from pydantic import BaseModel, Field
from SimpleLLMFunc import OpenAICompatible, llm_function

models = OpenAICompatible.load_from_json_file("provider.json")
llm = models["openrouter"]["openai/gpt-4o"]


class Translation(BaseModel):
    text: str = Field(description="The translated text")
    confidence: float = Field(description="0.0-1.0 translation confidence")


@llm_function(llm_interface=llm)
async def translate(text: str, target_language: str) -> Translation:
    """
    Translate the text to the target language.
    Preserve tone and intent. If uncertain about a phrase, lower confidence.
    """
    pass


result = await translate("Hello world", "French")
# Translation(text="Bonjour le monde", confidence=0.95)

@llm_function(llm_interface=llm)
async def analyze(review: str) -> ProductReview:
    """Analyze the product review..."""
    pass

@llm_function(llm_interface=llm)
async def write_poem(topic: str) -> str:
    """Write a short poem about the topic."""
    pass

@llm_function(llm_interface=llm)
async def extract_entities(text: str) -> list[Entity]:
    """Extract all named entities."""
    pass

@llm_function(llm_interface=llm)
async def score(text: str) -> float:
    """Rate quality from 0.0 to 1.0."""
    pass

@llm_function(llm_interface=llm)
async def answer(question: str, context: str) -> str:
    """
    Answer the question using the provided context.

    Domain: {domain}
    Style: {style}
    """
    pass

result = await answer(
    "What is X?",
    "X is a framework...",
    _template_params={"domain": "software engineering", "style": "concise"},
)

from SimpleLLMFunc import llm_function
from SimpleLLMFunc.type import ImgPath, ImgUrl, Text

@llm_function(llm_interface=llm)
async def compare_images(
    instruction: Text,
    reference: ImgUrl,
    candidate: ImgPath,
) -> str:
    """Compare the two images according to the instruction."""
    pass

result = await compare_images(
    Text("Explain the visual differences."),
    ImgUrl("https://example.com/reference.jpg", detail="high"),
    ImgPath("./candidate.png", detail="high"),
)

@llm_function(llm_interface=llm, toolkit=[search_tool], max_tool_calls=5)
async def research(question: str) -> ResearchReport:
    """Research the question using search. Cite your sources."""
    pass

from SimpleLLMFunc.hooks import is_response_yield, is_event_yield

async for output in translate.stream("hello", "French"):
    if is_response_yield(output):
        result = output.response  # 类型化的 Translation 对象
    elif is_event_yield(output):
        # LLM 事件、工具事件等
        pass

from SimpleLLMFunc.hooks import responses_only

async for response in responses_only(translate.stream("hello", "French")):
    result = response.response

from SimpleLLMFunc.hooks import AbortSignal

signal = AbortSignal()

async def run():
    async for output in translate.stream("...", "French", _abort_signal=signal):
        ...

# 从另一个任务中取消：
signal.abort("timeout")