diff options
Diffstat (limited to 'g4f/Provider/nexra')
24 files changed, 1474 insertions, 303 deletions
diff --git a/g4f/Provider/nexra/NexraBing.py b/g4f/Provider/nexra/NexraBing.py index 59e06a3d..716e9254 100644 --- a/g4f/Provider/nexra/NexraBing.py +++ b/g4f/Provider/nexra/NexraBing.py @@ -1,21 +1,42 @@ from __future__ import annotations + from aiohttp import ClientSession +from aiohttp.client_exceptions import ContentTypeError + 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" + url = "https://nexra.aryahcr.cc/documentation/bing/en" api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements" - - bing_models = { - 'Bing (Balanced)': 'Balanced', - 'Bing (Creative)': 'Creative', - 'Bing (Precise)': 'Precise' - } + working = False + supports_gpt_4 = False + supports_stream = False - models = [*bing_models.keys()] + default_model = 'Bing (Balanced)' + models = ['Bing (Balanced)', 'Bing (Creative)', 'Bing (Precise)'] + + model_aliases = { + "gpt-4": "Bing (Balanced)", + "gpt-4": "Bing (Creative)", + "gpt-4": "Bing (Precise)", + } + + @classmethod + def get_model_and_style(cls, model: str) -> tuple[str, str]: + # Default to the default model if not found + model = cls.model_aliases.get(model, model) + if model not in cls.models: + model = cls.default_model + + # Extract the base model and conversation style + base_model, conversation_style = model.split(' (') + conversation_style = conversation_style.rstrip(')') + return base_model, conversation_style @classmethod async def create_async_generator( @@ -23,20 +44,19 @@ class NexraBing(AsyncGeneratorProvider, ProviderModelMixin): model: str, messages: Messages, proxy: str = None, + stream: bool = False, + markdown: bool = False, **kwargs ) -> AsyncResult: + base_model, conversation_style = cls.get_model_and_style(model) + 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", + "origin": cls.url, + "referer": f"{cls.url}/chat", } - async with ClientSession(headers=headers) as session: prompt = format_prompt(messages) - if prompt is None: - raise ValueError("Prompt cannot be None") - data = { "messages": [ { @@ -44,39 +64,33 @@ class NexraBing(AsyncGeneratorProvider, ProviderModelMixin): "content": prompt } ], - "conversation_style": cls.bing_models.get(model, 'Balanced'), - "markdown": False, - "stream": True, - "model": "Bing" + "conversation_style": conversation_style, + "markdown": markdown, + "stream": stream, + "model": base_model } - - 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 + try: + # Read the entire response text + text_response = await response.text() + # Split the response on the separator character + segments = text_response.split('\x1e') + + complete_message = "" + for segment in segments: + if not segment.strip(): + continue + try: + response_data = json.loads(segment) + if response_data.get('message'): + complete_message = response_data['message'] + if response_data.get('finish'): + break + except json.JSONDecodeError: + raise Exception(f"Failed to parse segment: {segment}") - yield full_response.strip() + # Yield the complete message + yield complete_message + except ContentTypeError: + raise Exception("Failed to parse response content type.") diff --git a/g4f/Provider/nexra/NexraBlackbox.py b/g4f/Provider/nexra/NexraBlackbox.py new file mode 100644 index 00000000..a8b4fca1 --- /dev/null +++ b/g4f/Provider/nexra/NexraBlackbox.py @@ -0,0 +1,101 @@ +from __future__ import annotations + +import json +from aiohttp import ClientSession, ClientTimeout, ClientError + +from ...typing import AsyncResult, Messages +from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin + +class NexraBlackbox(AsyncGeneratorProvider, 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 + async def create_async_generator( + cls, + model: str, + messages: Messages, + proxy: str = None, + stream: bool = False, + markdown: bool = False, + websearch: bool = False, + **kwargs + ) -> AsyncResult: + model = cls.get_model(model) + + headers = { + "Content-Type": "application/json" + } + + payload = { + "messages": [{"role": msg["role"], "content": msg["content"]} for msg in messages], + "websearch": websearch, + "stream": stream, + "markdown": markdown, + "model": model + } + + timeout = ClientTimeout(total=600) # 10 minutes timeout + + try: + async with ClientSession(headers=headers, timeout=timeout) as session: + async with