//! 对抗式评估系统 — 正反方辩论 + AI 分析师
//!
//! 双轨实现：
//! - **启发式**（默认/降级）：基于评分与内容信号生成正反方论点，稳定有区分度。
//! - **LLM**（注入 provider 后）：调一次 `complete()` 让论点由 LLM 生成，失败自动降级启发式。
//!
//! 评估来源由 [`EvaluatedBy`] 三态标记：`Llm`（LLM 深度评估）/ `Heuristic`（主动选启发式，
//! 无 provider）/ `HeuristicFallback`（LLM 调用失败降级）。前端可据此显示评估深度标签。
//!
//! LLM prompt 构造与 JSON 解析在 F-260614-03（已由本任务解锁）接入，当前 `evaluate_with_llm`
//! 返回 Err 触发降级路径——机制完整，仅缺 prompt/解析实现。

use std::sync::Arc;

use anyhow::Result;
use serde::{Deserialize, Serialize};

use df_ai_core::provider::LlmProvider;
use df_core::types::{IdeaId, Priority};
use crate::capture::Idea;
use crate::scoring::IdeaScores;

/// 评估来源标记
///
/// `Default = Heuristic`：老数据（F-07 之前）序列化时无 evaluated_by 字段，
/// 反序列化回落启发式（与 F-07 之前行为一致）。
#[derive(Debug, Clone, Default, Serialize, Deserialize, PartialEq, Eq)]
pub enum EvaluatedBy {
    /// LLM 深度评估
    Llm,
    /// 启发式评估（无 LLM 配置时的默认模式，也是老数据反序列化默认值）
    #[default]
    Heuristic,
    /// 启发式降级（LLM 调用失败后 fallback）
    HeuristicFallback,
}

/// 对抗评估结果
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AdversarialEval {
    pub idea_id: IdeaId,
    pub positive: Argument,
    pub negative: Argument,
    pub analyst: AnalystAnalysis,
    pub final_score: f64,
    pub recommendation: Recommendation,
    /// 评估来源（Llm / Heuristic / HeuristicFallback），前端据此显示评估深度标签
    #[serde(default)]
    pub evaluated_by: EvaluatedBy,
}

/// 论点（正方/反方共用同一结构）
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Argument {
    pub thesis: String,           // 核心观点
    pub evidence: Vec<String>,    // 证据支持
    pub reasoning: Vec<String>,   // 推理过程
    pub confidence: f64,          // 置信度 0-1
}

/// AI 分析师综合分析
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnalystAnalysis {
    pub summary: String,                    // 综合总结
    pub strengths: Vec<String>,             // 主要优势
    pub weaknesses: Vec<String>,            // 主要劣势
    pub risks: Vec<String>,                // 潜在风险
    pub opportunities: Vec<String>,          // 机会点
    pub final_assessment: AssessmentLevel,  // 最终评估
}

/// 评估等级
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum AssessmentLevel {
    StrongGo,      // 强烈推荐执行
    Recommended,    // 推荐执行
    Conditional,   // 有条件执行
    Revised,       // 需要修改后执行
    Defer,         // 推迟执行
}

/// 最终建议
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
pub enum Recommendation {
    ImmediateAction,   // 立即行动
    Soon,              // 尽快行动
    WithResources,      // 配置资源后行动
    ResearchMore,      // 需要更多研究
    Monitor,           // 持续监控
}

/// 对抗评估引擎
pub struct AdversarialEngine {
    /// 可选 LLM provider。Some → 优先 LLM 评估（失败降级启发式）；None → 纯启发式。
    /// 构造注入（与 IdeaPromoter::new(policy) 同一模式），批量评估复用同一 provider。
    provider: Option<Arc<dyn LlmProvider>>,
}

impl AdversarialEngine {
    /// 注入 LLM provider 构造（provider Some 时走 LLM，调用失败自动降级启发式）
    pub fn new(provider: Arc<dyn LlmProvider>) -> Self {
        Self { provider: Some(provider) }
    }

