15 files changed, 0 insertions, 1347 deletions

diff --git a/g4f/Provider/nexra/NexraBing.py b/g4f/Provider/nexra/NexraBing.py
deleted file mode 100644
index 28f0b117..00000000
--- a/g4f/Provider/nexra/NexraBing.py
+++ /dev/null
@@ -1,93 +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 NexraBing(AbstractProvider, ProviderModelMixin):
-    label = "Nexra Bing"
-    url = "https://nexra.aryahcr.cc/documentation/bing/en"
-    api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements"
-    working = True
-    supports_stream = True
-    
-    default_model = 'Balanced'
-    models = [default_model, 'Creative', 'Precise']
-    
-    model_aliases = {
-        "gpt-4": "Balanced",
-        "gpt-4": "Creative",
-        "gpt-4": "Precise",
-    }
-
-    @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 = False,
-        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)
-                }
-            ],
-            "conversation_style": model,
-            "markdown": markdown,
-            "stream": stream,
-            "model": "Bing"
-        }
-        
-        response = requests.post(cls.api_endpoint, headers=headers, json=data, stream=True)
-
-        return cls.process_response(response)
-
-    @classmethod
-    def process_response(cls, response):
-        if response.status_code != 200:
-            yield f"Error: {response.status_code}"
-            return
-
-        full_message = ""
-        for chunk in response.iter_content(chunk_size=None):
-            if chunk:
-                messages = chunk.decode('utf-8').split('\x1e')
-                for message in messages:
-                    try:
-                        json_data = json.loads(message)
-                        if json_data.get('finish', False):
-                            return
-                        current_message = json_data.get('message', '')
-                        if current_message:
-                            new_content = current_message[len(full_message):]
-                            if new_content:
-                                yield new_content
-                                full_message = current_message
-                    except json.JSONDecodeError:
-                        continue
-
-        if not full_message:
-            yield "No message received"
diff --git a/g4f/Provider/nexra/NexraBlackbox.py b/g4f/Provider/nexra/NexraBlackbox.py
deleted file mode 100644
index be048fdd..00000000
--- a/g4f/Provider/nexra/NexraBlackbox.py
+++ /dev/null
@@ -1,100 +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 NexraBlackbox(AbstractProvider, ProviderModelMixin):
-    label = "Nexra Blackbox"
-    url = "https://nexra.aryahcr.cc/documentation/blackbox/en"
-    api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements"
-    working = True
-    supports_stream = True
-    
-    default_model = "blackbox"
-    models = [default_model]
-    model_aliases = {"blackboxai": "blackbox",}
-
-    @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,
-        websearch: bool = False,
-        **kwargs
-    ) -> CreateResult:
-        model = cls.get_model(model)
-
-        headers = {
-            'Content-Type': 'application/json'
-        }
-        
-        data = {
-            "messages": [
-                {
-                    "role": "user",
-                    "content": format_prompt(messages)
-                }
-            ],
-            "websearch": websearch,
-            "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:
-                full_response = ""
-                for line in response.iter_lines(decode_unicode=True):
-                    if line:
-                        data = json.loads(line)
-                        if data.get('finish'):
-                            break
-                        message = data.get('message', '')
-                        if message:
-                            full_response = message
-                return full_response
-            except json.JSONDecodeError:
-                return "Error: Unable to decode JSON response"
-        else:
-            return f"Error: {response.status_code}"
-
-    @classmethod
-    def process_streaming_response(cls, response):
