default_model = 'gpt-3.5-turbo'
     models = [
         'gpt-3.5-turbo',
diff --git a/g4f/Provider/AiMathGPT.py b/g4f/Provider/AiMathGPT.py
index 4399320a..90931691 100644
--- a/g4f/Provider/AiMathGPT.py
+++ b/g4f/Provider/AiMathGPT.py
@@ -60,10 +60,6 @@ class AiMathGPT(AsyncGeneratorProvider, ProviderModelMixin):
             data = {
                 "messages": [
                     {
-                        "role": "system",
-                        "content": ""
-                    },
-                    {
                         "role": "user",
                         "content": format_prompt(messages)
                     }
diff --git a/g4f/Provider/Blackbox.py b/g4f/Provider/Blackbox.py
index 5cd43eed..4052893a 100644
--- a/g4f/Provider/Blackbox.py
+++ b/g4f/Provider/Blackbox.py
@@ -51,7 +51,6 @@ class Blackbox(AsyncGeneratorProvider, ProviderModelMixin):
         'ReactAgent',
         'XcodeAgent',
         'AngularJSAgent',
-        'RepoMap',
     ]
 
     agentMode = {
@@ -78,7 +77,6 @@ class Blackbox(AsyncGeneratorProvider, ProviderModelMixin):
         'ReactAgent': {'mode': True, 'id': "React Agent"},
         'XcodeAgent': {'mode': True, 'id': "Xcode Agent"},
         'AngularJSAgent': {'mode': True, 'id': "AngularJS Agent"},
-        'RepoMap': {'mode': True, 'id': "repomap"},
     }
 
     userSelectedModel = {
@@ -174,7 +172,7 @@ class Blackbox(AsyncGeneratorProvider, ProviderModelMixin):
         proxy: Optional[str] = None,
         image: ImageType = None,
         image_name: str = None,
-        websearch: bool = False,
+        web_search: bool = False,
         **kwargs
     ) -> AsyncGenerator[Union[str, ImageResponse], None]:
         """
@@ -186,7 +184,7 @@ class Blackbox(AsyncGeneratorProvider, ProviderModelMixin):
             proxy (Optional[str]): Proxy URL, if needed.
             image (ImageType): Image data to be processed, if any.
             image_name (str): Name of the image file, if an image is provided.
-            websearch (bool): Enables or disables web search mode.
+            web_search (bool): Enables or disables web search mode.
             **kwargs: Additional keyword arguments.
 
         Yields:
@@ -276,7 +274,7 @@ class Blackbox(AsyncGeneratorProvider, ProviderModelMixin):
             "clickedForceWebSearch": False,
             "visitFromDelta": False,
             "mobileClient": False,
-            "webSearchMode": websearch,
+            "webSearchMode": web_search,
             "userSelectedModel": cls.userSelectedModel.get(model, model)
         }
 