session.post(cls.api_endpoint, json=payload, proxy=proxy) as response: + if response.status != 200: + error_text = await response.text() + raise Exception(f"Error: {response.status} - {error_text}") + + content = await response.text() + + # Split content by Record Separator character + parts = content.split('\x1e') + full_message = "" + links = [] + + for part in parts: + if part: + try: + json_response = json.loads(part) + + if json_response.get("message"): + full_message = json_response["message"] # Overwrite instead of append + + if isinstance(json_response.get("search"), list): + links = json_response["search"] # Overwrite instead of extend + + if json_response.get("finish", False): + break + + except json.JSONDecodeError: + pass + + if full_message: + yield full_message.strip() + + if payload["websearch"] and links: + yield "\n\n**Source:**" + for i, link in enumerate(links, start=1): + yield f"\n{i}. {link['title']}: {link['link']}" + + except ClientError: + raise + except Exception: + raise diff --git a/g4f/Provider/nexra/NexraChatGPT.py b/g4f/Provider/nexra/NexraChatGPT.py index 8ed83f98..f9f49139 100644 --- a/g4f/Provider/nexra/NexraChatGPT.py +++ b/g4f/Provider/nexra/NexraChatGPT.py @@ -1,22 +1,60 @@ 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 -import json + class NexraChatGPT(AsyncGeneratorProvider, ProviderModelMixin): label = "Nexra ChatGPT" + url = "https://nexra.aryahcr.cc/documentation/chatgpt/en" api_endpoint = "https://nexra.aryahcr.cc/api/chat/gpt" + working = True + supports_gpt_35_turbo = True + supports_gpt_4 = True + supports_stream = False + + default_model = 'gpt-3.5-turbo' + models = ['gpt-4', 'gpt-4-0613', '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', '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'] + + 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", + } - 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 + 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( @@ -26,41 +64,26 @@ class NexraChatGPT(AsyncGeneratorProvider, ProviderModelMixin): proxy: str = None, **kwargs ) -> AsyncResult: + model = cls.get_model(model) + headers = { - "Accept": "application/json", - "Content-Type": "application/json", - "Referer": f"{cls.url}/chat", + "Content-Type": "application/json" } - async with ClientSession(headers=headers) as session: prompt = format_prompt(messages) data = { + "messages": messages, "prompt": prompt, "model": model, - "markdown": False, - "messages": messages or [], + "markdown": False } - 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()}") - + response_text = await response.text() + try: + if response_text.startswith('_'): + response_text = response_text[1:] + response_data = json.loads(response_text) + yield response_data.get('gpt', '') + except json.JSONDecodeError: + yield '' diff --git a/g4f/Provider/nexra/NexraChatGPT4o.py b/g4f/Provider/nexra/NexraChatGPT4o.py index eb18d439..62144163 100644 --- a/g4f/Provider/nexra/NexraChatGPT4o.py +++ b/g4f/Provider/nexra/NexraChatGPT4o.py @@ -1,17 +1,26 @@ 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 - +import json class NexraChatGPT4o(AsyncGeneratorProvider, ProviderModelMixin): - label = "Nexra GPT-4o" + label = "Nexra ChatGPT4o" + url = "https://nexra.aryahcr.cc/documentation/chatgpt/en" api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements" - models = ['gpt-4o'] + working = True + supports_gpt_4 = True + supports_stream = False + + default_model = 'gpt-4o' + models = [default_model] + + @classmethod + def get_model(cls, model: str) -> str: + return cls.default_model @classmethod async def create_async_generator( @@ -21,32 +30,45 @@ class NexraChatGPT4o(AsyncGeneratorProvider, ProviderModelMixin): proxy: str = None, **kwargs ) -> AsyncResult: + model = cls.get_model(model) + headers = { - "Content-Type": "application/json" + "Content-Type": "application/json", } async with ClientSession(headers=headers) as session: data = { "messages": [ - {'role': 'assistant', 'content': ''}, - {'role': 'user', 'content': format_prompt(messages)} + { + "role": "user", + "content": format_prompt(messages) + } ], + "stream": False, "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 + buffer = "" + last_message = "" + async for chunk in response.content.iter_any(): + chunk_str = chunk.decode() + buffer += chunk_str + while '{' in buffer and '}' in buffer: + start = buffer.index('{') + end = buffer.index('}', start) + 1 + json_str = buffer[start:end] + buffer = buffer[end:] + try: + json_obj = json.loads(json_str) + if json_obj.get("finish"): + if last_message: + yield last_message + return + elif json_obj.get("message"): + last_message = json_obj["message"] + except json.JSONDecodeError: + pass + + if last_message: + yield last_message diff --git a/g4f/Provider/nexra/NexraChatGPTWeb.py b/g4f/Provider/nexra/NexraChatGPTWeb.py deleted file mode 100644 index e7738665..00000000 --- a/g4f/Provider/nexra/NexraChatGPTWeb.py +++ /dev/null @@ -1,53 +0,0 @@ -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() diff --git a/g4f/Provider/nexra/NexraChatGptV2.py b/g4f/Provider/nexra/NexraChatGptV2.py new file mode 100644 index 00000000..c0faf93a --- /dev/null +++ b/g4f/Provider/nexra/NexraChatGptV2.py @@ -0,0 +1,93 @@ +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 + + +class NexraChatGptV2(AsyncGeneratorProvider, 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_gpt_4 = 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 + async def create_async_generator( + cls, + model: str, + messages: Messages, + proxy: str = None, + stream: bool = False, + markdown: bool = False, + **kwargs + ) -> AsyncResult: + model = cls.get_model(model) + + headers = { + "Content-Type": "application/json" + } + + async with ClientSession(headers=headers) as session: + prompt = format_prompt(messages) + data = { + "messages": [ + { + "role": "user", + "content": prompt + } + ], + "stream": stream, + "markdown": markdown, + "model": model + } + + async with session.post(f"{cls.api_endpoint}", json=data, proxy=proxy) as response: + response.raise_for_status() + + if stream: + # Streamed response handling (stream=True) + collected_message = "" + async for chunk in response.content.iter_any(): + if chunk: + decoded_chunk = chunk.decode().strip().split("\x1e") + for part in decoded_chunk: + if part: + message_data = json.loads(part) + + # Collect messages until 'finish': true + if 'message' in message_data and message_data['message']: + collected_message = message_data['message'] + + # When finish is true, yield the final collected message + if message_data.get('finish', False): + yield collected_message + return + else: + # Non-streamed response handling (stream=False) + response_data = await response.json(content_type=None) + + # Yield the message directly from the response + if 'message' in response_data and response_data['message']: + yield response_data['message'] + return diff --git a/g4f/Provider/nexra/NexraChatGptWeb.py b/g4f/Provider/nexra/NexraChatGptWeb.py new file mode 100644 index 00000000..d14a2162 --- /dev/null +++ b/g4f/Provider/nexra/NexraChatGptWeb.py @@ -0,0 +1,69 @@ +from __future__ import annotations + +from aiohttp import ClientSession, ContentTypeError +import json + +from ...typing import AsyncResult, Messages +from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin +from ..helper import format_prompt + + +class NexraChatGptWeb(AsyncGeneratorProvider, ProviderModelMixin): + label = "Nexra ChatGPT Web" + url = "https://nexra.aryahcr.cc/documentation/chatgpt/en" + api_endpoint = "https://nexra.aryahcr.cc/api/chat/{}" + working = True + supports_gpt_35_turbo = True + supports_gpt_4 = True + supports_stream = True + + default_model = 'gptweb' + models = [default_model] + + model_aliases = { + "gpt-4": "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, + proxy: str = None, + markdown: bool = False, + **kwargs + ) -> AsyncResult: + headers = { + "Content-Type": "application/json" + } + async with ClientSession(headers=headers) as session: + prompt = format_prompt(messages) + data = { + "prompt": prompt, + "markdown": markdown + } + model = cls.get_model(model) + endpoint = cls.api_endpoint.format(model) + async with session.post(endpoint, json=data, proxy=proxy) as response: + response.raise_for_status() + response_text = await response.text() + + # Remove leading underscore if present + if response_text.startswith('_'): + response_text = response_text[1:] + + try: + response_data = json.loads(response_text) + yield response_data.get('gpt', response_text) + except json.JSONDecodeError: + yield response_text diff --git a/g4f/Provider/nexra/NexraDallE.py b/g4f/Provider/nexra/NexraDallE.py new file mode 100644 index 00000000..9c8ad12d --- /dev/null +++ b/g4f/Provider/nexra/NexraDallE.py @@ -0,0 +1,66 @@ +from __future__ import annotations + +from aiohttp import ClientSession +import json + +from ...typing import AsyncResult, Messages +from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin +from ...image import ImageResponse + + +class NexraDallE(AsyncGeneratorProvider, 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 + async def create_async_generator( + cls, + 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) + + # 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() + + 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) diff --git a/g4f/Provider/nexra/NexraDallE2.py b/g4f/Provider/nexra/NexraDallE2.py new file mode 100644 index 00000000..6b46e8cb --- /dev/null +++ b/g4f/Provider/nexra/NexraDallE2.py @@ -0,0 +1,74 @@ +from __future__ import annotations + +from aiohttp import ClientSession +import json + +from ...typing import AsyncResult, Messages +from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin +from ...image import ImageResponse + + +class NexraDallE2(AsyncGeneratorProvider, 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] + model_aliases = { + "dalle-2": "dalle2", + } + + @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, + proxy: str = None, + response: str = "url", # base64 or url + **kwargs + ) -> AsyncResult: + # Retrieve the correct model to use + model = cls.get_model(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() + + 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) diff --git a/g4f/Provider/nexra/NexraDalleMini.py b/g4f/Provider/nexra/NexraDalleMini.py new file mode 100644 index 00000000..7fcc7a81 --- /dev/null +++ b/g4f/Provider/nexra/NexraDalleMini.py @@ -0,0 +1,66 @@ +from __future__ import annotations + +from aiohttp import ClientSession +import json + +from ...typing import AsyncResult, Messages +from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin +from ...image import ImageResponse + + +class NexraDalleMini(AsyncGeneratorProvider, ProviderModelMixin): + label = "Nexra DALL-E Mini" + url = "https://nexra.aryahcr.cc/documentation/dall-e/en" + api_endpoint = "https://nexra.aryahcr.cc/api/image/complements" + working = True + + default_model = 'dalle-mini' + models = [default_model] + + @classmethod + def get_model(cls, model: str) -> str: + return cls.default_model + + @classmethod + async def create_async_generator( + cls, + 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) + + # 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() + + 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) diff --git a/g4f/Provider/nexra/NexraEmi.py b/g4f/Provider/nexra/NexraEmi.py new file mode 100644 index 00000000..0d3ed6ba --- /dev/null +++ b/g4f/Provider/nexra/NexraEmi.py @@ -0,0 +1,66 @@ +from __future__ import annotations + +from aiohttp import ClientSession +import json + +from ...typing import AsyncResult, Messages +from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin +from ...image import ImageResponse + + +class NexraEmi(AsyncGeneratorProvider, 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 + async def create_async_generator( + cls, + 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) + + # 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() + + 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) diff --git a/g4f/Provider/nexra/NexraFluxPro.py b/g4f/Provider/nexra/NexraFluxPro.py new file mode 100644 index 00000000..1dbab633 --- /dev/null +++ b/g4f/Provider/nexra/NexraFluxPro.py @@ -0,0 +1,74 @@ +from __future__ import annotations + +from aiohttp import ClientSession +import json + +from ...typing import AsyncResult, Messages +from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin +from ...image import ImageResponse + + +class NexraFluxPro(AsyncGeneratorProvider, ProviderModelMixin): + label = "Nexra Flux PRO" + 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 + async def create_async_generator( + cls, + 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) + + # 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() + + 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) diff --git a/g4f/Provider/nexra/NexraGeminiPro.py b/g4f/Provider/nexra/NexraGeminiPro.py index a57daed4..fb0b096b 100644 --- a/g4f/Provider/nexra/NexraGeminiPro.py +++ b/g4f/Provider/nexra/NexraGeminiPro.py @@ -1,17 +1,25 @@ from __future__ import annotations -import json