mirror of
https://github.com/xtekky/gpt4free.git
synced 2025-10-07 09:11:38 +08:00
341 lines
12 KiB
Python
341 lines
12 KiB
Python
from __future__ import annotations
|
|
|
|
import json
|
|
import random
|
|
import requests
|
|
from urllib.parse import quote_plus
|
|
from typing import Optional
|
|
from aiohttp import ClientSession
|
|
|
|
from .helper import filter_none, format_image_prompt
|
|
from .base_provider import AsyncGeneratorProvider, ProviderModelMixin
|
|
from ..typing import AsyncResult, Messages, ImagesType
|
|
from ..image import to_data_uri
|
|
from ..errors import ModelNotFoundError
|
|
from ..requests.raise_for_status import raise_for_status
|
|
from ..requests.aiohttp import get_connector
|
|
from ..providers.response import ImageResponse, ImagePreview, FinishReason, Usage, Audio, ToolCalls
|
|
from .. import debug
|
|
|
|
DEFAULT_HEADERS = {
|
|
"accept": "*/*",
|
|
'accept-language': 'en-US,en;q=0.9',
|
|
"user-agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/133.0.0.0 Safari/537.36",
|
|
"priority": "u=1, i",
|
|
"sec-ch-ua": "\"Not(A:Brand\";v=\"99\", \"Google Chrome\";v=\"133\", \"Chromium\";v=\"133\"",
|
|
"sec-ch-ua-mobile": "?0",
|
|
"sec-ch-ua-platform": "\"Linux\"",
|
|
"sec-fetch-dest": "empty",
|
|
"sec-fetch-mode": "cors",
|
|
"sec-fetch-site": "same-site",
|
|
"referer": "https://pollinations.ai/",
|
|
"origin": "https://pollinations.ai",
|
|
}
|
|
|
|
class PollinationsAI(AsyncGeneratorProvider, ProviderModelMixin):
|
|
label = "Pollinations AI"
|
|
url = "https://pollinations.ai"
|
|
|
|
working = True
|
|
supports_stream = False
|
|
supports_system_message = True
|
|
supports_message_history = True
|
|
|
|
# API endpoints
|
|
text_api_endpoint = "https://text.pollinations.ai"
|
|
openai_endpoint = "https://text.pollinations.ai/openai"
|
|
image_api_endpoint = "https://image.pollinations.ai/"
|
|
|
|
# Models configuration
|
|
default_model = "openai"
|
|
default_image_model = "flux"
|
|
default_vision_model = default_model
|
|
text_models = [default_model]
|
|
image_models = [default_image_model]
|
|
extra_image_models = ["flux-pro", "flux-dev", "flux-schnell", "midjourney", "dall-e-3"]
|
|
vision_models = [default_vision_model, "gpt-4o-mini", "o1-mini", "openai", "openai-large"]
|
|
extra_text_models = vision_models
|
|
_models_loaded = False
|
|
model_aliases = {
|
|
### Text Models ###
|
|
"gpt-4o-mini": "openai",
|
|
"gpt-4": "openai-large",
|
|
"gpt-4o": "openai-large",
|
|
"o1-mini": "openai-reasoning",
|
|
"qwen-2.5-coder-32b": "qwen-coder",
|
|
"llama-3.3-70b": "llama",
|
|
"mistral-nemo": "mistral",
|
|
"gpt-4o-mini": "searchgpt",
|
|
"llama-3.1-8b": "llamalight",
|
|
"llama-3.3-70b": "llama-scaleway",
|
|
"phi-4": "phi",
|
|
"gemini-2.0": "gemini",
|
|
"gemini-2.0-flash": "gemini",
|
|
"gemini-2.0-flash-thinking": "gemini-thinking",
|
|
|
|
### Image Models ###
|
|
"sdxl-turbo": "turbo",
|
|
}
|
|
|
|
@classmethod
|
|
def get_models(cls, **kwargs):
|
|
if not cls._models_loaded:
|
|
try:
|
|
# Update of image models
|
|
image_response = requests.get("https://image.pollinations.ai/models")
|
|
if image_response.ok:
|
|
new_image_models = image_response.json()
|
|
else:
|
|
new_image_models = []
|
|
|
|
# Combine models without duplicates
|
|
all_image_models = (
|
|
cls.image_models + # Already contains the default
|
|
cls.extra_image_models +
|
|
new_image_models
|
|
)
|
|
cls.image_models = list(dict.fromkeys(all_image_models))