@@ -313,7 +311,7 @@ class Blackbox(AsyncGeneratorProvider, ProviderModelMixin):
                         else:
                             yield cleaned_response
                     else:
-                        if websearch:
+                        if web_search:
                             match = re.search(r'\$~~~\$(.*?)\$~~~\$', cleaned_response, re.DOTALL)
                             if match:
                                 source_part = match.group(1).strip()
diff --git a/g4f/Provider/GizAI.py b/g4f/Provider/GizAI.py
new file mode 100644
index 00000000..127edc9e
--- /dev/null
+++ b/g4f/Provider/GizAI.py
@@ -0,0 +1,151 @@
+from __future__ import annotations
+
+import json
+from aiohttp import ClientSession
+
+from ..typing import AsyncResult, Messages
+from ..image import ImageResponse
+from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from .helper import format_prompt
+
+class GizAI(AsyncGeneratorProvider, ProviderModelMixin):
+    url = "https://app.giz.ai/assistant/"
+    api_endpoint = "https://app.giz.ai/api/data/users/inferenceServer.infer"
+    working = True
+    
+    supports_system_message = True
+    supports_message_history = True
+    
+    # Chat models
+    default_model = 'chat-gemini-flash'
+    chat_models = [
+        default_model,
+        'chat-gemini-pro',
+        'chat-gpt4m',
+        'chat-gpt4',
+        'claude-sonnet',
+        'claude-haiku',
+        'llama-3-70b',
+        'llama-3-8b',
+        'mistral-large',
+        'chat-o1-mini'
+    ]
+
+    # Image models
+    image_models = [
+        'flux1',
+        'sdxl',
+        'sd',
+        'sd35',
+    ]
+
+    models = [*chat_models, *image_models]
+    
+    model_aliases = {
+        # Chat model aliases
+        "gemini-flash": "chat-gemini-flash",
+        "gemini-pro": "chat-gemini-pro",
+        "gpt-4o-mini": "chat-gpt4m",
+        "gpt-4o": "chat-gpt4",
+        "claude-3.5-sonnet": "claude-sonnet",
+        "claude-3-haiku": "claude-haiku",
+        "llama-3.1-70b": "llama-3-70b",
+        "llama-3.1-8b": "llama-3-8b",
+        "o1-mini": "chat-o1-mini",
+        # Image model aliases
+        "sd-1.5": "sd",
+        "sd-3.5": "sd35",
+        "flux-schnell": "flux1",
+    }
+
+    @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[model]
+        else:
+            return cls.default_model
+
+    @classmethod
+    def is_image_model(cls, model: str) -> bool:
+        return model in cls.image_models
+
+    @classmethod
+    async def create_async_generator(
+        cls,
+        model: str,
+        messages: Messages,
+        proxy: str = None,
+        **kwargs
+    ) -> AsyncResult:
+        model = cls.get_model(model)
+        
+        headers = {
+            'Accept': 'application/json, text/plain, */*',
+            'Accept-Language': 'en-US,en;q=0.9',
+            'Cache-Control': 'no-cache',
+            'Connection': 'keep-alive',
+            'Content-Type': 'application/json',
+            'Origin': 'https://app.giz.ai',
+            'Pragma': 'no-cache',
+            '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/130.0.0.0 Safari/537.36',
+            'sec-ch-ua': '"Not?A_Brand";v="99", "Chromium";v="130"',
+            'sec-ch-ua-mobile': '?0',
+            'sec-ch-ua-platform': '"Linux"'
+        }
+
+        async with ClientSession() as session:
+            if cls.is_image_model(model):
+                # Image generation
+                prompt = messages[-1]["content"]
+                data = {
+                    "model": model,
+                    "input": {
+                        "width": "1024",
+                        "height": "1024",
+                        "steps": 4,
+                        "output_format": "webp",
+                        "batch_size": 1,
+                        "mode": "plan",
+                        "prompt": prompt
+                    }
+                }
+                async with session.post(
+                    cls.api_endpoint,
+                    headers=headers,
+                    data=json.dumps(data),
+                    proxy=proxy
+                ) as response:
+                    response.raise_for_status()
+                    response_data = await response.json()
+                    if response_data.get('status') == 'completed' and response_data.get('output'):
+                        for url in response_data['output']:
+                            yield ImageResponse(images=url, alt="Generated Image")
+            else:
+                # Chat completion
+                data = {
+                    "model": model,
+                    "input": {
+                        "messages": [
+                            {
+                                "type": "human",
+                                "content": format_prompt(messages)
+                            }
+                        ],
+                        "mode": "plan"
+                    },
+                    "noStream": True
+                }
+                async with session.post(
+                    cls.api_endpoint,
+                    headers=headers,
+                    data=json.dumps(data),
+                    proxy=proxy
+                ) as response:
+                    response.raise_for_status()
+                    result = await response.json()
+                    yield result.get('output', '')
diff --git a/g4f/Provider/__init__.py b/g4f/Provider/__init__.py
index 8f36606b..1caf8aaf 100644
--- a/g4f/Provider/__init__.py
+++ b/g4f/Provider/__init__.py
@@ -47,6 +47,7 @@ from .FreeChatgpt      import FreeChatgpt
 from .FreeGpt          import FreeGpt
 from .FreeNetfly       import FreeNetfly
 from .GeminiPro        import GeminiPro
+from .GizAI            import GizAI
 from .GPROChat         import GPROChat
 from .HuggingChat      import HuggingChat
 from .HuggingFace      import HuggingFace
diff --git a/g4f/Provider/nexra/NexraChatGPT.py b/g4f/Provider/nexra/NexraChatGPT.py
index fc5051ee..074a0363 100644
--- a/g4f/Provider/nexra/NexraChatGPT.py
+++ b/g4f/Provider/nexra/NexraChatGPT.py
@@ -1,45 +1,52 @@
 from __future__ import annotations
 
