feat(g4f/Provider/Nexra.py): enhance model handling and add new providers

docs/providers-and-models.md

@@ -51,7 +51,7 @@
 |[magickpen.com](https://magickpen.com)|`g4f.Provider.MagickPen`|`gpt-4o-mini`|❌|❌|✔|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
 |[meta.ai](https://www.meta.ai)|`g4f.Provider.MetaAI`|✔|✔|?|?|![Active](https://img.shields.io/badge/Active-brightgreen)|✔|
 |[app.myshell.ai/chat](https://app.myshell.ai/chat)|`g4f.Provider.MyShell`|✔|❌|?|?|![Disabled](https://img.shields.io/badge/Disabled-red)|❌|
-|[aryahcr.cc](https://nexra.aryahcr.cc)|`g4f.Provider.Nexra`|`gpt-3, gpt-3.5-turbo, gpt-4`|`dalle, dalle-2, dalle-mini, emi`|❌|✔|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
+|[aryahcr.cc](https://nexra.aryahcr.cc)|`g4f.Provider.Nexra`|`gpt-3, gpt-3.5-turbo, gpt-4, gpt-4o, gemini-pro, llama-3.1, qwen`|`dalle, dalle-2, dalle-mini, emi, sdxl-turbo, prodia`|❌|✔|![Active](https://img.shields.io/badge/Active-brightgreen)|❌|
 |[openrouter.ai](https://openrouter.ai)|`g4f.Provider.OpenRouter`|✔|❌|?|?|![Disabled](https://img.shields.io/badge/Disabled-red)|❌|
 |[platform.openai.com](https://platform.openai.com/)|`g4f.Provider.Openai`|✔|❌|✔||![Unknown](https://img.shields.io/badge/Unknown-grey)|✔|
 |[chatgpt.com](https://chatgpt.com/)|`g4f.Provider.OpenaiChat`|`gpt-4o, gpt-4o-mini, gpt-4`|❌|✔||![Unknown](https://img.shields.io/badge/Unknown-grey)|✔|

g4f/Provider/Nexra.py

@@ -1,32 +1,49 @@
 from __future__ import annotations
-import json
-from aiohttp import ClientSession
 
-from ..typing import AsyncResult, Messages
+from aiohttp import ClientSession
 from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
 from .helper import format_prompt
-from ..image import ImageResponse
+from .nexra.NexraBing import NexraBing
+from .nexra.NexraChatGPT import NexraChatGPT
+from .nexra.NexraChatGPT4o import NexraChatGPT4o
+from .nexra.NexraChatGPTWeb import NexraChatGPTWeb
+from .nexra.NexraGeminiPro import NexraGeminiPro
+from .nexra.NexraImageURL import NexraImageURL
+from .nexra.NexraLlama import NexraLlama
+from .nexra.NexraQwen import NexraQwen
 
 class Nexra(AsyncGeneratorProvider, ProviderModelMixin):
     url = "https://nexra.aryahcr.cc"
-    chat_api_endpoint = "https://nexra.aryahcr.cc/api/chat/gpt"
-    image_api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
     working = True
     supports_gpt_35_turbo = True
     supports_gpt_4 = True
+    supports_stream = True
     supports_system_message = True
     supports_message_history = True
-    
     default_model = 'gpt-3.5-turbo'
-    text_models = [
-        'gpt-4', 'gpt-4-0613', 'gpt-4-32k', 'gpt-4-0314', '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-3.5-turbo-0301',
-        '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',
-    ]
-    image_models = ['dalle', 'dalle2', 'dalle-mini', 'emi']
-    models = [*text_models, *image_models]
+    image_model = 'sdxl-turbo'
+    
+    models = (
+        *NexraBing.models,
+        *NexraChatGPT.models,
+        *NexraChatGPT4o.models,
+        *NexraChatGPTWeb.models,
+        *NexraGeminiPro.models,
+        *NexraImageURL.models,
+        *NexraLlama.models,
+        *NexraQwen.models,
+    )
+
+    model_to_provider = {
+        **{model: NexraChatGPT for model in NexraChatGPT.models},
+        **{model: NexraChatGPT4o for model in NexraChatGPT4o.models},
+        **{model: NexraChatGPTWeb for model in NexraChatGPTWeb.models},
+        **{model: NexraGeminiPro for model in NexraGeminiPro.models},
+        **{model: NexraImageURL for model in NexraImageURL.models},
+        **{model: NexraLlama for model in NexraLlama.models},
+        **{model: NexraQwen for model in NexraQwen.models},
+        **{model: NexraBing for model in NexraBing.models},
+    }
     
