package llm

import (
	"bufio"
	"bytes"
	"context"
	"encoding/base64"
	"encoding/json"
	"fmt"
	"io"
	"net/http"
	"strings"
	"time"

	"git.yeij.top/AskaEth/Cyrene/ai-core/internal/model"
	"git.yeij.top/AskaEth/Cyrene/pkg/logger"
)

// OpenAIConfig OpenAI适配器配置
type OpenAIConfig struct {
	BaseURL        string        // API基础URL
	APIKey         string        // API密钥
	Model          string        // 主模型
	FallbackModel  string        // 备用模型（主模型不可用时）
	MaxRetries     int           // 最大重试次数
	Timeout        time.Duration // 请求超时
}

// OpenAIProvider OpenAI兼容的LLM提供商
type OpenAIProvider struct {
	config     OpenAIConfig
	httpClient *http.Client
}

// NewOpenAIProvider 创建OpenAI提供商
func NewOpenAIProvider(cfg OpenAIConfig) *OpenAIProvider {
	if cfg.MaxRetries == 0 {
		cfg.MaxRetries = 3
	}
	if cfg.Timeout == 0 {
		cfg.Timeout = 60 * time.Second
	}

	return &OpenAIProvider{
		config: cfg,
		httpClient: &http.Client{
			Timeout: cfg.Timeout,
		},
	}
}

// openAIRequest OpenAI请求结构
type openAIRequest struct {
	Model       string             `json:"model"`
	Messages    []openAIMessage    `json:"messages"`
	Temperature float64            `json:"temperature"`
	MaxTokens   int                `json:"max_tokens,omitempty"`
	Stream      bool               `json:"stream"`
	Tools       []OpenAITool       `json:"tools,omitempty"`
	ToolChoice  string             `json:"tool_choice,omitempty"` // "auto", "none", or specific tool
}

type openAIMessage struct {
	Role             string            `json:"role"`
	Content          interface{}       `json:"content,omitempty"` // string or []model.ImageContent for multimodal
	Name             string            `json:"name,omitempty"`
	ToolCalls        []openAIToolCall  `json:"tool_calls,omitempty"`
	ToolCallID       string            `json:"tool_call_id,omitempty"`
	ReasoningContent string            `json:"reasoning_content,omitempty"` // DeepSeek 思考链
}

// openAIToolCall OpenAI工具调用
type openAIToolCall struct {
	ID       string                  `json:"id"`
	Type     string                  `json:"type"`
	Function openAIToolCallFunction  `json:"function"`
}

type openAIToolCallFunction struct {
	Name      string `json:"name"`
	Arguments string `json:"arguments"` // JSON string
}

// openAIResponse OpenAI响应结构
type openAIResponse struct {
	ID      string           `json:"id"`
	Object  string           `json:"object"`
	Choices []openAIChoice   `json:"choices"`
	Usage   openAIUsage      `json:"usage,omitempty"`
	Error   *openAIError     `json:"error,omitempty"`
}

type openAIChoice struct {
	Index        int           `json:"index"`
	Message      openAIMessage `json:"message"`
	Delta        openAIMessage `json:"delta,omitempty"`
	FinishReason string        `json:"finish_reason"`
}

type openAIUsage struct {
	PromptTokens     int `json:"prompt_tokens"`
	CompletionTokens int `json:"completion_tokens"`
	TotalTokens      int `json:"total_tokens"`
}

type openAIError struct {
	Message string `json:"message"`
	Type    string `json:"type"`
	Code    string `json:"code,omitempty"`
}

// Chat 同步对话
func (p *OpenAIProvider) Chat(ctx context.Context, messages []model.LLMMessage) (*model.LLMResponse, error) {
	return p.ChatWithTools(ctx, messages, nil)
}

// ChatWithTools 同步对话（支持工具调用）
func (p *OpenAIProvider) ChatWithTools(ctx context.Context, messages []model.LLMMessage, tools []OpenAITool) (*model.LLMResponse, error) {
	resp, err := p.doChat(ctx, messages, p.config.Model, false, tools)
	if err != nil {
		// 尝试fallback模型
		if p.config.FallbackModel != "" && p.config.FallbackModel != p.config.Model {
			logger.Printf("[LLM] 主模型 %s 调用失败，降级到 %s: %v", p.config.Model, p.config.FallbackModel, err)
			return p.doChat(ctx, messages, p.config.FallbackModel, false, tools)
		}
		return nil, err
	}
	return resp, nil
}