+import asyncio
 import json
 import requests
+from typing import Any, Dict
 
-from ...typing import CreateResult, Messages
-from ..base_provider import ProviderModelMixin, AbstractProvider
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
 from ..helper import format_prompt
 
-class NexraChatGPT(AbstractProvider, ProviderModelMixin):
+
+class NexraChatGPT(AsyncGeneratorProvider, ProviderModelMixin):
     label = "Nexra ChatGPT"
     url = "https://nexra.aryahcr.cc/documentation/chatgpt/en"
-    api_endpoint = "https://nexra.aryahcr.cc/api/chat/gpt"
+    api_endpoint_nexra_chatgpt = "https://nexra.aryahcr.cc/api/chat/gpt"
+    api_endpoint_nexra_chatgpt4o = "https://nexra.aryahcr.cc/api/chat/complements"
+    api_endpoint_nexra_chatgpt_v2 = "https://nexra.aryahcr.cc/api/chat/complements"
+    api_endpoint_nexra_gptweb = "https://nexra.aryahcr.cc/api/chat/gptweb"
     working = True
+    supports_system_message = True
+    supports_message_history = True
+    supports_stream = True
     
     default_model = 'gpt-3.5-turbo'
-    models = ['gpt-4', 'gpt-4-0613', 'gpt-4-0314', 'gpt-4-32k-0314', default_model, 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0613', 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0301', 'text-davinci-003', 'text-davinci-002', 'code-davinci-002', 'gpt-3', 'text-curie-001', 'text-babbage-001', 'text-ada-001', 'davinci', 'curie', 'babbage', 'ada', 'babbage-002', 'davinci-002']
+    nexra_chatgpt = [
+        'gpt-4', 'gpt-4-0613', 'gpt-4-0314', 'gpt-4-32k-0314',
+        default_model, 'gpt-3.5-turbo-16k', 'gpt-3.5-turbo-0613', 'gpt-3.5-turbo-16k-0613', 'gpt-3.5-turbo-0301', 
+        'text-davinci-003', 'text-davinci-002', 'code-davinci-002', 'gpt-3', 'text-curie-001', 'text-babbage-001', 'text-ada-001', 'davinci', 'curie', 'babbage', 'ada', 'babbage-002', 'davinci-002'
+    ]
+    nexra_chatgpt4o = ['gpt-4o']
+    nexra_chatgptv2 = ['chatgpt']
+    nexra_gptweb = ['gptweb']
+    models = nexra_chatgpt + nexra_chatgpt4o + nexra_chatgptv2 + nexra_gptweb
     
     model_aliases = {
         "gpt-4": "gpt-4-0613",
-        "gpt-4": "gpt-4-32k",
-        "gpt-4": "gpt-4-0314",
-        "gpt-4": "gpt-4-32k-0314",
-        
+        "gpt-4-32k": "gpt-4-32k-0314",
         "gpt-3.5-turbo": "gpt-3.5-turbo-16k",
-        "gpt-3.5-turbo": "gpt-3.5-turbo-0613",
-        "gpt-3.5-turbo": "gpt-3.5-turbo-16k-0613",
-        "gpt-3.5-turbo": "gpt-3.5-turbo-0301",
-        
+        "gpt-3.5-turbo-0613": "gpt-3.5-turbo-16k-0613",
         "gpt-3": "text-davinci-003",
-        "gpt-3": "text-davinci-002",
-        "gpt-3": "code-davinci-002",
-        "gpt-3": "text-curie-001",
-        "gpt-3": "text-babbage-001",
-        "gpt-3": "text-ada-001",
-        "gpt-3": "text-ada-001",
-        "gpt-3": "davinci",
-        "gpt-3": "curie",
-        "gpt-3": "babbage",
-        "gpt-3": "ada",
-        "gpt-3": "babbage-002",
-        "gpt-3": "davinci-002",
+        "text-davinci-002": "code-davinci-002",
+        "text-curie-001": "text-babbage-001",
+        "text-ada-001": "davinci",
+        "curie": "babbage",
+        "ada": "babbage-002",
+        "davinci-002": "davinci-002",
+        "chatgpt": "chatgpt",
+        "gptweb": "gptweb"
     }
 