     model_aliases = {
         "gpt-4": "gpt-4-0613",
@@ -53,18 +70,34 @@ class Nexra(AsyncGeneratorProvider, ProviderModelMixin):
         "gpt-3": "babbage-002",
         "gpt-3": "davinci-002",
         
+        "gpt-4": "gptweb",
+        
+        "gpt-4": "Bing (Balanced)",
+        "gpt-4": "Bing (Creative)",
+        "gpt-4": "Bing (Precise)",
+        
         "dalle-2": "dalle2",
+        "sdxl": "sdxl-turbo",
     }
-    
+
+
     @classmethod
     def get_model(cls, model: str) -> str:
-        if model in cls.text_models or model in cls.image_models:
+        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_api_endpoint(cls, model: str) -> str:
+        provider_class = cls.model_to_provider.get(model)
+
+        if provider_class:
+            return provider_class.api_endpoint
+        raise ValueError(f"API endpoint for model {model} not found.")
+
     @classmethod
     async def create_async_generator(
         cls,
@@ -74,43 +107,12 @@ class Nexra(AsyncGeneratorProvider, ProviderModelMixin):
         **kwargs
     ) -> AsyncResult:
         model = cls.get_model(model)
-        
-        headers = {
-            "Content-Type": "application/json",
-        }
-        
-        async with ClientSession(headers=headers) as session:
-            if model in cls.image_models:
-                # Image generation
-                prompt = messages[-1]['content'] if messages else ""
-                data = {
-                    "prompt": prompt,
-                    "model": model,
-                    "response": "url"
-                }
-                async with session.post(cls.image_api_endpoint, json=data, proxy=proxy) as response:
-                    response.raise_for_status()
-                    result = await response.text()
-                    result_json = json.loads(result.strip('_'))
-                    image_url = result_json['images'][0] if result_json['images'] else None
-                    
-                    if image_url:
-                        yield ImageResponse(images=image_url, alt=prompt)
-            else:
-                # Text completion
-                data = {
-                    "messages": messages,
-                    "prompt": format_prompt(messages),
-                    "model": model,
-                    "markdown": False
-                }
-                async with session.post(cls.chat_api_endpoint, json=data, proxy=proxy) as response:
-                    response.raise_for_status()
-                    result = await response.text()
-                    
-                    try:
-                        json_response = json.loads(result)
-                        gpt_response = json_response.get('gpt', '')
-                        yield gpt_response
-                    except json.JSONDecodeError:
-                        yield result
+        api_endpoint = cls.get_api_endpoint(model)
+
+        provider_class = cls.model_to_provider.get(model)
+
+        if provider_class:
+            async for response in provider_class.create_async_generator(model, messages, proxy, **kwargs):
+                yield response
+        else:
+            raise ValueError(f"Provider for model {model} not found.")