    /// 纯启发式构造（无 LLM 配置时的默认模式）
    pub fn heuristic() -> Self {
        Self { provider: None }
    }

    /// 执行完整的对抗评估（内部按 provider 有无调度 LLM / 启发式，失败降级）
    pub async fn evaluate(&self, idea: &Idea) -> Result<AdversarialEval> {
        match &self.provider {
            Some(p) => match self.evaluate_with_llm(idea, p).await {
                Ok(mut eval) => {
                    eval.evaluated_by = EvaluatedBy::Llm;
                    Ok(eval)
                }
                Err(e) => {
                    // LLM 调用失败/超时/格式异常 → 自动降级启发式，保证前端结构完整返回
                    tracing::warn!("LLM 对抗评估失败, 降级到启发式: {e}");
                    let mut eval = self.evaluate_heuristic(idea)?;
                    eval.evaluated_by = EvaluatedBy::HeuristicFallback;
                    Ok(eval)
                }
            },
            None => {
                let mut eval = self.evaluate_heuristic(idea)?;
                eval.evaluated_by = EvaluatedBy::Heuristic;
                Ok(eval)
            }
        }
    }

    /// LLM 对抗评估（注入 provider 后走此路）。
    ///
    /// prompt 构造 + JSON 解析在 F-260614-03（已由本任务解锁）接入。当前返回 Err
    /// 触发降级路径——降级机制与启发式评估路径完整，仅缺 LLM 调用实现。
    async fn evaluate_with_llm(&self, _idea: &Idea, _provider: &Arc<dyn LlmProvider>) -> Result<AdversarialEval> {
        anyhow::bail!("LLM 对抗评估尚未实现(F-260614-03)")
    }

    /// 启发式评估（基于评分与内容信号，稳定有区分度）
    fn evaluate_heuristic(&self, idea: &Idea) -> Result<AdversarialEval> {
        // 先做多维评分，作为正反方论点与置信度的依据
        let scores = crate::scoring::ScoringEngine::compute_default(idea);

        let positive = self.generate_positive_argument(idea, &scores)?;
        let negative = self.generate_negative_argument(idea, &scores)?;
        let analyst = self.analyst_analysis(idea, &scores)?;
        let recommendation = self.recommendation_for(&analyst.final_assessment);

        Ok(AdversarialEval {
            idea_id: idea.id.clone(),
            positive,
            negative,
            analyst,
            final_score: scores.overall,
            recommendation,
            // 由 evaluate() 调用方按调度路径覆盖（Heuristic / HeuristicFallback）
            evaluated_by: EvaluatedBy::Heuristic,
        })
    }

    /// 生成正方观点（支持执行）— confidence 由可行性 + 影响力驱动
    /// 注:返回 Result 为后续 LLM 注入失败预留,启发式阶段恒 Ok
    fn generate_positive_argument(&self, idea: &Idea, scores: &IdeaScores) -> Result<Argument> {
        let desc = idea.description.trim();
        let mut evidence = Vec::new();
        evidence.push(format!("优先级：{}", priority_label(&idea.priority)));
        if desc.is_empty() {
            evidence.push("需求待补充（建议补全描述）".to_string());
        } else {
            let head: String = desc.chars().take(60).collect();
            evidence.push(format!("明确需求：{}", head));
        }
        if idea.tags.is_empty() {
            evidence.push("关联领域待界定".to_string());
        } else {
            evidence.push(format!("关联领域：{}", idea.tags.join("、")));
        }
        if scores.impact >= 7.0 {
            evidence.push("业务价值显著，影响面较广".to_string());
        }

        // 正方置信度:可行性+影响力等权折算到 [0.1, 0.95],满分≈0.95 留质疑余地
        let confidence =
            ((scores.feasibility * 0.5 + scores.impact * 0.5) / 10.0).clamp(0.1, 0.95);

        let reasoning = vec![
            format!("可行性评分 {:.1}/10，路径相对清晰", scores.feasibility),
            format!("影响力评分 {:.1}/10，预期回报可观", scores.impact),
            "整体风险可控，适合推进".to_string(),
        ];