     @classmethod
@@ -50,40 +57,229 @@ class NexraChatGPT(AbstractProvider, ProviderModelMixin):
             return cls.model_aliases[model]
         else:
             return cls.default_model
-            
+
     @classmethod
-    def create_completion(
+    async def create_async_generator(
         cls,
         model: str,
         messages: Messages,
+        stream: bool = False,
         proxy: str = None,
         markdown: bool = False,
         **kwargs
-    ) -> CreateResult:
-        model = cls.get_model(model)
+    ) -> AsyncResult:
+        if model in cls.nexra_chatgpt:
+            async for chunk in cls._create_async_generator_nexra_chatgpt(model, messages, proxy, **kwargs):
+                yield chunk
+        elif model in cls.nexra_chatgpt4o:
+            async for chunk in cls._create_async_generator_nexra_chatgpt4o(model, messages, stream, proxy, markdown, **kwargs):
+                yield chunk
+        elif model in cls.nexra_chatgptv2:
+            async for chunk in cls._create_async_generator_nexra_chatgpt_v2(model, messages, stream, proxy, markdown, **kwargs):
+                yield chunk
+        elif model in cls.nexra_gptweb:
+            async for chunk in cls._create_async_generator_nexra_gptweb(model, messages, proxy, **kwargs):
+                yield chunk
 
+    @classmethod
+    async def _create_async_generator_nexra_chatgpt(
+        cls,
+        model: str,
+        messages: Messages,
+        proxy: str = None,
+        markdown: bool = False,
+        **kwargs
+    ) -> AsyncResult:
+        model = cls.get_model(model)
+        
         headers = {
-            'Content-Type': 'application/json'
+            "Content-Type": "application/json"
         }
         
+        prompt = format_prompt(messages)
         data = {
-            "messages": [],
-            "prompt": format_prompt(messages),
+            "messages": messages,
+            "prompt": prompt,
             "model": model,
             "markdown": markdown
         }
+
+        loop = asyncio.get_event_loop()
+        try:
+            response = await loop.run_in_executor(None, cls._sync_post_request, cls.api_endpoint_nexra_chatgpt, data, headers, proxy)
+            filtered_response = cls._filter_response(response)
+            
+            for chunk in filtered_response:
+                yield chunk
+        except Exception as e:
+            print(f"Error during API request (nexra_chatgpt): {e}")
+
+    @classmethod
+    async def _create_async_generator_nexra_chatgpt4o(
+        cls,
+        model: str,
+        messages: Messages,
+        stream: bool = False,
+        proxy: str = None,
+        markdown: bool = False,
+        **kwargs
+    ) -> AsyncResult:
+        model = cls.get_model(model)
         
-        response = requests.post(cls.api_endpoint, headers=headers, json=data)
+        headers = {
+            "Content-Type": "application/json"
+        }
+        
+        prompt = format_prompt(messages)
+        data = {
+            "messages": [
+                {
+                    "role": "user",
+                    "content": prompt
+                }
+            ],
+            "stream": stream,
+            "markdown": markdown,
+            "model": model
+        }
 
-        return cls.process_response(response)
+        loop = asyncio.get_event_loop()
+        try:
+            response = await loop.run_in_executor(None, cls._sync_post_request, cls.api_endpoint_nexra_chatgpt4o, data, headers, proxy, stream)
+            
+            if stream:
+                async for chunk in cls._process_streaming_response(response):
+                    yield chunk
+            else:
+                for chunk in cls._process_non_streaming_response(response):
+                    yield chunk
+        except Exception as e:
+            print(f"Error during API request (nexra_chatgpt4o): {e}")
 