g4f/Provider/nexra/NexraBing.py

@@ -0,0 +1,82 @@
+from __future__ import annotations
+from aiohttp import ClientSession
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import format_prompt
+import json
+
+class NexraBing(AsyncGeneratorProvider, ProviderModelMixin):
+    label = "Nexra Bing"
+    api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements"
+
+    bing_models = {
+        'Bing (Balanced)': 'Balanced',
+        'Bing (Creative)': 'Creative',
+        'Bing (Precise)': 'Precise'
+    }
+    
+    models = [*bing_models.keys()]
+
+    @classmethod
+    async def create_async_generator(
+        cls,
+        model: str,
+        messages: Messages,
+        proxy: str = None,
+        **kwargs
+    ) -> AsyncResult:
+        headers = {
+            "Content-Type": "application/json",
+            "Accept": "application/json",
+            "Origin": cls.url or "https://default-url.com",
+            "Referer": f"{cls.url}/chat" if cls.url else "https://default-url.com/chat",
+        }
+
+        async with ClientSession(headers=headers) as session:
+            prompt = format_prompt(messages)
+            if prompt is None:
+                raise ValueError("Prompt cannot be None")
+
+            data = {
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": prompt
+                    }
+                ],
+                "conversation_style": cls.bing_models.get(model, 'Balanced'),
+                "markdown": False,
+                "stream": True,
+                "model": "Bing"
+            }
+
+            full_response = ""
+            last_message = ""
+
+            async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
+                response.raise_for_status()
+                
+                async for line in response.content:
+                    if line:
+                        raw_data = line.decode('utf-8').strip()
+
+                        parts = raw_data.split('')
+                        for part in parts:
+                            if part:
+                                try:
+                                    json_data = json.loads(part)
+                                except json.JSONDecodeError:
+                                    continue
+
+                                if json_data.get("error"):
+                                    raise Exception("Error in API response")
+
+                                if json_data.get("finish"):
+                                    break
+
+                                if message := json_data.get("message"):
+                                    if message != last_message:
+                                        full_response = message
+                                        last_message = message
+
+            yield full_response.strip()

g4f/Provider/nexra/NexraChatGPT.py

@@ -0,0 +1,66 @@
+from __future__ import annotations
+from aiohttp import ClientSession
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import format_prompt
+import json
+
+class NexraChatGPT(AsyncGeneratorProvider, ProviderModelMixin):
+    label = "Nexra ChatGPT"
+    api_endpoint = "https://nexra.aryahcr.cc/api/chat/gpt"
+
+    models = [
+        'gpt-4', 'gpt-4-0613', 'gpt-4-32k', 'gpt-4-0314', '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-3.5-turbo-0301',
+        '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',
+    ]
+
+    @classmethod
+    async def create_async_generator(
+        cls,
+        model: str,
+        messages: Messages,
+        proxy: str = None,
+        **kwargs
+    ) -> AsyncResult:
+        headers = {
+            "Accept": "application/json",
+            "Content-Type": "application/json",
+            "Referer": f"{cls.url}/chat",
+        }
+
+        async with ClientSession(headers=headers) as session:
+            prompt = format_prompt(messages)
+            data = {
+                "prompt": prompt,
+                "model": model,
+                "markdown": False,
+                "messages": messages or [],
+            }
+
+            async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
+                response.raise_for_status()
+
+                content_type = response.headers.get('Content-Type', '')
+                if 'application/json' in content_type:
+                    result = await response.json()
+                    if result.get("status"):
+                        yield result.get("gpt", "")
+                    else:
+                        raise Exception(f"Error in response: {result.get('message', 'Unknown error')}")
+                elif 'text/plain' in content_type:
+                    text = await response.text()
+                    try:
+                        result = json.loads(text)
+                        if result.get("status"):
+                            yield result.get("gpt", "")
+                        else:
+                            raise Exception(f"Error in response: {result.get('message', 'Unknown error')}")
+                    except json.JSONDecodeError:
+                        yield text  # If not JSON, return text
+                else:
+                    raise Exception(f"Unexpected response type: {content_type}. Response text: {await response.text()}")
+