|
|
|
|
# Update of text models
|
|
text_response = requests.get("https://text.pollinations.ai/models")
|
|
text_response.raise_for_status()
|
|
models = text_response.json()
|
|
original_text_models = [
|
|
model.get("name")
|
|
for model in models
|
|
if model.get("type") == "chat"
|
|
]
|
|
cls.audio_models = {
|
|
model.get("name"): model.get("voices")
|
|
for model in models
|
|
if model.get("audio")
|
|
}
|
|
|
|
# Combining text models
|
|
combined_text = (
|
|
cls.text_models + # Already contains the default
|
|
cls.extra_text_models +
|
|
[
|
|
model for model in original_text_models
|
|
if model not in cls.extra_text_models
|
|
]
|
|
)
|
|
cls.text_models = list(dict.fromkeys(combined_text))
|
|
|
|
cls._models_loaded = True
|
|
|
|
except Exception as e:
|
|
# Save default models in case of an error
|
|
if not cls.text_models:
|
|
cls.text_models = [cls.default_model]
|
|
if not cls.image_models:
|
|
cls.image_models = [cls.default_image_model]
|
|
debug.error(f"Failed to fetch models: {e}")
|
|
|
|
return cls.text_models + cls.image_models
|
|
|
|
@classmethod
|
|
async def create_async_generator(
|
|
cls,
|
|
model: str,
|
|
messages: Messages,
|
|
stream: bool = False,
|
|
proxy: str = None,
|
|
cache: bool = False,
|
|
# Image generation parameters
|
|
prompt: str = None,
|
|
width: int = 1024,
|
|
height: int = 1024,
|
|
seed: Optional[int] = None,
|
|
nologo: bool = True,
|
|
private: bool = False,
|
|
enhance: bool = False,
|
|
safe: bool = False,
|
|
# Text generation parameters
|
|
images: ImagesType = None,
|
|
temperature: float = None,
|
|
presence_penalty: float = None,
|
|
top_p: float = 1,
|
|
frequency_penalty: float = None,
|
|
response_format: Optional[dict] = None,
|
|
extra_parameters: list[str] = ["tools", "parallel_tool_calls", "tool_choice", "reasoning_effort", "logit_bias", "voice"],
|
|
**kwargs
|
|
) -> AsyncResult:
|
|
# Load model list
|
|
cls.get_models()
|
|
try:
|
|
model = cls.get_model(model)
|
|
except ModelNotFoundError:
|
|
if model not in cls.image_models:
|
|
raise
|
|
|
|
if model in cls.image_models:
|
|
async for chunk in cls._generate_image(
|
|
model=model,
|
|
prompt=format_image_prompt(messages, prompt),
|
|
proxy=proxy,
|
|
width=width,
|
|
height=height,
|
|
seed=seed,
|
|
cache=cache,
|
|
nologo=nologo,
|
|
private=private,
|
|
enhance=enhance,
|
|
safe=safe
|
|
):
|
|
yield chunk
|
|
else:
|
|
async for result in cls._generate_text(
|
|
model=model,
|
|
messages=messages,
|
|
images=images,
|
|
proxy=proxy,
|
|
temperature=temperature,
|
|
presence_penalty=presence_penalty,
|
|
top_p=top_p,
|
|
frequency_penalty=frequency_penalty,
|
|
response_format=response_format,
|
|
seed=seed,
|
|
cache=cache,
|
|
stream=stream,
|
|
extra_parameters=extra_parameters,
|
|
**kwargs
|
|
):
|
|
yield result
|
|
|
|
@classmethod
|
|
async def _generate_image(
|
|
cls,
|
|
model: str,
|
|
prompt: str,
|
|
proxy: str,
|
|
width: int,
|
|
height: int,
|
|
seed: Optional[int],
|
|
cache: bool,
|
|
nologo: bool,
|
|
private: bool,
|
|
enhance: bool,
|
|
safe: bool
|
|
) -> AsyncResult:
|
|
if not cache and seed is None:
|
|
seed = random.randint(9999, 99999999)
|
|
params = {
|
|
"seed": str(seed) if seed is not None else None,
|
|
"width": str(width),
|
|
"height": str(height),
|
|
"model": model,
|
|
"nologo": str(nologo).lower(),
|
|
"private": str(private).lower(),
|
|
"enhance": str(enhance).lower(),
|
|
"safe": str(safe).lower()
|
|
}
|
|