     @classmethod
-    def process_response(cls, response):
+    async def _create_async_generator_nexra_chatgpt_v2(
+        cls,
+        model: str,
+        messages: Messages,
+        stream: bool = False,
+        proxy: str = None,
+        markdown: bool = False,
+        **kwargs
+    ) -> AsyncResult:
+        model = cls.get_model(model)
+        
+        headers = {
+            "Content-Type": "application/json"
+        }
+        
+        prompt = format_prompt(messages)
+        data = {
+            "messages": [
+                {
+                    "role": "user",
+                    "content": prompt
+                }
+            ],
+            "stream": stream,
+            "markdown": markdown,
+            "model": model
+        }
+
+        loop = asyncio.get_event_loop()
+        try:
+            response = await loop.run_in_executor(None, cls._sync_post_request, cls.api_endpoint_nexra_chatgpt_v2, data, headers, proxy, stream)
+            
+            if stream:
+                async for chunk in cls._process_streaming_response(response):
+                    yield chunk
+            else:
+                for chunk in cls._process_non_streaming_response(response):
+                    yield chunk
+        except Exception as e:
+            print(f"Error during API request (nexra_chatgpt_v2): {e}")
+
+    @classmethod
+    async def _create_async_generator_nexra_gptweb(
+        cls,
+        model: str,
+        messages: Messages,
+        proxy: str = None,
+        markdown: bool = False,
+        **kwargs
+    ) -> AsyncResult:
+        model = cls.get_model(model)
+        
+        headers = {
+            "Content-Type": "application/json"
+        }
+        
+        prompt = format_prompt(messages)
+        data = {
+            "prompt": prompt,
+            "markdown": markdown,
+        }
+
+        loop = asyncio.get_event_loop()
+        try:
+            response = await loop.run_in_executor(None, cls._sync_post_request, cls.api_endpoint_nexra_gptweb, data, headers, proxy)
+            
+            for chunk in response.iter_content(1024):
+                if chunk:
+                    decoded_chunk = chunk.decode().lstrip('_')
+                    try:
+                        response_json = json.loads(decoded_chunk)
+                        if response_json.get("status"):
+                            yield response_json.get("gpt", "")
+                    except json.JSONDecodeError:
+                        continue
+        except Exception as e:
+            print(f"Error during API request (nexra_gptweb): {e}")
+
+    @staticmethod
+    def _sync_post_request(url: str, data: Dict[str, Any], headers: Dict[str, str], proxy: str = None, stream: bool = False) -> requests.Response:
+        proxies = {
+            "http": proxy,
+            "https": proxy,
+        } if proxy else None
+        
+        try:
+            response = requests.post(url, json=data, headers=headers, proxies=proxies, stream=stream)
+            response.raise_for_status()
+            return response
+        except requests.RequestException as e:
+            print(f"Request failed: {e}")
+            raise
+
+    @staticmethod
+    def _process_non_streaming_response(response: requests.Response) -> str:
         if response.status_code == 200:
             try:
-                data = response.json()
-                return data.get('gpt', '')
+                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}"
+
+    @staticmethod
+    async def _process_streaming_response(response: requests.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
+
+    @staticmethod
+    def _filter_response(response: requests.Response) -> str:
+        response_json = response.json()
+        return response_json.get("gpt", "")
diff --git a/g4f/Provider/nexra/NexraChatGPT4o.py b/g4f/Provider/nexra/NexraChatGPT4o.py
deleted file mode 100644
index 126d32b8..00000000
--- a/g4f/Provider/nexra/NexraChatGPT4o.py
+++ /dev/null
@@ -1,86 +0,0 @@
-from __future__ import annotations
-
-import json
-import requests
-
-from ...typing import CreateResult, Messages
-from ..base_provider import ProviderModelMixin, AbstractProvider
-from ..helper import format_prompt
-
-class NexraChatGPT4o(AbstractProvider, ProviderModelMixin):
-    label = "Nexra ChatGPT4o"
-    url = "https://nexra.aryahcr.cc/documentation/chatgpt/en"
-    api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements"
-    working = True
-    supports_stream = True
-    
-    default_model = "gpt-4o"
-    models = [default_model]
-
-    @classmethod
-    def get_model(cls, model: str) -> str:
-        return cls.default_model
-            
-    @classmethod
-    def create_completion(
-        cls,
-        model: str,
-        messages: Messages,
-        stream: bool,
-        proxy: str = None,
-        markdown: bool = False,
-        **kwargs
-    ) -> CreateResult:
-        model = cls.get_model(model)
-
-        headers = {
-            'Content-Type': 'application/json'
-        }
-        
-        data = {
-            "messages": [
-                {
-                    "role": "user",
-                    "content": format_prompt(messages)
-                }
-            ],