-        previous_message = ""
-        for line in response.iter_lines(decode_unicode=True):
-            if line:
-                try:
-                    data = json.loads(line)
-                    if data.get('finish'):
-                        break
-                    message = data.get('message', '')
-                    if message and message != previous_message:
-                        yield message[len(previous_message):]
-                        previous_message = message
-                except json.JSONDecodeError:
-                    pass
diff --git a/g4f/Provider/nexra/NexraChatGPT.py b/g4f/Provider/nexra/NexraChatGPT.py
deleted file mode 100644
index 074a0363..00000000
--- a/g4f/Provider/nexra/NexraChatGPT.py
+++ /dev/null
@@ -1,285 +0,0 @@
-from __future__ import annotations
-
-import asyncio
-import json
-import requests
-from typing import Any, Dict
-
-from ...typing import AsyncResult, Messages
-from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
-from ..helper import format_prompt
-
-
-class NexraChatGPT(AsyncGeneratorProvider, ProviderModelMixin):
-    label = "Nexra ChatGPT"
-    url = "https://nexra.aryahcr.cc/documentation/chatgpt/en"
-    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'
-    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-4-32k": "gpt-4-32k-0314",
-        "gpt-3.5-turbo": "gpt-3.5-turbo-16k",
-        "gpt-3.5-turbo-0613": "gpt-3.5-turbo-16k-0613",
-        "gpt-3": "text-davinci-003",
-        "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
-    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
-    async def create_async_generator(
-        cls,
-        model: str,
-        messages: Messages,
-        stream: bool = False,
-        proxy: str = None,
-        markdown: bool = False,
-        **kwargs
-    ) -> 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"
-        }
-        
-        prompt = format_prompt(messages)
-        data = {
-            "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)
-        
-        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_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
-    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:
-                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/NexraDallE.py b/g4f/Provider/nexra/NexraDallE.py
deleted file mode 100644
index f605c6d0..00000000
--- a/g4f/Provider/nexra/NexraDallE.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from __future__ import annotations
-
-import json
-import requests
-from ...typing import CreateResult, Messages
-from ..base_provider import ProviderModelMixin, AbstractProvider
-from ...image import ImageResponse
-
-class NexraDallE(AbstractProvider, ProviderModelMixin):
-    label = "Nexra DALL-E"
-    url = "https://nexra.aryahcr.cc/documentation/dall-e/en"
-    api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
-    working = True
-    
-    default_model = "dalle"
-    models = [default_model]
-
-    @classmethod
-    def get_model(cls, model: str) -> str:
-        return cls.default_model
-            
-    @classmethod
-    def create_completion(
-        cls,
-        model: str,
-        messages: Messages,
-        proxy: str = None,
-        response: str = "url",  # base64 or url
-        **kwargs
-    ) -> CreateResult:
-        model = cls.get_model(model)
-
-        headers = {
-            'Content-Type': 'application/json'
-        }
-        
-        data = {
-            "prompt": messages[-1]["content"],
-            "model": model,
-            "response": response
-        }
-        
-        response = requests.post(cls.api_endpoint, headers=headers, json=data)
-
-        result = cls.process_response(response)
-        yield result
-
-    @classmethod
-    def process_response(cls, response):
-        if response.status_code == 200:
-            try:
-                content = response.text.strip()
-                content = content.lstrip('_')
-                data = json.loads(content)
-                if data.get('status') and data.get('images'):
-                    image_url = data['images'][0]
-                    return ImageResponse(images=[image_url], alt="Generated Image")
-                else:
