from __future__ import annotations

from typing import List, Tuple

from app.models import (
    DataImportAnalysisRequest,
    LLMMessage,
    LLMProvider,
    LLMRole,
)


def resolve_provider_from_model(llm_model: str) -> Tuple[LLMProvider, str]:
    """Resolve provider based on the llm_model string.

    The llm_model may be provided as 'provider:model' or 'provider/model'.
    If no provider prefix is present, try an educated guess from common model name patterns.
    """

    normalized = llm_model.strip()
    provider_hint: str | None = None
    model_name = normalized

    for delimiter in (":", "/", "|"):
        if delimiter in normalized:
            provider_hint, model_name = normalized.split(delimiter, 1)
            provider_hint = provider_hint.strip().lower()
            model_name = model_name.strip()
            break

    provider_map = {provider.value: provider for provider in LLMProvider}

    if provider_hint:
        if provider_hint not in provider_map:
            raise ValueError(
                f"Unsupported provider '{provider_hint}'. Expected one of: {', '.join(provider_map.keys())}."
            )
        return provider_map[provider_hint], model_name

    return _guess_provider_from_model(model_name), model_name


def _guess_provider_from_model(model_name: str) -> LLMProvider:
    lowered = model_name.lower()

    if lowered.startswith(("gpt", "o1", "text-", "dall-e", "whisper")):
        return LLMProvider.OPENAI
    if lowered.startswith(("claude", "anthropic")):
        return LLMProvider.ANTHROPIC
    if lowered.startswith(("gemini", "models/gemini")):
        return LLMProvider.GEMINI
    if lowered.startswith("qwen"):
        return LLMProvider.QWEN
    if lowered.startswith("deepseek"):
        return LLMProvider.DEEPSEEK
    if lowered.startswith(("openrouter", "router-")):
        return LLMProvider.OPENROUTER

    supported = ", ".join(provider.value for provider in LLMProvider)
    raise ValueError(
        f"Unable to infer provider from model '{model_name}'. "
        f"Please prefix with 'provider:model'. Supported providers: {supported}."
    )


def build_import_messages(
    request: DataImportAnalysisRequest,
) -> List[LLMMessage]:
    """Create system and user messages for the import analysis prompt."""
    headers_formatted = "\n".join(f"- {header}" for header in request.table_headers)

    system_prompt = (
        "你是一名数据导入识别助手。请根据给定的表头和示例数据，判断字段含义、"
        "典型数据类型以及潜在的数据质量问题。最终请返回一个结构化的JSON。\n"
        "JSON结构需包含: field_summaries (数组, 每项含 header、meaning、data_type、quality_notes), "
        "detected_issues (字符串数组)，以及 overall_suggestion (字符串)。"
    )

    user_prompt = (
        f"导入记录ID: {request.import_record_id}\n\n"
        "表头信息:\n"
        f"{headers_formatted}\n\n"
        "示例数据:\n"
        f"{request.example_data}\n\n"
        "请仔细分析示例数据与表头之间的对应关系，并返回符合上述JSON结构的内容。"
    )

    return [
        LLMMessage(role=LLMRole.SYSTEM, content=system_prompt),
        LLMMessage(role=LLMRole.USER, content=user_prompt),
    ]