// ChatStream 流式对话
func (p *OpenAIProvider) ChatStream(ctx context.Context, messages []model.LLMMessage) (<-chan StreamChunk, error) {
	return p.ChatStreamWithTools(ctx, messages, nil)
}

// ChatStreamWithTools 流式对话（支持工具调用）
func (p *OpenAIProvider) ChatStreamWithTools(ctx context.Context, messages []model.LLMMessage, tools []OpenAITool) (<-chan StreamChunk, error) {
	ch := make(chan StreamChunk, 100)

	go func() {
		defer close(ch)

		startTime := time.Now()
		modelName := p.config.Model
		var streamErr error
		var finalUsage *model.Usage

		defer func() {
			r := CallRecord{
				Model:    modelName,
				Duration: time.Since(startTime),
				Success:  streamErr == nil,
			}
			if streamErr != nil {
				r.Error = streamErr.Error()
			}
			if finalUsage != nil {
				r.PromptTokens = finalUsage.PromptTokens
				r.CompletionTokens = finalUsage.CompletionTokens
				r.TotalTokens = finalUsage.TotalTokens
			}
			LogCall(r)
		}()

		resp, err := p.doChatStream(ctx, messages, p.config.Model, tools)
		if err != nil {
			// Fallback
			if p.config.FallbackModel != "" {
				logger.Printf("[LLM] 流式调用主模型失败，降级: %v", err)
				modelName = p.config.FallbackModel
				resp, err = p.doChatStream(ctx, messages, p.config.FallbackModel, tools)
			}
			if err != nil {
				streamErr = err
				ch <- StreamChunk{Error: err, Done: true}
				return
			}
		}
		defer resp.Body.Close()

		scanner := bufio.NewScanner(resp.Body)
		// 增大scanner buffer以处理大块SSE数据
		scanner.Buffer(make([]byte, 0, 64*1024), 1024*1024)

		for scanner.Scan() {
			line := scanner.Text()

			// SSE格式: data: {...}
			if !strings.HasPrefix(line, "data: ") {
				continue
			}

			data := strings.TrimPrefix(line, "data: ")

			// 流结束标记
			if data == "[DONE]" {
				ch <- StreamChunk{Done: true}
				return
			}

			var streamResp openAIStreamResponse
			if err := json.Unmarshal([]byte(data), &streamResp); err != nil {
				continue
			}

			if len(streamResp.Choices) > 0 {
				delta := streamResp.Choices[0].Delta
				if deltaStr := contentString(delta.Content); deltaStr != "" {
					ch <- StreamChunk{Content: deltaStr}
				}
				if streamResp.Choices[0].FinishReason != "" {
					if streamResp.Usage != nil {
						finalUsage = &model.Usage{
							PromptTokens:     streamResp.Usage.PromptTokens,
							CompletionTokens: streamResp.Usage.CompletionTokens,
							TotalTokens:      streamResp.Usage.TotalTokens,
						}
					}
					ch <- StreamChunk{Done: true, Usage: finalUsage}
					return
				}
			}
		}

		if err := scanner.Err(); err != nil {
			streamErr = fmt.Errorf("读取流式响应失败: %w", err)
			ch <- StreamChunk{Error: streamErr, Done: true}
			return
		}

		ch <- StreamChunk{Done: true}
	}()

	return ch, nil
}

// openAIStreamResponse 流式响应结构
type openAIStreamResponse struct {
	ID      string                `json:"id"`
	Object  string                `json:"object"`
	Choices []openAIStreamChoice  `json:"choices"`
	Usage   *openAIUsage          `json:"usage,omitempty"`
}

type openAIStreamChoice struct {
	Index        int            `json:"index"`
	Delta        openAIMessage  `json:"delta"`
	FinishReason string         `json:"finish_reason"`
}

// doChat 执行同步对话请求
func (p *OpenAIProvider) doChat(ctx context.Context, messages []model.LLMMessage, modelName string, stream bool, tools []OpenAITool) (llmResp *model.LLMResponse, err error) {
	startTime := time.Now()
	defer func() {
		r := CallRecord{
			Model:    modelName,
			Duration: time.Since(startTime),
			Success:  err == nil,
		}
		if err != nil {
			r.Error = err.Error()
		}
		if llmResp != nil {
			r.PromptTokens = llmResp.Usage.PromptTokens
			r.CompletionTokens = llmResp.Usage.CompletionTokens
			r.TotalTokens = llmResp.Usage.TotalTokens
		}
		LogCall(r)
	}()