-                    return "Error: No image URL found in the response"
-            except json.JSONDecodeError as e:
-                return f"Error: Unable to decode JSON response. Details: {str(e)}"
-        else:
-            return f"Error: {response.status_code}, Response: {response.text}"
diff --git a/g4f/Provider/nexra/NexraDallE2.py b/g4f/Provider/nexra/NexraDallE2.py
deleted file mode 100644
index 2a36b6e6..00000000
--- a/g4f/Provider/nexra/NexraDallE2.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from __future__ import annotations
-
-import json
-import requests
-from ...typing import CreateResult, Messages
-from ..base_provider import ProviderModelMixin, AbstractProvider
-from ...image import ImageResponse
-
-class NexraDallE2(AbstractProvider, ProviderModelMixin):
-    label = "Nexra DALL-E 2"
-    url = "https://nexra.aryahcr.cc/documentation/dall-e/en"
-    api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
-    working = True
-    
-    default_model = "dalle2"
-    models = [default_model]
-
-    @classmethod
-    def get_model(cls, model: str) -> str:
-        return cls.default_model
-            
-    @classmethod
-    def create_completion(
-        cls,
-        model: str,
-        messages: Messages,
-        proxy: str = None,
-        response: str = "url",  # base64 or url
-        **kwargs
-    ) -> CreateResult:
-        model = cls.get_model(model)
-
-        headers = {
-            'Content-Type': 'application/json'
-        }
-        
-        data = {
-            "prompt": messages[-1]["content"],
-            "model": model,
-            "response": response
-        }
-        
-        response = requests.post(cls.api_endpoint, headers=headers, json=data)
-
-        result = cls.process_response(response)
-        yield result
-
-    @classmethod
-    def process_response(cls, response):
-        if response.status_code == 200:
-            try:
-                content = response.text.strip()
-                content = content.lstrip('_')
-                data = json.loads(content)
-                if data.get('status') and data.get('images'):
-                    image_url = data['images'][0]
-                    return ImageResponse(images=[image_url], alt="Generated Image")
-                else:
-                    return "Error: No image URL found in the response"
-            except json.JSONDecodeError as e:
-                return f"Error: Unable to decode JSON response. Details: {str(e)}"
-        else:
-            return f"Error: {response.status_code}, Response: {response.text}"
diff --git a/g4f/Provider/nexra/NexraEmi.py b/g4f/Provider/nexra/NexraEmi.py
deleted file mode 100644
index c26becec..00000000
--- a/g4f/Provider/nexra/NexraEmi.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from __future__ import annotations
-
-import json
-import requests
-from ...typing import CreateResult, Messages
-from ..base_provider import ProviderModelMixin, AbstractProvider
-from ...image import ImageResponse
-
-class NexraEmi(AbstractProvider, ProviderModelMixin):
-    label = "Nexra Emi"
-    url = "https://nexra.aryahcr.cc/documentation/emi/en"
-    api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
-    working = True
-    
-    default_model = "emi"
-    models = [default_model]
-
-    @classmethod
-    def get_model(cls, model: str) -> str:
-        return cls.default_model
-            
-    @classmethod
-    def create_completion(
-        cls,
-        model: str,
-        messages: Messages,
-        proxy: str = None,
-        response: str = "url",  # base64 or url
-        **kwargs
-    ) -> CreateResult:
-        model = cls.get_model(model)
-
-        headers = {
-            'Content-Type': 'application/json'
-        }
-        
-        data = {
-            "prompt": messages[-1]["content"],
-            "model": model,
-            "response": response
-        }
-        
-        response = requests.post(cls.api_endpoint, headers=headers, json=data)
-
-        result = cls.process_response(response)
-        yield result
-
-    @classmethod
-    def process_response(cls, response):
-        if response.status_code == 200:
-            try:
-                content = response.text.strip()