query = "&".join(f"{k}={quote_plus(v)}" for k, v in params.items() if v is not None)
|
|
url = f"{cls.image_api_endpoint}prompt/{quote_plus(prompt)}?{query}"
|
|
#yield ImagePreview(url, prompt)
|
|
|
|
async with ClientSession(headers=DEFAULT_HEADERS, connector=get_connector(proxy=proxy)) as session:
|
|
async with session.get(url, allow_redirects=True) as response:
|
|
await raise_for_status(response)
|
|
image_url = str(response.url)
|
|
yield ImageResponse(image_url, prompt)
|
|
|
|
@classmethod
|
|
async def _generate_text(
|
|
cls,
|
|
model: str,
|
|
messages: Messages,
|
|
images: Optional[ImagesType],
|
|
proxy: str,
|
|
temperature: float,
|
|
presence_penalty: float,
|
|
top_p: float,
|
|
frequency_penalty: float,
|
|
response_format: Optional[dict],
|
|
seed: Optional[int],
|
|
cache: bool,
|
|
stream: bool,
|
|
extra_parameters: list[str],
|
|
**kwargs
|
|
) -> AsyncResult:
|
|
if not cache and seed is None:
|
|
seed = random.randint(9999, 99999999)
|
|
json_mode = False
|
|
if response_format and response_format.get("type") == "json_object":
|
|
json_mode = True
|
|
|
|
if images and messages:
|
|
last_message = messages[-1].copy()
|
|
image_content = [
|
|
{
|
|
"type": "image_url",
|
|
"image_url": {"url": to_data_uri(image)}
|
|
}
|
|
for image, _ in images
|
|
]
|
|
last_message["content"] = image_content + [{"type": "text", "text": last_message["content"]}]
|
|
messages[-1] = last_message
|
|
|
|
async with ClientSession(headers=DEFAULT_HEADERS, connector=get_connector(proxy=proxy)) as session:
|
|
if model in cls.audio_models:
|
|
#data["voice"] = random.choice(cls.audio_models[model])
|
|
url = cls.text_api_endpoint
|
|
stream = False
|
|
else:
|
|
url = cls.openai_endpoint
|
|
extra_parameters = {param: kwargs[param] for param in extra_parameters if param in kwargs}
|
|
data = filter_none(**{
|
|
"messages": messages,
|
|
"model": model,
|
|
"temperature": temperature,
|
|
"presence_penalty": presence_penalty,
|
|
"top_p": top_p,
|
|
"frequency_penalty": frequency_penalty,
|
|
"jsonMode": json_mode,
|
|
"stream": stream,
|
|
"seed": seed,
|
|
"cache": cache,
|
|
**extra_parameters
|
|
})
|
|
async with session.post(url, json=data) as response:
|
|
await raise_for_status(response)
|
|
if response.headers["content-type"] == "audio/mpeg":
|
|
yield Audio(await response.read())
|
|
return
|
|
elif response.headers["content-type"].startswith("text/plain"):
|
|
yield await response.text()
|
|
return
|
|
elif response.headers["content-type"].startswith("text/event-stream"):
|
|
async for line in response.content:
|
|
if line.startswith(b"data: "):
|
|
if line[6:].startswith(b"[DONE]"):
|
|
break
|
|
result = json.loads(line[6:])
|
|
choice = result.get("choices", [{}])[0]
|
|
content = choice.get("delta", {}).get("content")
|
|
if content:
|
|
yield content
|
|
if "usage" in result:
|
|
yield Usage(**result["usage"])
|
|
finish_reason = choice.get("finish_reason")
|
|
if finish_reason:
|
|
yield FinishReason(finish_reason)
|
|
return
|
|
result = await response.json()
|
|
choice = result["choices"][0]
|
|
message = choice.get("message", {})
|
|
content = message.get("content", "")
|
|
|
|
if "tool_calls" in message:
|
|
yield ToolCalls(message["tool_calls"])
|
|
|
|
if content:
|
|
yield content
|
|
|
|
if "usage" in result:
|
|
yield Usage(**result["usage"])
|
|
|
|
finish_reason = choice.get("finish_reason")
|
|
if finish_reason:
|
|
yield FinishReason(finish_reason)
|