from aiohttp import ClientSession - -from ...typing import AsyncResult, Messages +import json from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin from ..helper import format_prompt +from ...typing import AsyncResult, Messages class NexraGeminiPro(AsyncGeneratorProvider, ProviderModelMixin): label = "Nexra Gemini PRO" + url = "https://nexra.aryahcr.cc/documentation/gemini-pro/en" api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements" - models = ['gemini-pro'] + working = False + supports_stream = True + + default_model = 'gemini-pro' + models = [default_model] + + @classmethod + def get_model(cls, model: str) -> str: + return cls.default_model @classmethod async def create_async_generator( @@ -19,34 +27,42 @@ class NexraGeminiPro(AsyncGeneratorProvider, ProviderModelMixin): model: str, messages: Messages, proxy: str = None, + stream: bool = False, + markdown: bool = False, **kwargs ) -> AsyncResult: + model = cls.get_model(model) + headers = { "Content-Type": "application/json" } + + data = { + "messages": [ + { + "role": "user", + "content": format_prompt(messages) + } + ], + "markdown": markdown, + "stream": stream, + "model": model + } + 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 + buffer = "" + async for chunk in response.content.iter_any(): + if chunk.strip(): # Check if chunk is not empty + buffer += chunk.decode() + while '\x1e' in buffer: + part, buffer = buffer.split('\x1e', 1) + if part.strip(): + try: + response_json = json.loads(part) + message = response_json.get("message", "") + if message: + yield message + except json.JSONDecodeError as e: + print(f"JSONDecodeError: {e}") diff --git a/g4f/Provider/nexra/NexraImageURL.py b/g4f/Provider/nexra/NexraImageURL.py deleted file mode 100644 index 13d70757..00000000 --- a/g4f/Provider/nexra/NexraImageURL.py +++ /dev/null @@ -1,46 +0,0 @@ -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." diff --git a/g4f/Provider/nexra/NexraLLaMA31.py b/g4f/Provider/nexra/NexraLLaMA31.py new file mode 100644 index 00000000..d461f2b2 --- /dev/null +++ b/g4f/Provider/nexra/NexraLLaMA31.py @@ -0,0 +1,91 @@ +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 + + +class NexraLLaMA31(AsyncGeneratorProvider, ProviderModelMixin): + label = "Nexra LLaMA 3.1" + url = "https://nexra.aryahcr.cc/documentation/llama-3.1/en" + api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements" + working = True + supports_stream = True + + default_model = 'llama-3.1' + models = [default_model] + model_aliases = { + "llama-3.1-8b": "llama-3.1", + } + + @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.get(model, cls.default_model) + else: + return cls.default_model + + @classmethod + async def create_async_generator( + cls, + model: str, + messages: Messages, + proxy: str = None, + stream: bool = False, + markdown: bool = False, + **kwargs + ) -> AsyncResult: + model = cls.get_model(model) + + headers = { + "Content-Type": "application/json" + } + + async with ClientSession(headers=headers) as session: + prompt = format_prompt(messages) + data = { + "messages": [ + { + "role": "user", + "content": prompt + } + ], + "stream": stream, + "markdown": markdown, + "model": model + } + + async with session.post(f"{cls.api_endpoint}", json=data, proxy=proxy) as response: + response.raise_for_status() + + if stream: + # Streamed response handling + collected_message = "" + async for chunk in response.content.iter_any(): + if chunk: + decoded_chunk = chunk.decode().strip().split("\x1e") + for part in decoded_chunk: + if part: + message_data = json.loads(part) + + # Collect messages until 'finish': true + if 'message' in message_data and message_data['message']: + collected_message = message_data['message'] + + # When finish is true, yield the final collected message + if message_data.get('finish', False): + yield collected_message + return + else: + # Non-streamed response handling + response_data = await response.json(content_type=None) + + # Yield the message directly from the response + if 'message' in response_data and response_data['message']: + yield response_data['message'] + return diff --git a/g4f/Provider/nexra/NexraLlama.py b/g4f/Provider/nexra/NexraLlama.py deleted file mode 100644 index 9ed892e8..00000000 --- a/g4f/Provider/nexra/NexraLlama.py +++ /dev/null @@ -1,52 +0,0 @@ -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 diff --git a/g4f/Provider/nexra/NexraMidjourney.py b/g4f/Provider/nexra/NexraMidjourney.py new file mode 100644 index 00000000..e43cb164 --- /dev/null +++ b/g4f/Provider/nexra/NexraMidjourney.py @@ -0,0 +1,66 @@ +from __future__ import annotations + +from aiohttp import ClientSession +import json + +from ...typing import AsyncResult, Messages +from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin +from ...image import ImageResponse + + +class NexraMidjourney(AsyncGeneratorProvider, ProviderModelMixin): + label = "Nexra Midjourney" + url = "https://nexra.aryahcr.cc/documentation/midjourney/en" + api_endpoint = "https://nexra.aryahcr.cc/api/image/complements" + working = False + + default_model = 'midjourney' + models = [default_model] + + @classmethod + def get_model(cls, model: str) -> str: + return cls.default_model + + @classmethod + async def create_async_generator( + cls, + 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) + + # 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() + + 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) diff --git a/g4f/Provider/nexra/NexraProdiaAI.py b/g4f/Provider/nexra/NexraProdiaAI.py new file mode 100644 index 00000000..9d82ab9b --- /dev/null +++ b/g4f/Provider/nexra/NexraProdiaAI.py @@ -0,0 +1,147 @@ +from __future__ import annotations + +from aiohttp import ClientSession +import json + +from ...typing import AsyncResult, Messages +from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin +from ...image import ImageResponse + + +class NexraProdiaAI(AsyncGeneratorProvider, ProviderModelMixin): + label = "Nexra Prodia AI" + url = "https://nexra.aryahcr.cc/documentation/prodia/en" + api_endpoint = "https://nexra.aryahcr.cc/api/image/complements" + working = False + + 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 + async def create_async_generator( + cls, + model: str, # Select from the list of models + 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 + ) -> AsyncResult: + model = cls.get_model(model) + + headers = { + "Content-Type": "application/json" + } + async with ClientSession(headers=headers) as session: + prompt = messages[0]['content'] + data = { + "prompt": prompt, + "model": "prodia", + "response": response, + "data": { + "model": model, + "steps": steps, + "cfg_scale": cfg_scale, + "sampler": sampler, + "negative_prompt": negative_prompt + } + } + async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response: + text_data = await response.text() + + if response.status == 200: + try: + json_start = text_data.find('{') + json_data = text_data[json_start:] + + data = json.loads(json_data) + if 'images' in data and len(data['images']) > 0: + image_url = data['images'][-1] + 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) + else: + yield ImageResponse(f"Request failed with status: {response.status}", prompt) diff --git a/g4f/Provider/nexra/NexraQwen.py b/g4f/Provider/nexra/NexraQwen.py index ae8e9a0e..8bdf5475 100644 --- a/g4f/Provider/nexra/NexraQwen.py +++ b/g4f/Provider/nexra/NexraQwen.py @@ -1,7 +1,7 @@ from __future__ import annotations -import json from aiohttp import ClientSession +import json from ...typing import AsyncResult, Messages from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin @@ -10,8 +10,17 @@ from ..helper import format_prompt class NexraQwen(AsyncGeneratorProvider, ProviderModelMixin): label = "Nexra Qwen" + url = "https://nexra.aryahcr.cc/documentation/qwen/en" api_endpoint = "https://nexra.aryahcr.cc/api/chat/complements" - models = ['qwen'] + working = True + supports_stream = True + + default_model = 'qwen' + models = [default_model] + + @classmethod + def get_model(cls, model: str) -> str: + return cls.default_model @classmethod async def create_async_generator( @@ -19,34 +28,59 @@ class NexraQwen(AsyncGeneratorProvider, ProviderModelMixin): model: str, messages: Messages, proxy: str = None, + stream: bool = False, + markdown: bool = False, **kwargs ) -> AsyncResult: + model = cls.get_model(model) + headers = { - "Content-Type": "application/json" + "Content-Type": "application/json", + "accept": "application/json", + "origin": cls.url, + "referer": f"{cls.url}/chat", } async with ClientSession(headers=headers) as session: + prompt = format_prompt(messages) data = { "messages": [ - {'role': 'assistant', 'content': ''}, - {'role': 'user', 