g4f/Provider/nexra/NexraChatGPT4o.py

@@ -0,0 +1,52 @@
+from __future__ import annotations
+
+import json
+from aiohttp import ClientSession
+
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import format_prompt
+
+
+class NexraChatGPT4o(AsyncGeneratorProvider, ProviderModelMixin):
+    label = "Nexra GPT-4o"
+    api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements"
+    models = ['gpt-4o']
+
+    @classmethod
+    async def create_async_generator(
+        cls,
+        model: str,
+        messages: Messages,
+        proxy: str = None,
+        **kwargs
+    ) -> AsyncResult:
+        headers = {
+            "Content-Type": "application/json"
+        }
+        async with ClientSession(headers=headers) as session:
+            data = {
+                "messages": [
+                    {'role': 'assistant', 'content': ''},
+                    {'role': 'user', 'content': format_prompt(messages)}
+                ],
+                "markdown": False,
+                "stream": True,
+                "model": model
+            }
+            async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
+                response.raise_for_status()
+                full_response = ''
+                async for line in response.content:
+                    if line:
+                        messages = line.decode('utf-8').split('\x1e')
+                        for message_str in messages:
+                            try:
+                                message = json.loads(message_str)
+                                if message.get('message'):
+                                    full_response = message['message']
+                                if message.get('finish'):
+                                    yield full_response.strip()
+                                    return
+                            except json.JSONDecodeError:
+                                pass

g4f/Provider/nexra/NexraChatGPTWeb.py

@@ -0,0 +1,53 @@
+from __future__ import annotations
+from aiohttp import ClientSession
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import format_prompt
+import json
+
+class NexraChatGPTWeb(AsyncGeneratorProvider, ProviderModelMixin):
+    label = "Nexra ChatGPT Web"
+    api_endpoint = "https://nexra.aryahcr.cc/api/chat/gptweb"
+    models = ['gptweb']
+
+    @classmethod
+    async def create_async_generator(
+        cls,
+        model: str,
+        messages: Messages,
+        proxy: str = None,
+        **kwargs
+    ) -> AsyncResult:
+        headers = {
+            "Content-Type": "application/json",
+        }
+
+        async with ClientSession(headers=headers) as session:
+            prompt = format_prompt(messages)
+            if prompt is None:
+                raise ValueError("Prompt cannot be None")
+
+            data = {
+                "prompt": prompt,
+                "markdown": False
+            }
+
+            async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
+                response.raise_for_status()
+                
+                full_response = ""
+                async for chunk in response.content:
+                    if chunk:
+                        result = chunk.decode("utf-8").strip()
+                        
+                        try:
+                            json_data = json.loads(result)
+                            
+                            if json_data.get("status"):
+                                full_response = json_data.get("gpt", "")
+                            else:
+                                full_response = f"Error: {json_data.get('message', 'Unknown error')}"
+                        except json.JSONDecodeError:
+                            full_response = "Error: Invalid JSON response."
+
+                yield full_response.strip()

g4f/Provider/nexra/NexraGeminiPro.py

@@ -0,0 +1,52 @@
+from __future__ import annotations
+
+import json
+from aiohttp import ClientSession
+
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import format_prompt
+
+
+class NexraGeminiPro(AsyncGeneratorProvider, ProviderModelMixin):
+    label = "Nexra Gemini PRO"
+    api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements"
+    models = ['gemini-pro']
+
+    @classmethod
+    async def create_async_generator(
+        cls,
+        model: str,
+        messages: Messages,
+        proxy: str = None,
+        **kwargs
+    ) -> AsyncResult:
+        headers = {
+            "Content-Type": "application/json"
+        }
+        async with ClientSession(headers=headers) as session:
+            data = {
+                "messages": [
+                    {'role': 'assistant', 'content': ''},
+                    {'role': 'user', 'content': format_prompt(messages)}
+                ],
+                "markdown": False,
+                "stream": True,
+                "model": model
+            }
+            async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
+                response.raise_for_status()
+                full_response = ''
+                async for line in response.content:
+                    if line:
+                        messages = line.decode('utf-8').split('\x1e')
+                        for message_str in messages:
+                            try:
+                                message = json.loads(message_str)
+                                if message.get('message'):
+                                    full_response = message['message']
+                                if message.get('finish'):
+                                    yield full_response.strip()
+                                    return
+                            except json.JSONDecodeError:
+                                pass