-            "stream": stream,
-            "markdown": markdown,
-            "model": model
-        }
-        
-        response = requests.post(cls.api_endpoint, headers=headers, json=data, stream=stream)
-
-        if stream:
-            return cls.process_streaming_response(response)
-        else:
-            return cls.process_non_streaming_response(response)
-
-    @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}"
-
-    @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 and message != full_message:
-                        yield message[len(full_message):]
-                        full_message = message
-                except json.JSONDecodeError:
-                    pass
diff --git a/g4f/Provider/nexra/NexraChatGptV2.py b/g4f/Provider/nexra/NexraChatGptV2.py
deleted file mode 100644
index 1ff42705..00000000
--- a/g4f/Provider/nexra/NexraChatGptV2.py
+++ /dev/null
@@ -1,92 +0,0 @@
-from __future__ import annotations
-
-import json
-import requests
-
-from ...typing import CreateResult, Messages
-from ..base_provider import ProviderModelMixin, AbstractProvider
-from ..helper import format_prompt
-
-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 = True
-    supports_stream = True
-    
-    default_model = 'chatgpt'
-    models = [default_model]
-    model_aliases = {"gpt-4": "chatgpt"}
-
-    @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[model]
-        else:
-            return cls.default_model
-            
-    @classmethod
-    def create_completion(
-        cls,
-        model: str,
-        messages: Messages,
-        stream: bool,
-        proxy: str = None,
-        markdown: bool = False,
-        **kwargs
-    ) -> CreateResult:
-        model = cls.get_model(model)
-
-        headers = {
-            'Content-Type': 'application/json'
-        }
-        
-        data = {
-            "messages": [
-                {
-                    "role": "user",
-                    "content": format_prompt(messages)
-                }
-            ],
-            "stream": stream,
-            "markdown": markdown,
-            "model": model
-        }
-        
-        response = requests.post(cls.api_endpoint, headers=headers, json=data, stream=stream)
-
-        if stream:
-            return cls.process_streaming_response(response)
-        else:
-            return cls.process_non_streaming_response(response)
-
-    @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}"
-
-    @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
diff --git a/g4f/Provider/nexra/NexraChatGptWeb.py b/g4f/Provider/nexra/NexraChatGptWeb.py
deleted file mode 100644
index f82694d4..00000000
--- a/g4f/Provider/nexra/NexraChatGptWeb.py
+++ /dev/null
@@ -1,64 +0,0 @@
-from __future__ import annotations
-
-import json
-import requests
-
-from ...typing import CreateResult, Messages
-from ..base_provider import ProviderModelMixin, AbstractProvider
-from ..helper import format_prompt
-
-class NexraChatGptWeb(AbstractProvider, ProviderModelMixin):
-    label = "Nexra ChatGPT Web"
-    url = "https://nexra.aryahcr.cc/documentation/chatgpt/en"
-    working = True
-    
-    default_model = "gptweb"
-    models = [default_model]
-    model_aliases = {"gpt-4": "gptweb"}
-    api_endpoints = {"gptweb": "https://nexra.aryahcr.cc/api/chat/gptweb"}
-
-    @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[model]
-        else:
-            return cls.default_model
-            
-    @classmethod
-    def create_completion(
-        cls,
-        model: str,
-        messages: Messages,
-        proxy: str = None,
-        markdown: bool = False,
-        **kwargs
-    ) -> CreateResult:
-        model = cls.get_model(model)
-        api_endpoint = cls.api_endpoints.get(model, cls.api_endpoints[cls.default_model])
-
-        headers = {
-            'Content-Type': 'application/json'
-        }
-        
-        data = {
-            "prompt": format_prompt(messages),
-            "markdown": markdown
-        }
-        
-        response = requests.post(api_endpoint, headers=headers, json=data)
-
-        return cls.process_response(response)
-
-    @classmethod
-    def process_response(cls, response):
-        if response.status_code == 200:
-            try:
-                content = response.text.lstrip('_')
-                json_response = json.loads(content)
-                return json_response.get('gpt', '')
-            except json.JSONDecodeError:
-                return "Error: Unable to decode JSON response"
-        else:
-            return f"Error: {response.status_code}"
diff --git a/g4f/Provider/nexra/__init__.py b/g4f/Provider/nexra/__init__.py
index 6121fdc0..bebc1fb6 100644
--- a/g4f/Provider/nexra/__init__.py
+++ b/g4f/Provider/nexra/__init__.py
@@ -1,9 +1,6 @@
 from .NexraBing                import NexraBing
 from .NexraBlackbox            import NexraBlackbox
 from .NexraChatGPT             import NexraChatGPT
-from .NexraChatGPT4o           import NexraChatGPT4o
-from .NexraChatGptV2           import NexraChatGptV2
-from .NexraChatGptWeb          import NexraChatGptWeb
 from .NexraDallE               import NexraDallE
 from .NexraDallE2              import NexraDallE2
 from .NexraEmi                 import NexraEmi