-                content = content.lstrip('_')
-                data = json.loads(content)
-                if data.get('status') and data.get('images'):
-                    image_url = data['images'][0]
-                    return ImageResponse(images=[image_url], alt="Generated Image")
-                else:
-                    return "Error: No image URL found in the response"
-            except json.JSONDecodeError as e:
-                return f"Error: Unable to decode JSON response. Details: {str(e)}"
-        else:
-            return f"Error: {response.status_code}, Response: {response.text}"
diff --git a/g4f/Provider/nexra/NexraFluxPro.py b/g4f/Provider/nexra/NexraFluxPro.py
deleted file mode 100644
index cfb26385..00000000
--- a/g4f/Provider/nexra/NexraFluxPro.py
+++ /dev/null
@@ -1,70 +0,0 @@
-from __future__ import annotations
-
-import json
-import requests
-from ...typing import CreateResult, Messages
-from ..base_provider import ProviderModelMixin, AbstractProvider
-from ...image import ImageResponse
-
-class NexraFluxPro(AbstractProvider, ProviderModelMixin):
-    url = "https://nexra.aryahcr.cc/documentation/flux-pro/en"
-    api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
-    working = True
-    
-    default_model = 'flux'
-    models = [default_model]
-    model_aliases = {
-        "flux-pro": "flux",
-    }
-
-    @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,
-        response: str = "url",  # base64 or url
-        **kwargs
-    ) -> CreateResult:
-        model = cls.get_model(model)
-
-        headers = {
-            'Content-Type': 'application/json'
-        }
-        
-        data = {
-            "prompt": messages[-1]["content"],
-            "model": model,
-            "response": response
-        }
-        
-        response = requests.post(cls.api_endpoint, headers=headers, json=data)
-
-        result = cls.process_response(response)
-        yield result
-
-    @classmethod
-    def process_response(cls, response):
-        if response.status_code == 200:
-            try:
-                content = response.text.strip()
-                content = content.lstrip('_')
-                data = json.loads(content)
-                if data.get('status') and data.get('images'):
-                    image_url = data['images'][0]
-                    return ImageResponse(images=[image_url], alt="Generated Image")
-                else:
-                    return "Error: No image URL found in the response"
-            except json.JSONDecodeError as e:
-                return f"Error: Unable to decode JSON response. Details: {str(e)}"
-        else:
-            return f"Error: {response.status_code}, Response: {response.text}"
diff --git a/g4f/Provider/nexra/NexraGeminiPro.py b/g4f/Provider/nexra/NexraGeminiPro.py
deleted file mode 100644
index e4e6a8ec..00000000
--- a/g4f/Provider/nexra/NexraGeminiPro.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 NexraGeminiPro(AbstractProvider, ProviderModelMixin):
-    label = "Nexra Gemini PRO"
-    url = "https://nexra.aryahcr.cc/documentation/gemini-pro/en"
-    api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements"
-    working = True
-    supports_stream = True
-    
-    default_model = 'gemini-pro'
-    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:
-                        yield message[len(full_message):]
-                        full_message = message
-                except json.JSONDecodeError:
-                    pass
diff --git a/g4f/Provider/nexra/NexraMidjourney.py b/g4f/Provider/nexra/NexraMidjourney.py
deleted file mode 100644
index c427f8a0..00000000
--- a/g4f/Provider/nexra/NexraMidjourney.py
+++ /dev/null
@@ -1,63 +0,0 @@
-from __future__ import annotations
-
-import json
-import requests
-from ...typing import CreateResult, Messages
-from ..base_provider import ProviderModelMixin, AbstractProvider
-from ...image import ImageResponse
-
-class NexraMidjourney(AbstractProvider, ProviderModelMixin):
-    label = "Nexra Midjourney"
-    url = "https://nexra.aryahcr.cc/documentation/midjourney/en"
-    api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
-    working = True
-    
-    default_model = "midjourney"