        Ok(Argument {
            thesis: format!("「{}」具备明确价值与可行性，建议优先推进", idea.title),
            evidence,
            reasoning,
            confidence,
        })
    }

    /// 生成反方观点（反对或谨慎）— 论点基于想法实际缺陷，confidence 随风险上升
    fn generate_negative_argument(&self, idea: &Idea, scores: &IdeaScores) -> Result<Argument> {
        let desc = idea.description.trim();
        let mut evidence = Vec::new();
        if desc.is_empty() {
            evidence.push("描述过于简略，需求边界不清".to_string());
        } else if desc.chars().count() < 50 {
            evidence.push("描述偏短，实现细节尚未论证".to_string());
        }
        if idea.tags.is_empty() {
            evidence.push("缺少标签，影响范围未界定".to_string());
        }
        if scores.feasibility < 6.0 {
            evidence.push(format!("可行性 {:.1}/10 偏低，实现路径存疑", scores.feasibility));
        }
        if matches!(idea.priority, Priority::Low) {
            evidence.push("优先级偏低，可能非当前关键路径".to_string());
        }
        if evidence.is_empty() {
            evidence.push("机会成本需权衡，可能存在更优替代方案".to_string());
        }

        // 反方强度:feasibility 每降 1 分 +0.04,impact 每降 1 分 +0.03,基线 0.25(满分也保留最低质疑),clamp [0.1, 0.9]
        let confidence = ((10.0 - scores.feasibility) * 0.04 + (10.0 - scores.impact) * 0.03 + 0.25)
            .clamp(0.1, 0.9);

        let reasoning = vec![
            format!("资源投入与当前综合评分 {:.1} 需匹配", scores.overall),
            "ROI 需进一步验证".to_string(),
            "需评估是否存在更优解".to_string(),
        ];

        Ok(Argument {
            thesis: format!("「{}」需谨慎评估，存在风险与机会成本", idea.title),
            evidence,
            reasoning,
            confidence,
        })
    }

    /// AI 分析师综合分析 — 评估等级由综合评分决定，优势/劣势按维度动态生成
    fn analyst_analysis(&self, idea: &Idea, scores: &IdeaScores) -> Result<AnalystAnalysis> {
        let final_assessment = match scores.overall {
            x if x >= 7.5 => AssessmentLevel::StrongGo,
            x if x >= 6.0 => AssessmentLevel::Recommended,
            x if x >= 4.5 => AssessmentLevel::Conditional,
            x if x >= 3.0 => AssessmentLevel::Revised,
            _ => AssessmentLevel::Defer,
        };

        let mut strengths = Vec::new();
        if scores.impact >= 6.0 {
            strengths.push("业务价值明确".to_string());
        }
        if scores.feasibility >= 6.0 {
            strengths.push("技术路径清晰".to_string());
        }
        if scores.urgency >= 7.0 {
            strengths.push("时间窗口合适".to_string());
        }
        if strengths.is_empty() {
            strengths.push("方向值得探索".to_string());
        }

        let mut weaknesses = Vec::new();
        if scores.feasibility < 6.0 {
            weaknesses.push("可行性论证不足".to_string());
        }
        if idea.description.trim().is_empty() {
            weaknesses.push("需求描述缺失".to_string());
        }
        if scores.urgency < 4.0 {
            weaknesses.push("紧急度偏低，易被搁置".to_string());
        }
        if weaknesses.is_empty() {
            weaknesses.push("资源需求待评估".to_string());
        }