g4f/Provider/nexra/NexraImageURL.py

@@ -0,0 +1,46 @@
+from __future__ import annotations
+from aiohttp import ClientSession
+import json
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import format_prompt
+from ...image import ImageResponse
+
+class NexraImageURL(AsyncGeneratorProvider, ProviderModelMixin):
+    label = "Image Generation Provider"
+    api_endpoint = "https://nexra.aryahcr.cc/api/image/complements"
+    models = ['dalle', 'dalle2', 'dalle-mini', 'emi', 'sdxl-turbo', 'prodia']
+
+    @classmethod
+    async def create_async_generator(
+        cls,
+        model: str,
+        messages: Messages,
+        proxy: str = None,
+        **kwargs
+    ) -> AsyncResult:
+        headers = {
+            "Content-Type": "application/json",
+        }
+        
+        async with ClientSession(headers=headers) as session:
+            prompt = format_prompt(messages)
+            data = {
+                "prompt": prompt,
+                "model": model,
+                "response": "url"
+            }
+            
+            async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
+                response.raise_for_status()
+                response_text = await response.text()
+                
+                cleaned_response = response_text.lstrip('_')
+                response_json = json.loads(cleaned_response)
+                
+                images = response_json.get("images")
+                if images and len(images) > 0:
+                    image_response = ImageResponse(images[0], alt="Generated Image")
+                    yield image_response
+                else:
+                    yield "No image URL found."

g4f/Provider/nexra/NexraLlama.py

@@ -0,0 +1,52 @@
+from __future__ import annotations
+
+import json
+from aiohttp import ClientSession
+
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import format_prompt
+
+
+class NexraLlama(AsyncGeneratorProvider, ProviderModelMixin):
+    label = "Nexra LLaMA 3.1"
+    api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements"
+    models = ['llama-3.1']
+
+    @classmethod
+    async def create_async_generator(
+        cls,
+        model: str,
+        messages: Messages,
+        proxy: str = None,
+        **kwargs
+    ) -> AsyncResult:
+        headers = {
+            "Content-Type": "application/json"
+        }
+        async with ClientSession(headers=headers) as session:
+            data = {
+                "messages": [
+                    {'role': 'assistant', 'content': ''},
+                    {'role': 'user', 'content': format_prompt(messages)}
+                ],
+                "markdown": False,
+                "stream": True,
+                "model": model
+            }
+            async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
+                response.raise_for_status()
+                full_response = ''
+                async for line in response.content:
+                    if line:
+                        messages = line.decode('utf-8').split('\x1e')
+                        for message_str in messages:
+                            try:
+                                message = json.loads(message_str)
+                                if message.get('message'):
+                                    full_response = message['message']
+                                if message.get('finish'):
+                                    yield full_response.strip()
+                                    return
+                            except json.JSONDecodeError:
+                                pass

g4f/Provider/nexra/NexraQwen.py

@@ -0,0 +1,52 @@
+from __future__ import annotations
+
+import json
+from aiohttp import ClientSession
+
+from ...typing import AsyncResult, Messages
+from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin
+from ..helper import format_prompt
+
+
+class NexraQwen(AsyncGeneratorProvider, ProviderModelMixin):
+    label = "Nexra Qwen"
+    api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements"
+    models = ['qwen']
+
+    @classmethod
+    async def create_async_generator(
+        cls,
+        model: str,
+        messages: Messages,
+        proxy: str = None,
+        **kwargs
+    ) -> AsyncResult:
+        headers = {
+            "Content-Type": "application/json"
+        }
+        async with ClientSession(headers=headers) as session:
+            data = {
+                "messages": [
+                    {'role': 'assistant', 'content': ''},
+                    {'role': 'user', 'content': format_prompt(messages)}
+                ],
+                "markdown": False,
+                "stream": True,
+                "model": model
+            }
+            async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response:
+                response.raise_for_status()
+                full_response = ''
+                async for line in response.content:
+                    if line:
+                        messages = line.decode('utf-8').split('\x1e')
+                        for message_str in messages:
+                            try:
+                                message = json.loads(message_str)
+                                if message.get('message'):
+                                    full_response = message['message']
+                                if message.get('finish'):
+                                    yield full_response.strip()
+                                    return
+                            except json.JSONDecodeError:
+                                pass