-    models = [default_model]
-
-    @classmethod
-    def get_model(cls, model: str) -> str:
-        return cls.default_model
-            
-    @classmethod
-    def create_completion(
-        cls,
-        model: str,
-        messages: Messages,
-        proxy: str = None,
-        response: str = "url",  # base64 or url
-        **kwargs
-    ) -> CreateResult:
-        model = cls.get_model(model)
-
-        headers = {
-            'Content-Type': 'application/json'
-        }
-        
-        data = {
-            "prompt": messages[-1]["content"],
-            "model": model,
-            "response": response
-        }
-        
-        response = requests.post(cls.api_endpoint, headers=headers, json=data)
-
-        result = cls.process_response(response)
-        yield result
-
-    @classmethod
-    def process_response(cls, response):
-        if response.status_code == 200:
-            try:
-                content = response.text.strip()
-                content = content.lstrip('_')
-                data = json.loads(content)
-                if data.get('status') and data.get('images'):
-                    image_url = data['images'][0]
-                    return ImageResponse(images=[image_url], alt="Generated Image")
-                else:
-                    return "Error: No image URL found in the response"
-            except json.JSONDecodeError as e:
-                return f"Error: Unable to decode JSON response. Details: {str(e)}"
-        else:
-            return f"Error: {response.status_code}, Response: {response.text}"
diff --git a/g4f/Provider/nexra/NexraProdiaAI.py b/g4f/Provider/nexra/NexraProdiaAI.py
deleted file mode 100644
index de997fce..00000000
--- a/g4f/Provider/nexra/NexraProdiaAI.py
+++ /dev/null
@@ -1,151 +0,0 @@
-from __future__ import annotations
-
-import json
-import requests
-from ...typing import CreateResult, Messages
-from ..base_provider import ProviderModelMixin, AbstractProvider
-from ...image import ImageResponse
-
-class NexraProdiaAI(AbstractProvider, ProviderModelMixin):
-    label = "Nexra Prodia AI"
-    url = "https://nexra.aryahcr.cc/documentation/prodia/en"
-    api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
-    working = True
-    
-    default_model = 'absolutereality_v181.safetensors [3d9d4d2b]'
-    models = [
-        '3Guofeng3_v34.safetensors [50f420de]',
-        'absolutereality_V16.safetensors [37db0fc3]',
-        default_model,
-        'amIReal_V41.safetensors [0a8a2e61]',
-        'analog-diffusion-1.0.ckpt [9ca13f02]',
-        'aniverse_v30.safetensors [579e6f85]',
-        'anythingv3_0-pruned.ckpt [2700c435]',
-        'anything-v4.5-pruned.ckpt [65745d25]',
-        'anythingV5_PrtRE.safetensors [893e49b9]',
-        'AOM3A3_orangemixs.safetensors [9600da17]',
-        'blazing_drive_v10g.safetensors [ca1c1eab]',
-        'breakdomain_I2428.safetensors [43cc7d2f]',
-        'breakdomain_M2150.safetensors [15f7afca]',
-        'cetusMix_Version35.safetensors [de2f2560]',
-        'childrensStories_v13D.safetensors [9dfaabcb]',
-        'childrensStories_v1SemiReal.safetensors [a1c56dbb]',
-        'childrensStories_v1ToonAnime.safetensors [2ec7b88b]',
-        'Counterfeit_v30.safetensors [9e2a8f19]',
-        'cuteyukimixAdorable_midchapter3.safetensors [04bdffe6]',
-        'cyberrealistic_v33.safetensors [82b0d085]',
-        'dalcefo_v4.safetensors [425952fe]',
-        'deliberate_v2.safetensors [10ec4b29]',
-        'deliberate_v3.safetensors [afd9d2d4]',
-        'dreamlike-anime-1.0.safetensors [4520e090]',
-        'dreamlike-diffusion-1.0.safetensors [5c9fd6e0]',
-        'dreamlike-photoreal-2.0.safetensors [fdcf65e7]',
-        'dreamshaper_6BakedVae.safetensors [114c8abb]',
-        'dreamshaper_7.safetensors [5cf5ae06]',
-        'dreamshaper_8.safetensors [9d40847d]',
-        'edgeOfRealism_eorV20.safetensors [3ed5de15]',
-        'EimisAnimeDiffusion_V1.ckpt [4f828a15]',
-        'elldreths-vivid-mix.safetensors [342d9d26]',
-        'epicphotogasm_xPlusPlus.safetensors [1a8f6d35]',
-        'epicrealism_naturalSinRC1VAE.safetensors [90a4c676]',
-        'epicrealism_pureEvolutionV3.safetensors [42c8440c]',
-        'ICantBelieveItsNotPhotography_seco.safetensors [4e7a3dfd]',