	// 转换消息格式（先解析图片 URL 为 data URL）
	oaiMessages := make([]openAIMessage, len(messages))
	for i, msg := range messages {
		resolvedImages := p.resolveImages(msg.Images)
		oaiMsg := openAIMessage{
			Role:             string(msg.Role),
			Content:          buildContent(msg.Content, resolvedImages),
			Name:             msg.Name,
			ToolCallID:       msg.ToolCallID,
			ReasoningContent: msg.ReasoningContent,
		}
		// 转换工具调用
		if len(msg.ToolCalls) > 0 {
			oaiMsg.ToolCalls = make([]openAIToolCall, len(msg.ToolCalls))
			for j, tc := range msg.ToolCalls {
				oaiMsg.ToolCalls[j] = openAIToolCall{
					ID:   tc.ID,
					Type: "function",
					Function: openAIToolCallFunction{
						Name:      tc.Name,
						Arguments: tc.Arguments,
					},
				}
			}
		}
		oaiMessages[i] = oaiMsg
	}

	reqBody := openAIRequest{
		Model:       modelName,
		Messages:    oaiMessages,
		Temperature: 0.8,
		Stream:      stream,
		Tools:       tools,
	}
	if len(tools) > 0 {
		reqBody.ToolChoice = "auto"
	}

	jsonBody, err := json.Marshal(reqBody)
	if err != nil {
		return nil, fmt.Errorf("序列化请求失败: %w", err)
	}

	req, err := http.NewRequestWithContext(ctx, "POST", p.config.BaseURL+"/chat/completions", bytes.NewReader(jsonBody))
	if err != nil {
		return nil, fmt.Errorf("创建请求失败: %w", err)
	}

	req.Header.Set("Content-Type", "application/json")
	req.Header.Set("Authorization", "Bearer "+p.config.APIKey)

	resp, err := p.httpClient.Do(req)
	if err != nil {
		return nil, fmt.Errorf("请求失败: %w", err)
	}
	defer resp.Body.Close()

	body, err := io.ReadAll(resp.Body)
	if err != nil {
		return nil, fmt.Errorf("读取响应失败: %w", err)
	}

	if resp.StatusCode != http.StatusOK {
		var errResp openAIResponse
		if json.Unmarshal(body, &errResp) == nil && errResp.Error != nil {
			return nil, fmt.Errorf("API错误 [%s]: %s", errResp.Error.Code, errResp.Error.Message)
		}
		return nil, fmt.Errorf("API返回状态码 %d: %s", resp.StatusCode, string(body))
	}

	var oaiResp openAIResponse
	if err := json.Unmarshal(body, &oaiResp); err != nil {
		return nil, fmt.Errorf("解析响应失败: %w", err)
	}

	if len(oaiResp.Choices) == 0 {
		return nil, fmt.Errorf("API返回空choices")
	}

	// 检查是否有工具调用
	choice := oaiResp.Choices[0]
	llmResp = &model.LLMResponse{
		Content:          contentString(choice.Message.Content),
		FinishReason:     choice.FinishReason,
		ReasoningContent: choice.Message.ReasoningContent,
		Usage: model.Usage{
			PromptTokens:     oaiResp.Usage.PromptTokens,
			CompletionTokens: oaiResp.Usage.CompletionTokens,
			TotalTokens:      oaiResp.Usage.TotalTokens,
		},
	}

	if len(choice.Message.ToolCalls) > 0 {
		llmResp.ToolCalls = make([]model.ToolCall, 0, len(choice.Message.ToolCalls))
		for _, tc := range choice.Message.ToolCalls {
			llmResp.ToolCalls = append(llmResp.ToolCalls, model.ToolCall{
				ID:        tc.ID,
				Name:      tc.Function.Name,
				Arguments: tc.Function.Arguments,
			})
		}
	}

	return llmResp, nil
}

// doChatStream 执行流式对话请求（返回原始HTTP响应）
func (p *OpenAIProvider) doChatStream(ctx context.Context, messages []model.LLMMessage, modelName string, tools []OpenAITool) (*http.Response, error) {
	oaiMessages := make([]openAIMessage, len(messages))
	for i, msg := range messages {
		resolvedImages := p.resolveImages(msg.Images)
		oaiMsg := openAIMessage{
			Role:             string(msg.Role),
			Content:          buildContent(msg.Content, resolvedImages),
			Name:             msg.Name,
			ToolCallID:       msg.ToolCallID,
			ReasoningContent: msg.ReasoningContent,
		}
		if len(msg.ToolCalls) > 0 {
			oaiMsg.ToolCalls = make([]openAIToolCall, len(msg.ToolCalls))
			for j, tc := range msg.ToolCalls {
				oaiMsg.ToolCalls[j] = openAIToolCall{
					ID:   tc.ID,
					Type: "function",
					Function: openAIToolCallFunction{
						Name:      tc.Name,
						Arguments: tc.Arguments,
					},
				}
			}
		}
		oaiMessages[i] = oaiMsg
	}