'content': format_prompt(messages)} + { + "role": "user", + "content": prompt + } ], - "markdown": False, - "stream": True, + "markdown": markdown, + "stream": stream, "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: + + complete_message = "" + + # If streaming, process each chunk separately + if stream: + async for chunk in response.content.iter_any(): + if chunk: try: - message = json.loads(message_str) - if message.get('message'): - full_response = message['message'] - if message.get('finish'): - yield full_response.strip() - return + # Decode the chunk and split by the delimiter + parts = chunk.decode('utf-8').split('\x1e') + for part in parts: + if part.strip(): # Ensure the part is not empty + response_data = json.loads(part) + message_part = response_data.get('message') + if message_part: + complete_message = message_part except json.JSONDecodeError: - pass + continue + + # Yield the final complete message + if complete_message: + yield complete_message + else: + # Handle non-streaming response + text_response = await response.text() + response_data = json.loads(text_response) + message = response_data.get('message') + if message: + yield message diff --git a/g4f/Provider/nexra/NexraSD15.py b/g4f/Provider/nexra/NexraSD15.py new file mode 100644 index 00000000..03b35013 --- /dev/null +++ b/g4f/Provider/nexra/NexraSD15.py @@ -0,0 +1,70 @@ +from __future__ import annotations + +import json +from aiohttp import ClientSession +from ...image import ImageResponse + +from ...typing import AsyncResult, Messages +from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin + + +class NexraSD15(AsyncGeneratorProvider, 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 = False + + 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 + async def create_async_generator( + cls, + model: str, + messages: Messages, + proxy: str = None, + response: str = "url", # base64 or url + **kwargs + ) -> AsyncResult: + model = cls.get_model(model) + + headers = { + "Content-Type": "application/json", + } + async with ClientSession(headers=headers) as session: + data = { + "prompt": messages, + "model": model, + "response": response + } + async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response: + response.raise_for_status() + text_response = await response.text() + + # Clean the response by removing unexpected characters + cleaned_response = text_response.strip('__') + + if not cleaned_response.strip(): + raise ValueError("Received an empty response from the server.") + + try: + json_response = json.loads(cleaned_response) + image_url = json_response.get("images", [])[0] + # Create an ImageResponse object + image_response = ImageResponse(images=image_url, alt="Generated Image") + yield image_response + except json.JSONDecodeError: + raise ValueError("Unable to decode JSON from the received text response.") diff --git a/g4f/Provider/nexra/NexraSD21.py b/g4f/Provider/nexra/NexraSD21.py new file mode 100644 index 00000000..46cd6611 --- /dev/null +++ b/g4f/Provider/nexra/NexraSD21.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +import json +from aiohttp import ClientSession +from ...image import ImageResponse + +from ...typing import AsyncResult, Messages +from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin + + +class NexraSD21(AsyncGeneratorProvider, ProviderModelMixin): + label = "Nexra Stable Diffusion 2.1" + url = "https://nexra.aryahcr.cc/documentation/stable-diffusion/en" + api_endpoint = "https://nexra.aryahcr.cc/api/image/complements" + working = False + + default_model = 'stablediffusion-2.1' + models = [default_model] + + model_aliases = { + "sd-2.1": "stablediffusion-2.1", + } + + @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, + proxy: str = None, + response: str = "url", # base64 or url + **kwargs + ) -> AsyncResult: + model = cls.get_model(model) + + headers = { + "Content-Type": "application/json", + } + async with ClientSession(headers=headers) as session: + # Directly use the messages as the prompt + data = { + "prompt": messages, + "model": model, + "response": response, + "data": { + "prompt_negative": "", + "guidance_scale": 9 + } + } + async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response: + response.raise_for_status() + text_response = await response.text() + + # Clean the response by removing unexpected characters + cleaned_response = text_response.strip('__') + + if not