-        'indigoFurryMix_v75Hybrid.safetensors [91208cbb]',
-        'juggernaut_aftermath.safetensors [5e20c455]',
-        'lofi_v4.safetensors [ccc204d6]',
-        'lyriel_v16.safetensors [68fceea2]',
-        'majicmixRealistic_v4.safetensors [29d0de58]',
-        'mechamix_v10.safetensors [ee685731]',
-        'meinamix_meinaV9.safetensors [2ec66ab0]',
-        'meinamix_meinaV11.safetensors [b56ce717]',
-        'neverendingDream_v122.safetensors [f964ceeb]',
-        'openjourney_V4.ckpt [ca2f377f]',
-        'pastelMixStylizedAnime_pruned_fp16.safetensors [793a26e8]',
-        'portraitplus_V1.0.safetensors [1400e684]',
-        'protogenx34.safetensors [5896f8d5]',
-        'Realistic_Vision_V1.4-pruned-fp16.safetensors [8d21810b]',
-        'Realistic_Vision_V2.0.safetensors [79587710]',
-        'Realistic_Vision_V4.0.safetensors [29a7afaa]',
-        'Realistic_Vision_V5.0.safetensors [614d1063]',
-        'Realistic_Vision_V5.1.safetensors [a0f13c83]',
-        'redshift_diffusion-V10.safetensors [1400e684]',
-        'revAnimated_v122.safetensors [3f4fefd9]',
-        'rundiffusionFX25D_v10.safetensors [cd12b0ee]',
-        'rundiffusionFX_v10.safetensors [cd4e694d]',
-        'sdv1_4.ckpt [7460a6fa]',
-        'v1-5-pruned-emaonly.safetensors [d7049739]',
-        'v1-5-inpainting.safetensors [21c7ab71]',
-        'shoninsBeautiful_v10.safetensors [25d8c546]',
-        'theallys-mix-ii-churned.safetensors [5d9225a4]',
-        'timeless-1.0.ckpt [7c4971d4]',
-        'toonyou_beta6.safetensors [980f6b15]',
-    ]
-    
-    model_aliases = {}
-
-    @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 get_model(cls, model: str) -> str:
-        return cls.default_model
-            
-    @classmethod
-    def create_completion(
-        cls,
-        model: str,
-        messages: Messages,
-        proxy: str = None,
-        response: str = "url", # base64 or url
-        steps: str = 25, # Min: 1, Max: 30
-        cfg_scale: str = 7, # Min: 0, Max: 20
-        sampler: str = "DPM++ 2M Karras", # Select from these: "Euler","Euler a","Heun","DPM++ 2M Karras","DPM++ SDE Karras","DDIM"
-        negative_prompt: str = "", # Indicates what the AI should not do
-        **kwargs
-    ) -> CreateResult:
-        model = cls.get_model(model)
-
-        headers = {
-            'Content-Type': 'application/json'
-        }
-        
-        data = {
-            "prompt": messages[-1]["content"],
-            "model": "prodia",
-            "response": response,
-            "data": {
-                "model": model,
-                "steps": steps,
-                "cfg_scale": cfg_scale,
-                "sampler": sampler,
-                "negative_prompt": negative_prompt
-            }
-        }
-        
-        response = requests.post(cls.api_endpoint, headers=headers, json=data)
-
-        result = cls.process_response(response)
-        yield result
-
-    @classmethod
-    def process_response(cls, response):
-        if response.status_code == 200:
-            try:
-                content = response.text.strip()
-                content = content.lstrip('_')  # Remove leading underscores
-                data = json.loads(content)
-                if data.get('status') and data.get('images'):
-                    image_url = data['images'][0]
-                    return ImageResponse(images=[image_url], alt="Generated Image")
-                else:
-                    return "Error: No image URL found in the response"
-            except json.JSONDecodeError as e:
-                return f"Error: Unable to decode JSON response. Details: {str(e)}"
-        else:
-            return f"Error: {response.status_code}, Response: {response.text}"
diff --git a/g4f/Provider/nexra/NexraQwen.py b/g4f/Provider/nexra/NexraQwen.py
deleted file mode 100644
index 7f944e44..00000000
--- a/g4f/Provider/nexra/NexraQwen.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 NexraQwen(AbstractProvider, ProviderModelMixin):
-    label = "Nexra Qwen"
-    url = "https://nexra.aryahcr.cc/documentation/qwen/en"
-    api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements"
-    working = True
-    supports_stream = True
