# -*- coding: utf-8 -*-

from backend.enrich import llm_enricher


class DummyResponse:
    def __init__(self, metadata):
        self.metadata = metadata


class DummyModel:
    def __init__(self, metadata):
        self.metadata = metadata
        self.calls = []

    async def __call__(self, messages, structured_model=None, **kwargs):
        self.calls.append(
            {
                "messages": messages,
                "structured_model": structured_model,
                "kwargs": kwargs,
            }
        )
        return DummyResponse(self.metadata)


def test_analyze_news_row_with_llm_uses_agentscope_model(monkeypatch):
    model = DummyModel(
        {
            "id": "news-1",
            "relevance": "high",
            "sentiment": "positive",
            "key_discussion": "Demand remains resilient",
            "summary": "Structured summary",
            "reason_growth": "Orders improved",
            "reason_decrease": "",
        }
    )
    monkeypatch.setattr(llm_enricher, "llm_enrichment_enabled", lambda: True)
    monkeypatch.setattr(llm_enricher, "_get_explain_model", lambda: model)
    monkeypatch.setattr(
        llm_enricher,
        "get_explain_model_info",
        lambda: {"provider": "DASHSCOPE", "model_name": "qwen-max", "label": "DASHSCOPE:qwen-max"},
    )

    result = llm_enricher.analyze_news_row_with_llm(
        {
            "id": "news-1",
            "title": "Apple expands AI features",
            "summary": "New devices and software updates were announced.",
        }
    )

    assert result["sentiment"] == "positive"
    assert result["summary"] == "Structured summary"
    assert result["raw_json"]["model_label"] == "DASHSCOPE:qwen-max"
    assert model.calls
    assert model.calls[0]["structured_model"] is llm_enricher.EnrichedNewsItem


def test_analyze_news_rows_with_llm_uses_agentscope_structured_batch(monkeypatch):
    model = DummyModel(
        {
            "items": [
                {
                    "id": "news-1",
                    "relevance": "high",
                    "sentiment": "negative",
                    "key_discussion": "Margin pressure",
                    "summary": "Batch summary",
                    "reason_growth": "",
                    "reason_decrease": "Costs rose",
                }
            ]
        }
    )
    monkeypatch.setattr(llm_enricher, "llm_enrichment_enabled", lambda: True)
    monkeypatch.setattr(llm_enricher, "_get_explain_model", lambda: model)
    monkeypatch.setattr(
        llm_enricher,
        "get_explain_model_info",
        lambda: {"provider": "DASHSCOPE", "model_name": "qwen-max", "label": "DASHSCOPE:qwen-max"},
    )

    result = llm_enricher.analyze_news_rows_with_llm(
        [
            {
                "id": "news-1",
                "title": "Apple margins pressured",
                "summary": "Costs increased this quarter.",
            }
        ]
    )

    assert result["news-1"]["sentiment"] == "negative"
    assert result["news-1"]["reason_decrease"] == "Costs rose"
    assert result["news-1"]["raw_json"]["model_label"] == "DASHSCOPE:qwen-max"
    assert model.calls
    assert model.calls[0]["structured_model"] is llm_enricher.EnrichedNewsBatch


def test_analyze_range_with_llm_uses_agentscope_structured_output(monkeypatch):
    model = DummyModel(
        {
            "summary": "该股在区间内震荡下行，相关新闻主要集中在盈利预期和供应链扰动。",
            "trend_analysis": "前半段受利空新闻压制，后半段跌幅收敛。",
            "bullish_factors": ["估值消化后出现部分承接"],
            "bearish_factors": ["盈利预期下修", "供应链扰动持续"],
        }
    )
    monkeypatch.setattr(llm_enricher, "llm_range_analysis_enabled", lambda: True)
    monkeypatch.setattr(llm_enricher, "_get_explain_model", lambda: model)
    monkeypatch.setattr(
        llm_enricher,
        "get_explain_model_info",
        lambda: {"provider": "DASHSCOPE", "model_name": "qwen-max", "label": "DASHSCOPE:qwen-max"},
    )

    result = llm_enricher.analyze_range_with_llm(
        {
            "ticker": "AAPL",
            "start_date": "2026-03-10",
            "end_date": "2026-03-16",
            "price_change_pct": -3.42,
        }
    )

    assert result["summary"].startswith("该股在区间内震荡下行")
    assert result["model_label"] == "DASHSCOPE:qwen-max"
    assert result["bearish_factors"] == ["盈利预期下修", "供应链扰动持续"]
    assert model.calls
    assert model.calls[0]["structured_model"] is llm_enricher.RangeAnalysisPayload