	reqBody := openAIRequest{
		Model:       modelName,
		Messages:    oaiMessages,
		Temperature: 0.8,
		Stream:      true,
		Tools:       tools,
	}
	if len(tools) > 0 {
		reqBody.ToolChoice = "auto"
	}

	jsonBody, err := json.Marshal(reqBody)
	if err != nil {
		return nil, fmt.Errorf("序列化请求失败: %w", err)
	}

	req, err := http.NewRequestWithContext(ctx, "POST", p.config.BaseURL+"/chat/completions", bytes.NewReader(jsonBody))
	if err != nil {
		return nil, fmt.Errorf("创建请求失败: %w", err)
	}

	req.Header.Set("Content-Type", "application/json")
	req.Header.Set("Authorization", "Bearer "+p.config.APIKey)
	req.Header.Set("Accept", "text/event-stream")

	resp, err := p.httpClient.Do(req)
	if err != nil {
		return nil, fmt.Errorf("请求失败: %w", err)
	}

	if resp.StatusCode != http.StatusOK {
		defer resp.Body.Close()
		body, _ := io.ReadAll(resp.Body)
		return nil, fmt.Errorf("API返回状态码 %d: %s", resp.StatusCode, string(body))
	}

	return resp, nil
}

// ModelName 返回模型名称
func (p *OpenAIProvider) ModelName() string {
	return p.config.Model
}

// contentString extracts a string from an interface{} Content value.
func contentString(v interface{}) string {
	if v == nil {
		return ""
	}
	if s, ok := v.(string); ok {
		return s
	}
	return ""
}

// resolveImages converts non-data URLs to base64 data URLs so external LLM APIs can access them.
func (p *OpenAIProvider) resolveImages(images []string) []string {
	if len(images) == 0 {
		return images
	}
	resolved := make([]string, 0, len(images))
	for _, img := range images {
		if strings.HasPrefix(img, "data:") {
			resolved = append(resolved, img)
			continue
		}
		dataURL, err := p.downloadAsDataURL(img)
		if err != nil {
			logger.Printf("[openai] 图片下载失败, 保留原始 URL: %s, err=%v", img, err)
			resolved = append(resolved, img) // 保留原始 URL 作为 fallback
			continue
		}
		resolved = append(resolved, dataURL)
	}
	return resolved
}

// downloadAsDataURL downloads an image from a URL and returns it as a base64 data URL.
func (p *OpenAIProvider) downloadAsDataURL(url string) (string, error) {
	ctx, cancel := context.WithTimeout(context.Background(), 30*time.Second)
	defer cancel()

	req, err := http.NewRequestWithContext(ctx, "GET", url, nil)
	if err != nil {
		return "", fmt.Errorf("创建请求失败: %w", err)
	}

	resp, err := p.httpClient.Do(req)
	if err != nil {
		return "", fmt.Errorf("下载失败: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode != http.StatusOK {
		return "", fmt.Errorf("HTTP %d", resp.StatusCode)
	}

	// 限制最大 20MB
	const maxSize = 20 * 1024 * 1024
	body, err := io.ReadAll(io.LimitReader(resp.Body, maxSize+1))
	if err != nil {
		return "", fmt.Errorf("读取失败: %w", err)
	}
	if len(body) > maxSize {
		return "", fmt.Errorf("图片过大: %d bytes", len(body))
	}

	mimeType := resp.Header.Get("Content-Type")
	if mimeType == "" {
		mimeType = http.DetectContentType(body)
	}

	b64 := base64.StdEncoding.EncodeToString(body)
	return fmt.Sprintf("data:%s;base64,%s", mimeType, b64), nil
}

// buildContent converts text + optional images to API content format.
// Returns a plain string if no images, or a multimodal array otherwise.
func buildContent(text string, images []string) interface{} {
	if len(images) == 0 {
		return text
	}
	parts := make([]model.ImageContent, 0, len(images)+1)
	if text != "" {
		parts = append(parts, model.ImageContent{
			Type: "text",
			Text: text,
		})
	}
	for _, img := range images {
		parts = append(parts, model.ImageContent{
			Type: "image_url",
			ImageURL: &model.ImageURL{
				URL: img,
			},
		})
	}
	return parts
}