-    
-    default_model = 'qwen'
-    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 is not None and message != full_message:
-                        yield message[len(full_message):]
-                        full_message = message
-                except json.JSONDecodeError:
-                    pass
diff --git a/g4f/Provider/nexra/NexraSD15.py b/g4f/Provider/nexra/NexraSD15.py
deleted file mode 100644
index 860a132f..00000000
--- a/g4f/Provider/nexra/NexraSD15.py
+++ /dev/null
@@ -1,72 +0,0 @@
-from __future__ import annotations
-
-import json
-import requests
-from ...typing import CreateResult, Messages
-from ..base_provider import ProviderModelMixin, AbstractProvider
-from ...image import ImageResponse
-
-class NexraSD15(AbstractProvider, ProviderModelMixin):
-    label = "Nexra Stable Diffusion 1.5"
-    url = "https://nexra.aryahcr.cc/documentation/stable-diffusion/en"
-    api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
-    working = True
-    
-    default_model = 'stablediffusion-1.5'
-    models = [default_model]
-    
-    model_aliases = {
-        "sd-1.5": "stablediffusion-1.5",
-    }
-
-    @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,
-        response: str = "url",  # base64 or url
-        **kwargs
-    ) -> CreateResult:
-        model = cls.get_model(model)
-
-        headers = {
-            'Content-Type': 'application/json'
-        }
-        
-        data = {
-            "prompt": messages[-1]["content"],
-            "model": model,
-            "response": response
-        }
-        
-        response = requests.post(cls.api_endpoint, headers=headers, json=data)
-
-        result = cls.process_response(response)
-        yield result
-
-    @classmethod
-    def process_response(cls, response):
-        if response.status_code == 200:
-            try:
-                content = response.text.strip()
-                content = content.lstrip('_')
-                data = json.loads(content)
-                if data.get('status') and data.get('images'):
-                    image_url = data['images'][0]
-                    return ImageResponse(images=[image_url], alt="Generated Image")
-                else:
-                    return "Error: No image URL found in the response"
-            except json.JSONDecodeError as e:
-                return f"Error: Unable to decode JSON response. Details: {str(e)}"
-        else:
-            return f"Error: {response.status_code}, Response: {response.text}"
diff --git a/g4f/Provider/nexra/NexraSDLora.py b/g4f/Provider/nexra/NexraSDLora.py
deleted file mode 100644
index a12bff1a..00000000
--- a/g4f/Provider/nexra/NexraSDLora.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from __future__ import annotations
-
-import json
-import requests
-from ...typing import CreateResult, Messages
-from ..base_provider import ProviderModelMixin, AbstractProvider
-from ...image import ImageResponse
-
-class NexraSDLora(AbstractProvider, ProviderModelMixin):
-    label = "Nexra Stable Diffusion Lora"
-    url = "https://nexra.aryahcr.cc/documentation/stable-diffusion/en"
-    api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
-    working = True
-    
-    default_model = "sdxl-lora"
-    models = [default_model]
-
-    @classmethod
-    def get_model(cls, model: str) -> str:
-        return cls.default_model
-            
-    @classmethod
-    def create_completion(
-        cls,
-        model: str,
-        messages: Messages,
-        proxy: str = None,
-        response: str = "url", # base64 or url
-        guidance: str = 0.3, # Min: 0, Max: 5
-        steps: str = 2, # Min: 2, Max: 10
-        **kwargs
-    ) -> CreateResult:
-        model = cls.get_model(model)
-
-        headers = {
-            'Content-Type': 'application/json'
-        }
-        
-        data = {
-            "prompt": messages[-1]["content"],
-            "model": model,
-            "response": response,
-            "data": {
-                "guidance": guidance,
-                "steps": steps
-            }
-        }
-        
-        response = requests.post(cls.api_endpoint, headers=headers, json=data)
-
-        result = cls.process_response(response)
-        yield result
