package app

import (
	"context"
	"encoding/json"
	"fmt"
	"strings"
	"time"

	sharedllm "github.com/geo-platform/tenant-api/internal/shared/llm"
)

const (
	monitoringAnswerParseLLMModel           = "doubao-seed-2-0-lite-260215"
	monitoringAnswerParseLLMTimeout         = 30 * time.Second
	monitoringAnswerParseLLMMaxOutputTokens = 600
)

var monitoringAnswerParseLLMSchema = []byte(`{
  "type": "object",
  "additionalProperties": false,
  "required": [
    "brand_mentioned",
    "brand_mention_position",
    "first_recommended",
    "sentiment_label",
    "matched_brand_terms"
  ],
  "properties": {
    "brand_mentioned": {
      "type": "boolean"
    },
    "brand_mention_position": {
      "type": "string",
      "enum": ["top1", "mentioned", "not_mentioned"]
    },
    "first_recommended": {
      "type": "boolean"
    },
    "sentiment_label": {
      "type": "string",
      "enum": ["positive", "neutral", "negative", "unknown"]
    },
    "matched_brand_terms": {
      "type": "array",
      "items": {
        "type": "string"
      }
    }
  }
}`)

type monitoringAnswerLLMParsePayload struct {
	BrandMentioned       bool     `json:"brand_mentioned"`
	BrandMentionPosition string   `json:"brand_mention_position"`
	FirstRecommended     bool     `json:"first_recommended"`
	SentimentLabel       string   `json:"sentiment_label"`
	MatchedBrandTerms    []string `json:"matched_brand_terms"`
}

func parseMonitoringAnswerWithLLM(
	ctx context.Context,
	client sharedllm.Client,
	answer string,
	brandName string,
) (monitoringAnswerParseSummary, error) {
	answer = strings.TrimSpace(answer)
	brandName = strings.TrimSpace(brandName)
	if answer == "" {
		return monitoringAnswerParseSummary{}, fmt.Errorf("answer is empty")
	}
	if brandName == "" {
		return monitoringAnswerParseSummary{}, fmt.Errorf("brand name is empty")
	}

	result, err := client.Generate(ctx, sharedllm.GenerateRequest{
		Model:           monitoringAnswerParseLLMModel,
		Prompt:          buildMonitoringAnswerParsePrompt(answer, brandName),
		Timeout:         monitoringAnswerParseLLMTimeout,
		MaxOutputTokens: monitoringAnswerParseLLMMaxOutputTokens,
		ResponseFormat: &sharedllm.ResponseFormat{
			Type:        sharedllm.ResponseFormatTypeJSONSchema,
			Name:        "monitoring_answer_parse",
			Description: "Parse brand mention signals from an AI answer for brand monitoring.",
			SchemaJSON:  monitoringAnswerParseLLMSchema,
			Strict:      true,
		},
	}, nil)
	if err != nil {
		return monitoringAnswerParseSummary{}, err
	}

	return decodeMonitoringAnswerLLMParseResult(result.Content)
}

func buildMonitoringAnswerParsePrompt(answer string, brandName string) string {
	var builder strings.Builder
	builder.WriteString("你是品牌监测解析助手。\n")
	builder.WriteString("请只基于给定回答内容，判断目标品牌在回答中的提及、排序、推荐与情感，不要猜测回答外的信息。\n")
	builder.WriteString("\n判断规则：\n")
	builder.WriteString("1. brand_mentioned：回答中是否明确提到目标品牌，或可明确判断是在指代该品牌。\n")
	builder.WriteString("2. brand_mention_position：\n")
	builder.WriteString("   - top1：目标品牌被排在第一位，或被明确表述为首选/最推荐。\n")
	builder.WriteString("   - mentioned：目标品牌被提到，但不是第一位。\n")
	builder.WriteString("   - not_mentioned：未提到目标品牌。\n")
	builder.WriteString("3. first_recommended：是否明确把目标品牌作为首选推荐。\n")
	builder.WriteString("4. sentiment_label：结合品牌相关表述判断 positive / neutral / negative / unknown。\n")
	builder.WriteString("5. matched_brand_terms：把回答里实际出现、并用于指代该品牌的词语原样列出；没有就返回空数组。\n")
	builder.WriteString("\n只返回 JSON。\n")
	builder.WriteString("\n目标品牌：")
	builder.WriteString(brandName)
	builder.WriteString("\n回答内容：\n")
	builder.WriteString(answer)
	return builder.String()
}

func decodeMonitoringAnswerLLMParseResult(raw string) (monitoringAnswerParseSummary, error) {
	var lastErr error
	for _, candidate := range extractJSONCandidates(raw) {
		var payload monitoringAnswerLLMParsePayload
		if err := json.Unmarshal([]byte(candidate), &payload); err != nil {
			lastErr = err
			continue
		}
		return monitoringAnswerParseSummary{
			BrandMentioned:       payload.BrandMentioned,
			BrandMentionPosition: normalizeMonitoringBrandMentionPosition(payload.BrandMentionPosition, payload.BrandMentioned),
			FirstRecommended:     payload.FirstRecommended,
			SentimentLabel:       normalizeMonitoringSentiment(payload.SentimentLabel, payload.BrandMentioned),
			MatchedBrandTerms:    normalizeMonitoringBrandTermList(payload.MatchedBrandTerms),
		}, nil
	}
	if lastErr == nil {
		lastErr = fmt.Errorf("empty content")
	}
	return monitoringAnswerParseSummary{}, fmt.Errorf("decode monitoring answer parse result: %w", lastErr)
}

func normalizeMonitoringBrandMentionPosition(value string, brandMentioned bool) string {
	switch strings.ToLower(strings.TrimSpace(value)) {
	case "top1":
		if !brandMentioned {
			return "not_mentioned"
		}
		return "top1"
	case "mentioned":
		if !brandMentioned {
			return "not_mentioned"
		}
		return "mentioned"
	case "not_mentioned":
		return "not_mentioned"
	default:
		if brandMentioned {
			return "mentioned"
		}
		return "not_mentioned"
	}
}

func normalizeMonitoringSentiment(value string, brandMentioned bool) string {
	switch strings.ToLower(strings.TrimSpace(value)) {
	case "positive", "neutral", "negative":
		return strings.ToLower(strings.TrimSpace(value))
	case "unknown":
		return "unknown"
	default:
		if brandMentioned {
			return "neutral"
		}
		return "unknown"
	}
}