        // 启发式占位:固定风险模板,与具体想法无关,接 LLM 后改动态生成
        let risks = vec![
            "技术实现难度可能超出预期".to_string(),
            "优先级与资源争夺".to_string(),
            "需求范围蔓延".to_string(),
        ];

        let opportunities = vec![
            "可能形成可复用能力".to_string(),
            "积累技术资产".to_string(),
        ];

        let summary = format!(
            "「{}」综合评分 {:.1}/10，{}。建议{}。",
            idea.title,
            scores.overall,
            assessment_desc(&final_assessment),
            action_hint(&final_assessment)
        );

        Ok(AnalystAnalysis {
            summary,
            strengths,
            weaknesses,
            risks,
            opportunities,
            final_assessment,
        })
    }

    /// 评估等级 → 最终建议
    fn recommendation_for(&self, level: &AssessmentLevel) -> Recommendation {
        match level {
            AssessmentLevel::StrongGo => Recommendation::ImmediateAction,
            AssessmentLevel::Recommended => Recommendation::Soon,
            AssessmentLevel::Conditional => Recommendation::WithResources,
            AssessmentLevel::Revised => Recommendation::ResearchMore,
            AssessmentLevel::Defer => Recommendation::Monitor,
        }
    }
}

fn priority_label(p: &Priority) -> &'static str {
    match p {
        Priority::Critical => "紧急",
        Priority::High => "高",
        Priority::Medium => "中",
        Priority::Low => "低",
    }
}

fn assessment_desc(level: &AssessmentLevel) -> &'static str {
    match level {
        AssessmentLevel::StrongGo => "价值高且可行性强",
        AssessmentLevel::Recommended => "整体值得推进",
        AssessmentLevel::Conditional => "有条件地推进",
        AssessmentLevel::Revised => "需调整后再评估",
        AssessmentLevel::Defer => "建议暂缓",
    }
}

fn action_hint(level: &AssessmentLevel) -> &'static str {
    match level {
        AssessmentLevel::StrongGo => "立即立项启动",
        AssessmentLevel::Recommended => "尽快排期",
        AssessmentLevel::Conditional => "配置资源后启动",
        AssessmentLevel::Revised => "补充信息后重新评估",
        AssessmentLevel::Defer => "持续观察时机",
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::capture::Idea;
    use crate::scoring::ScoringEngine;
    use df_core::types::{IdeaStatus, Priority};

    fn make_idea(title: &str, desc: &str, priority: Priority, tags: Vec<&str>) -> Idea {
        Idea {
            id: "test-id".to_string(),
            title: title.to_string(),
            description: desc.to_string(),
            status: IdeaStatus::Draft,
            priority,
            scores: None,
            tags: tags.into_iter().map(String::from).collect(),
            source: None,
            related_ids: Vec::new(),
            created_at: chrono::Utc::now(),
            updated_at: chrono::Utc::now(),
        }
    }

    #[tokio::test]
    async fn a1_high_score_immediate_action() {
        let desc = "面向用户的核心功能，带来显著增长，大幅提升效率。集成成熟方案，复用已有组件。".repeat(3);
        let idea = make_idea("AI增长引擎", &desc, Priority::Critical, vec!["增长", "核心"]);
        let scores = ScoringEngine::compute_default(&idea);
        let eval = AdversarialEngine::heuristic().evaluate(&idea).await.unwrap();
        println!("\n[a1] 高分想法 → 期望 ImmediateAction");
        println!("  scores: feas={:.2} impact={:.2} urg={:.2} overall={:.2}", scores.feasibility, scores.impact, scores.urgency, scores.overall);
        println!("  eval: final_score={:.2} recommendation={:?}", eval.final_score, eval.recommendation);
        println!("  正方 confidence={:.2}  反方 confidence={:.2}", eval.positive.confidence, eval.negative.confidence);
        assert!(eval.final_score >= 7.5, "final_score 应≥7.5, 实际 {:.2}", eval.final_score);
        assert_eq!(eval.recommendation, Recommendation::ImmediateAction);
    }