-
-    @classmethod
-    def process_response(cls, response):
-        if response.status_code == 200:
-            try:
-                content = response.text.strip()
-                content = content.lstrip('_')
-                data = json.loads(content)
-                if data.get('status') and data.get('images'):
-                    image_url = data['images'][0]
-                    return ImageResponse(images=[image_url], alt="Generated Image")
-                else:
-                    return "Error: No image URL found in the response"
-            except json.JSONDecodeError as e:
-                return f"Error: Unable to decode JSON response. Details: {str(e)}"
-        else:
-            return f"Error: {response.status_code}, Response: {response.text}"
diff --git a/g4f/Provider/nexra/NexraSDTurbo.py b/g4f/Provider/nexra/NexraSDTurbo.py
deleted file mode 100644
index 865b4522..00000000
--- a/g4f/Provider/nexra/NexraSDTurbo.py
+++ /dev/null
@@ -1,69 +0,0 @@
-from __future__ import annotations
-
-import json
-import requests
-from ...typing import CreateResult, Messages
-from ..base_provider import ProviderModelMixin, AbstractProvider
-from ...image import ImageResponse
-
-class NexraSDTurbo(AbstractProvider, ProviderModelMixin):
-    label = "Nexra Stable Diffusion Turbo"
-    url = "https://nexra.aryahcr.cc/documentation/stable-diffusion/en"
-    api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
-    working = True
-    
-    default_model = "sdxl-turbo"
-    models = [default_model]
-
-    @classmethod
-    def get_model(cls, model: str) -> str:
-        return cls.default_model
-            
-    @classmethod
-    def create_completion(
-        cls,
-        model: str,
-        messages: Messages,
-        proxy: str = None,
-        response: str = "url", # base64 or url
-        strength: str = 0.7, # Min: 0, Max: 1
-        steps: str = 2, # Min: 1, Max: 10
-        **kwargs
-    ) -> CreateResult:
-        model = cls.get_model(model)
-
-        headers = {
-            'Content-Type': 'application/json'
-        }
-        
-        data = {
-            "prompt": messages[-1]["content"],
-            "model": model,
-            "response": response,
-            "data": {
-                "strength": strength,
-                "steps": steps 
-            }
-        }
-        
-        response = requests.post(cls.api_endpoint, headers=headers, json=data)
-
-        result = cls.process_response(response)
-        yield result
-
-    @classmethod
-    def process_response(cls, response):
-        if response.status_code == 200:
-            try:
-                content = response.text.strip()
-                content = content.lstrip('_')  # Remove the leading underscore
-                data = json.loads(content)
-                if data.get('status') and data.get('images'):
-                    image_url = data['images'][0]
-                    return ImageResponse(images=[image_url], alt="Generated Image")
-                else:
-                    return "Error: No image URL found in the response"
-            except json.JSONDecodeError as e:
-                return f"Error: Unable to decode JSON response. Details: {str(e)}"
-        else:
-            return f"Error: {response.status_code}, Response: {response.text}"
diff --git a/g4f/Provider/nexra/__init__.py b/g4f/Provider/nexra/__init__.py
deleted file mode 100644
index bebc1fb6..00000000
--- a/g4f/Provider/nexra/__init__.py
+++ /dev/null
@@ -1,14 +0,0 @@
-from .NexraBing                import NexraBing
-from .NexraBlackbox            import NexraBlackbox
-from .NexraChatGPT             import NexraChatGPT
-from .NexraDallE               import NexraDallE
-from .NexraDallE2              import NexraDallE2
-from .NexraEmi                 import NexraEmi
-from .NexraFluxPro             import NexraFluxPro
-from .NexraGeminiPro           import NexraGeminiPro
-from .NexraMidjourney          import NexraMidjourney
-from .NexraProdiaAI            import NexraProdiaAI
-from .NexraQwen                import NexraQwen
-from .NexraSD15                import NexraSD15
-from .NexraSDLora              import NexraSDLora
-from .NexraSDTurbo             import NexraSDTurbo