g4f/Provider/nexra/__init__.py

@@ -0,0 +1 @@
+

g4f/models.py

@@ -115,7 +115,7 @@ gpt_4o = Model(
     name          = 'gpt-4o',
     base_provider = 'OpenAI',
     best_provider = IterListProvider([
-        Liaobots, Airforce, Chatgpt4o, ChatGptEs,
+        Liaobots, Nexra, Airforce, Chatgpt4o, ChatGptEs,
         OpenaiChat
     ])
 )
@@ -211,7 +211,7 @@ llama_3_1_405b = Model(
 llama_3_1 = Model(
     name          = "llama-3.1",
     base_provider = "Meta Llama",
-    best_provider = IterListProvider([llama_3_1_8b.best_provider, llama_3_1_70b.best_provider, llama_3_1_405b.best_provider,])
+    best_provider = IterListProvider([Nexra, llama_3_1_8b.best_provider, llama_3_1_70b.best_provider, llama_3_1_405b.best_provider,])
 )
 
 
@@ -273,7 +273,7 @@ phi_3_5_mini = Model(
 gemini_pro = Model(
     name          = 'gemini-pro',
     base_provider = 'Google DeepMind',
-    best_provider = IterListProvider([GeminiPro, LiteIcoding, Blackbox, AIChatFree, GPROChat, Liaobots, Airforce])
+    best_provider = IterListProvider([GeminiPro, LiteIcoding, Blackbox, AIChatFree, GPROChat, Nexra, Liaobots, Airforce])
 )
 
 gemini_flash = Model(
@@ -285,10 +285,7 @@ gemini_flash = Model(
 gemini = Model(
     name          = 'gemini',
     base_provider = 'Google DeepMind',
-    best_provider = IterListProvider([
-        Gemini, 
-        gemini_flash.best_provider, gemini_pro.best_provider
-   ])
+    best_provider = IterListProvider([Gemini, gemini_flash.best_provider, gemini_pro.best_provider])
 )
 
 # gemma
@@ -458,9 +455,7 @@ qwen_turbo = Model(
 qwen = Model(
     name = 'qwen',
     base_provider = 'Qwen',
-    best_provider = IterListProvider([
-        qwen_1_5_14b.best_provider, qwen_1_5_72b.best_provider, qwen_1_5_110b.best_provider, qwen_2_72b.best_provider, qwen_turbo.best_provider
-   ])
+    best_provider = IterListProvider([Nexra, qwen_1_5_14b.best_provider, qwen_1_5_72b.best_provider, qwen_1_5_110b.best_provider, qwen_2_72b.best_provider, qwen_turbo.best_provider])
 )
 
 
@@ -639,7 +634,7 @@ sonar_chat = Model(
 sdxl = Model(
     name = 'sdxl',
     base_provider = 'Stability AI',
-    best_provider = IterListProvider([ReplicateHome, DeepInfraImage])
+    best_provider = IterListProvider([ReplicateHome, Nexra, DeepInfraImage])
     
 )
 
@@ -734,10 +729,7 @@ dalle_3 = Model(
 dalle = Model(
     name = 'dalle',
     base_provider = '',
-    best_provider = IterListProvider([
-        Nexra,
-        dalle_2.best_provider, dalle_3.best_provider,
-   ])
+    best_provider = IterListProvider([Nexra, dalle_2.best_provider, dalle_3.best_provider])
     
 )
 
@@ -748,7 +740,7 @@ dalle_mini = Model(
     
 )
 
-### ###
+### Other ###
 emi = Model(
     name = 'emi',
     base_provider = '',
@@ -763,6 +755,13 @@ any_dark = Model(
     
 )
 
+prodia = Model(
+    name = 'prodia',
+    base_provider = '',
+    best_provider = IterListProvider([Nexra])
+    
+)
+
 class ModelUtils:
     """
     Utility class for mapping string identifiers to Model instances.
@@ -985,6 +984,7 @@ class ModelUtils:
 'dalle-mini': dalle_mini,
 'emi': emi,
 'any-dark': any_dark,
+'prodia': prodia,
     }
 
 _all_models = list(ModelUtils.convert.keys())