    #[tokio::test]
    async fn a2_mid_score_soon() {
        let desc = "面向用户的功能，集成已有方案，提升体验".to_string();
        let idea = make_idea("体验优化", &desc, Priority::Medium, vec!["体验"]);
        let scores = ScoringEngine::compute_default(&idea);
        let eval = AdversarialEngine::heuristic().evaluate(&idea).await.unwrap();
        println!("\n[a2] 中分想法 → 期望 Soon");
        println!("  scores overall={:.2}  eval final_score={:.2}  recommendation={:?}", scores.overall, eval.final_score, eval.recommendation);
        assert_eq!(eval.recommendation, Recommendation::Soon);
    }

    #[tokio::test]
    async fn a3_low_score_monitor() {
        let desc = "重构迁移大规模分布式重写从零全新架构高并发底层".to_string();
        let idea = make_idea("过度工程", &desc, Priority::Low, vec![]);
        let scores = ScoringEngine::compute_default(&idea);
        let eval = AdversarialEngine::heuristic().evaluate(&idea).await.unwrap();
        println!("\n[a3] 低分想法 → 期望 Monitor");
        println!("  scores overall={:.2}  eval final_score={:.2}  recommendation={:?}", scores.overall, eval.final_score, eval.recommendation);
        assert!(eval.final_score < 3.0, "final_score 应<3.0, 实际 {:.2}", eval.final_score);
        assert_eq!(eval.recommendation, Recommendation::Monitor);
    }

    #[tokio::test]
    async fn a4_confidence_ranges() {
        let idea = make_idea("普通想法", "一般描述", Priority::Medium, vec!["标签"]);
        let eval = AdversarialEngine::heuristic().evaluate(&idea).await.unwrap();
        println!("\n[a4] confidence 区间校验");
        println!("  正方={:.2} (应∈[0.1, 0.95])   反方={:.2} (应∈[0.1, 0.9])", eval.positive.confidence, eval.negative.confidence);
        assert!(eval.positive.confidence >= 0.1 && eval.positive.confidence <= 0.95);
        assert!(eval.negative.confidence >= 0.1 && eval.negative.confidence <= 0.9);
    }

    #[tokio::test]
    async fn a5_positive_thesis_contains_title() {
        let idea = make_idea("独家创意", "描述内容", Priority::High, vec![]);
        let eval = AdversarialEngine::heuristic().evaluate(&idea).await.unwrap();
        println!("\n[a5] 正方论点含标题");
        println!("  thesis: {}", eval.positive.thesis);
        assert!(eval.positive.thesis.contains("独家创意"), "正方 thesis 应含标题");
    }

    #[tokio::test]
    async fn a6_negative_evidence_nonempty() {
        let idea = make_idea("待质疑想法", "短", Priority::Low, vec![]);
        let eval = AdversarialEngine::heuristic().evaluate(&idea).await.unwrap();
        println!("\n[a6] 反方证据非空 ({} 条)", eval.negative.evidence.len());
        for (i, e) in eval.negative.evidence.iter().enumerate() {
            println!("  证据{}: {}", i + 1, e);
        }
        assert!(!eval.negative.evidence.is_empty(), "反方 evidence 不应为空");
    }

    #[tokio::test]
    async fn a7_final_score_consistency() {
        let desc = "面向用户的核心功能".to_string();
        let idea = make_idea("一致性测试", &desc, Priority::High, vec!["核心"]);
        let scores = ScoringEngine::compute_default(&idea);
        let eval = AdversarialEngine::heuristic().evaluate(&idea).await.unwrap();
        println!("\n[a7] final_score == scores.overall 一致性");
        println!("  scores.overall={:.2}  eval.final_score={:.2}", scores.overall, eval.final_score);
        println!("  analyst.summary: {}", eval.analyst.summary);
        assert!((eval.final_score - scores.overall).abs() < 0.001, "final_score 应等于 overall");
        assert!(eval.analyst.summary.contains("一致性测试"), "summary 应含标题");
    }
}