1 files changed, 43 insertions, 95 deletions

diff --git a/g4f/Provider/Nexra.py b/g4f/Provider/Nexra.py
index 33e794f6..5fcdd242 100644
--- a/g4f/Provider/Nexra.py
+++ b/g4f/Provider/Nexra.py
@@ -1,102 +1,25 @@
 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 .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
+from ..image import ImageResponse
+
 
 class Nexra(AsyncGeneratorProvider, ProviderModelMixin):
-    url = "https://nexra.aryahcr.cc"
+    label = "Nexra Animagine XL"
+    url = "https://nexra.aryahcr.cc/documentation/midjourney/en"
+    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'
-    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",
-        "gpt-4": "gpt-4-32k",
-        "gpt-4": "gpt-4-0314",
-        "gpt-4": "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": "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",
-        
-        "gpt-4": "gptweb",
-        
-        "gpt-4": "Bing (Balanced)",
-        "gpt-4": "Bing (Creative)",
-        "gpt-4": "Bing (Precise)",
-        
-        "dalle-2": "dalle2",
-        "sdxl": "sdxl-turbo",
-    }
 
+    default_model = 'animagine-xl'
+    models = [default_model]
 
     @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_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.")
+        return cls.default_model
 
     @classmethod
     async def create_async_generator(
@@ -104,15 +27,40 @@ class Nexra(AsyncGeneratorProvider, ProviderModelMixin):
         model: str,
         messages: Messages,
         proxy: str = None,
+        response: str = "url",  # base64 or url
         **kwargs
     ) -> AsyncResult:
+        # Retrieve the correct model to use
         model = cls.get_model(model)
-        api_endpoint = cls.get_api_endpoint(model)
 
-        provider_class = cls.model_to_provider.get(model)
+        # Format the prompt from the messages
+        prompt = messages[0]['content']
+
+        headers = {
+            "Content-Type": "application/json"
+        }
+        payload = {
+            "prompt": prompt,
+            "model": model,
+            "response": response
+        }
+
+        async with ClientSession(headers=headers) as session:
+            async with session.post(cls.api_endpoint, json=payload, proxy=proxy) as response:
+                response.raise_for_status()
+                text_data = await response.text()
 
-        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.")
+                try:
+                    # Parse the JSON response
+                    json_start = text_data.find('{')
+                    json_data = text_data[json_start:]
+                    data = json.loads(json_data)
+                    
+                    # Check if the response contains images
+                    if 'images' in data and len(data['images']) > 0:
+                        image_url = data['images'][0]
+                        yield ImageResponse(image_url, prompt)
+                    else:
+                        yield ImageResponse("No images found in the response.", prompt)
+                except json.JSONDecodeError:
+                    yield ImageResponse("Failed to parse JSON. Response might not be in JSON format.", prompt)