cleaned_response.strip(): + raise ValueError("Received an empty response from the server.") + + try: + json_response = json.loads(cleaned_response) + image_url = json_response.get("images", [])[0] + # Create an ImageResponse object + image_response = ImageResponse(images=image_url, alt="Generated Image") + yield image_response + except json.JSONDecodeError: + raise ValueError("Unable to decode JSON from the received text response.") diff --git a/g4f/Provider/nexra/NexraSDLora.py b/g4f/Provider/nexra/NexraSDLora.py new file mode 100644 index 00000000..a33afa04 --- /dev/null +++ b/g4f/Provider/nexra/NexraSDLora.py @@ -0,0 +1,68 @@ +from __future__ import annotations + +from aiohttp import ClientSession +import json + +from ...typing import AsyncResult, Messages +from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin +from ...image import ImageResponse + + +class NexraSDLora(AsyncGeneratorProvider, 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 = False + + default_model = 'sdxl-lora' + models = [default_model] + + @classmethod + def get_model(cls, model: str) -> str: + return cls.default_model + + @classmethod + async def create_async_generator( + 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 + ) -> AsyncResult: + model = cls.get_model(model) + + headers = { + "Content-Type": "application/json" + } + async with ClientSession(headers=headers) as session: + prompt = messages[0]['content'] + data = { + "prompt": prompt, + "model": model, + "response": response, + "data": { + "guidance": guidance, + "steps": steps + } + } + async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response: + text_data = await response.text() + + if response.status == 200: + try: + json_start = text_data.find('{') + json_data = text_data[json_start:] + + data = json.loads(json_data) + if 'images' in data and len(data['images']) > 0: + image_url = data['images'][-1] + 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) + else: + yield ImageResponse(f"Request failed with status: {response.status}", prompt) diff --git a/g4f/Provider/nexra/NexraSDTurbo.py b/g4f/Provider/nexra/NexraSDTurbo.py new file mode 100644 index 00000000..da1428b8 --- /dev/null +++ b/g4f/Provider/nexra/NexraSDTurbo.py @@ -0,0 +1,68 @@ +from __future__ import annotations + +from aiohttp import ClientSession +import json + +from ...typing import AsyncResult, Messages +from ..base_provider import AsyncGeneratorProvider, ProviderModelMixin +from ...image import ImageResponse + + +class NexraSDTurbo(AsyncGeneratorProvider, 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 = False + + default_model = 'sdxl-turbo' + models = [default_model] + + @classmethod + def get_model(cls, model: str) -> str: + return cls.default_model + + @classmethod + async def create_async_generator( + 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 + ) -> AsyncResult: + model = cls.get_model(model) + + headers = { + "Content-Type": "application/json" + } + async with ClientSession(headers=headers) as session: + prompt = messages[0]['content'] + data = { + "prompt": prompt, + "model": model, + "response": response, + "data": { + "strength": strength, + "steps": steps + } + } + async with session.post(cls.api_endpoint, json=data, proxy=proxy) as response: + text_data = await response.text() + + if response.status == 200: + try: + json_start = text_data.find('{') + json_data = text_data[json_start:] + + data = json.loads(json_data) + if 'images' in data and len(data['images']) > 0: + image_url = data['images'][-1] + 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) + else: + yield ImageResponse(f"Request failed with status: {response.status}", prompt) diff --git a/g4f/Provider/nexra/__init__.py b/g4f/Provider/nexra/__init__.py index 8b137891..c2e6b2f6 100644 --- a/g4f/Provider/nexra/__init__.py +++ b/g4f/Provider/nexra/__init__.py @@ -1 +1,20 @@ - +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 .NexraDalleMini import NexraDalleMini +from .NexraEmi import NexraEmi +from .NexraFluxPro import NexraFluxPro +from .NexraGeminiPro import NexraGeminiPro +from .NexraLLaMA31 import NexraLLaMA31 +from .NexraMidjourney import NexraMidjourney +from .NexraProdiaAI import NexraProdiaAI +from .NexraQwen import NexraQwen +from .NexraSD15 import NexraSD15 +from .NexraSD21 import NexraSD21 +from .NexraSDLora import NexraSDLora +from .NexraSDTurbo import NexraSDTurbo |