package stream

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

	"aichat/llm"

	"github.com/volcengine/volcengine-go-sdk/service/arkruntime/model"
)

type ollamaChatRequest struct {
	Model    string              `json:"model"`
	Messages []ollamaChatMessage `json:"messages"`
	Stream   bool                `json:"stream"`
	Options  map[string]int      `json:"options,omitempty"`
}

type ollamaChatMessage struct {
	Role    string   `json:"role"`
	Content string   `json:"content"`
	Images  []string `json:"images,omitempty"`
}

type ollamaChatResponse struct {
	Message struct {
		Role     string `json:"role"`
		Content  string `json:"content"`
		Thinking string `json:"thinking"`
	} `json:"message"`
	Done            bool   `json:"done"`
	PromptEvalCount int    `json:"prompt_eval_count"`
	EvalCount       int    `json:"eval_count"`
	DoneReason      string `json:"done_reason"`
}

func StreamOllamaChat(ctx context.Context, profile *llm.Profile, messages []*model.ChatCompletionMessage, promptTokens int, usage *Tracker, emit EmitFunc, onDone func(string)) error {
	requestMessages, err := buildOllamaMessages(messages)
	if err != nil {
		return err
	}
	baseURL, err := llm.OllamaBaseURL(profile)
	if err != nil {
		return err
	}
	body, err := json.Marshal(ollamaChatRequest{
		Model:    profile.Config.Model,
		Messages: requestMessages,
		Stream:   true,
		Options:  map[string]int{"num_predict": 4096},
	})
	if err != nil {
		return err
	}
	req, err := http.NewRequestWithContext(ctx, http.MethodPost, strings.TrimRight(baseURL, "/")+"/api/chat", bytes.NewReader(body))
	if err != nil {
		return err
	}
	req.Header.Set("Content-Type", "application/json")
	resp, err := http.DefaultClient.Do(req)
	if err != nil {
		return err
	}
	defer resp.Body.Close()
	if resp.StatusCode < 200 || resp.StatusCode >= 300 {
		data, _ := io.ReadAll(io.LimitReader(resp.Body, 4096))
		return fmt.Errorf("Ollama 原生接口调用失败: %s %s", resp.Status, strings.TrimSpace(string(data)))
	}

	emit(Frame{Type: "trace", Tool: "model", Stage: "stream", Status: "running", Message: "Ollama 视觉模型已开始输出"})
	parseThinkTags := llm.ShouldParseThinkTags(profile)
	thinkParser := &Parser{}
	var full strings.Builder
	completionTokens := 0
	streamStarted := time.Now()
	peakTokensPerSecond := 0.0
	emitDelta := func(delta string) {
		if delta == "" {
			return
		}
		full.WriteString(delta)
		completionTokens += EstimateTokenCount(delta)
		usage.SetModel(promptTokens, completionTokens)
		currentSpeed := TokensPerSecond(completionTokens, streamStarted)
		if currentSpeed > peakTokensPerSecond {
			peakTokensPerSecond = currentSpeed
		}
		stats := usage.Snapshot(currentSpeed, peakTokensPerSecond)
		emit(Frame{Type: "delta", Text: delta, Stats: &stats})
	}
	emitContent := func(delta string) {
		if delta == "" {
			return
		}
		if !parseThinkTags {
			emitDelta(delta)
			return
		}
		visible, reasoning := thinkParser.Accept(delta)
		if reasoning != "" {
			emit(Frame{Type: "reasoning", Text: reasoning})
		}
		emitDelta(visible)
	}

	scanner := bufio.NewScanner(resp.Body)
	scanner.Buffer(make([]byte, 0, 64*1024), 10*1024*1024)
	for scanner.Scan() {
		line := strings.TrimSpace(scanner.Text())
		if line == "" {
			continue
		}
		var chunk ollamaChatResponse
		if err := json.Unmarshal([]byte(line), &chunk); err != nil {
			return fmt.Errorf("解析 Ollama 流失败: %w", err)
		}
		if chunk.Message.Thinking != "" {
			emit(Frame{Type: "reasoning", Text: chunk.Message.Thinking})
		}
		emitContent(chunk.Message.Content)
		if chunk.Done {
			if chunk.PromptEvalCount > 0 || chunk.EvalCount > 0 {
				usage.SetModel(chunk.PromptEvalCount, chunk.EvalCount)
			}
			break
		}
	}
	if err := scanner.Err(); err != nil {
		return err
	}
	if parseThinkTags {
		visible, reasoning := thinkParser.Flush()
		if reasoning != "" {
			emit(Frame{Type: "reasoning", Text: reasoning})
		}
		emitDelta(visible)
	}
	if onDone != nil {
		onDone(full.String())
	}
	finalStats := usage.Snapshot(TokensPerSecond(completionTokens, streamStarted), peakTokensPerSecond)
	emit(Frame{Type: "stats", Stats: &finalStats})
	emit(Frame{Type: "trace", Tool: "model", Stage: "stream", Status: "success", Message: "回答生成完成"})
	return nil
}

func buildOllamaMessages(messages []*model.ChatCompletionMessage) ([]ollamaChatMessage, error) {
	result := make([]ollamaChatMessage, 0, len(messages))
	for _, msg := range messages {
		if msg == nil {
			continue
		}
		role := string(msg.Role)
		if msg.Role == model.ChatMessageRoleTool {
			role = string(model.ChatMessageRoleUser)
		}
		item := ollamaChatMessage{Role: role}
		if msg.Content == nil {
			if len(msg.ToolCalls) > 0 {
				continue
			}
			result = append(result, item)
			continue
		}
		if msg.Content.StringValue != nil {
			item.Content = *msg.Content.StringValue
			if msg.Role == model.ChatMessageRoleTool {
				item.Content = "工具结果：\n" + item.Content
			}
			result = append(result, item)
			continue
		}
		for _, part := range msg.Content.ListValue {
			if part == nil {
				continue
			}
			switch part.Type {
			case model.ChatCompletionMessageContentPartTypeText:
				if part.Text != "" {
					if item.Content != "" {
						item.Content += "\n"
					}
					item.Content += part.Text
				}
			case model.ChatCompletionMessageContentPartTypeImageURL:
				if part.ImageURL == nil {
					continue
				}
				image, err := ollamaImagePayload(part.ImageURL.URL)
				if err != nil {
					return nil, err
				}
				item.Images = append(item.Images, image)
			}
		}
		result = append(result, item)
	}
	return result, nil
}

func ollamaImagePayload(raw string) (string, error) {
	raw = strings.TrimSpace(raw)
	if strings.HasPrefix(strings.ToLower(raw), "data:") {
		comma := strings.Index(raw, ",")
		if comma < 0 {
			return "", errors.New("图片 base64 数据格式错误")
		}
		return strings.TrimSpace(raw[comma+1:]), nil
	}
	return raw, nil
}