634 lines
22 KiB
Go
634 lines
22 KiB
Go
// controllers/ai.go
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package controllers
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import (
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"context"
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"encoding/json"
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"errors"
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"fmt"
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"github.com/gin-gonic/gin"
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"github.com/sashabaranov/go-openai"
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"hr_receiver/config"
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"hr_receiver/models"
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"hr_receiver/util"
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"io"
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"io/ioutil"
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"log"
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"mime/multipart"
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"net/http"
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"os"
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"strconv"
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"strings"
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"time"
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"gorm.io/gorm"
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)
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const (
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analysisTypeHeartRateOnly = "heart_rate_only"
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analysisTypeHeartRateWithSteps = "heart_rate_with_steps"
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sourceUpload = "upload"
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sourceCloud = "cloud"
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sourceWechat = "wechat"
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)
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func readDocxContent(fileHeader *multipart.FileHeader) (string, error) {
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tempFile, err := os.CreateTemp("", "upload_*.docx")
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if err != nil {
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return "", fmt.Errorf("failed to create temporary file: %w", err)
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}
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defer os.Remove(tempFile.Name())
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defer tempFile.Close()
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src, err := fileHeader.Open()
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if err != nil {
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return "", fmt.Errorf("failed to open uploaded file: %w", err)
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}
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defer src.Close()
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_, err = io.Copy(tempFile, src)
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if err != nil {
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return "", fmt.Errorf("failed to copy file to temporary location: %w", err)
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}
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tempFilePath := tempFile.Name()
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str, err := util.DocxToStructuredPrompt(tempFilePath)
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if err != nil {
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return "", fmt.Errorf("failed to parse docx with go-docx: %w", err)
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}
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return str, nil
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}
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func readDocxContentFromPath(filePath string) (string, error) {
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str, err := util.DocxToStructuredPrompt(filePath)
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if err != nil {
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return "", fmt.Errorf("failed to parse docx with go-docx: %w", err)
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}
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return str, nil
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}
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func readCSVContent(fileHeader *multipart.FileHeader) (string, error) {
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tempFile, err := os.CreateTemp("", "upload_*.csv")
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if err != nil {
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return "", fmt.Errorf("failed to create temporary file: %w", err)
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}
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defer os.Remove(tempFile.Name())
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defer tempFile.Close()
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src, err := fileHeader.Open()
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if err != nil {
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return "", fmt.Errorf("failed to open uploaded file: %w", err)
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}
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defer src.Close()
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_, err = io.Copy(tempFile, src)
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if err != nil {
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return "", fmt.Errorf("failed to copy file to temporary location: %w", err)
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}
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content, err := ioutil.ReadFile(tempFile.Name())
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if err != nil {
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return "", fmt.Errorf("failed to read CSV content from temporary file: %w", err)
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}
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lines := strings.Split(string(content), "\n")
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var compressedLines []string
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for i, line := range lines {
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if i == 0 {
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compressedLines = append(compressedLines, line)
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continue
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}
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if strings.TrimSpace(line) == "" {
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continue
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}
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if (i-1)%4 == 0 {
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compressedLines = append(compressedLines, line)
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}
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}
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resultContent := strings.Join(compressedLines, "\n")
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return resultContent, nil
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}
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func buildAnalysisPrompt(teachingPlanContent, heartRateContent, analysisType, stepContent string) string {
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if analysisType == analysisTypeHeartRateWithSteps {
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return fmt.Sprintf(`请根据以下体育课堂的教案、心率监测数据和训练结束步数汇总，生成一份详细的课堂分析报告：
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## 教案内容：
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%s
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## 心率监测数据：
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%s
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## 训练结束步数汇总：
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%s
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这是一份幼儿园体育课的教案、课程心率监测数据和训练结束步数汇总。请结合三类信息分析课程教学效果、运动量和运动负荷情况是否科学，并提出课程设计的优化方案。
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分析要求：
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1. 步数只作为移动量、活动密度和参与度的辅助参考，不能替代心率负荷判断。
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2. 请判断步数与心率是否一致。例如高步数高心率通常说明移动量较大；低步数高心率则可能是力量、支撑、跳跃、对抗或其他无氧/原地高强度活动。
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3. 不要简单以步数高低判断运动量是否合理，必须结合教案内容、动作形式和心率变化综合判断。
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4. 在教学建议中明确说明本节课是否适合继续使用步数作为辅助分析指标。
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优化方案参考如下格式,教学过程需要详细一些：
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# 幼儿体育教案（华侨大学版本）
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| 项目 | 内容 |
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| ------------ | -------------------------------- |
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| **课程名** | |
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| **年段** | 小 中 大 |
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| **教师姓名** | |
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| **时间** | 年 月 日 |
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| **地点** | |
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| **人数** | 男： 女： |
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| **时长** | 分钟 |
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| **天气预报** | 晴 雨 阴；温度 ℃ |
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| **器材准备** | |
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## 教学目标
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| 类型 | 目标 |
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| -------- | ------------ |
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| **体能目标** | |
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| **技能目标** | |
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| **情感目标** | |
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## 教学过程
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| 阶段 | 阶段 | 项目名称 | 引导语及教学方法 | 队形/站位/留意点 | 目标心率区间 | 时间（分） |
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| ---------- | -------- | ----------------------------- | ------------------------ | --------------------- | ------------ | ---------- |
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| **准备部分** | 热身 | | | | | 3 |
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| | 注意力游戏 | | | | | 3 |
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| **正课部分** | 基本素质练习及常规意识培养环节 | | | | | 5 |
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| | 复习环节 | | | | | 5 |
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| | 新授环节 | | | | | 8 |
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| **结束部分** | 社会性及情感目标游戏 | | | | | 4 |
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| | 整理放松 | | | | | 2 |
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请以专业体育教师的视角，提供详细的数据分析和教学建议。请直接输出报告内容，不要包含"好的"、"收到"、"作为一名..."等任何开场白或客套话。`, teachingPlanContent, heartRateContent, stepContent)
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}
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return fmt.Sprintf(`请根据以下体育课堂的教案和心率监测数据，生成一份详细的课堂分析报告：
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## 教案内容：
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%s
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## 心率监测数据：
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%s
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这是一份幼儿园体育课的教案和课程心率监测数据，请帮对照分析课程教学效果，运动量和运动负荷情况是否科学，并提出课程设计的优化方案。
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优化方案参考如下格式,教学过程需要详细一些：
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# 幼儿体育教案（华侨大学版本）
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| 项目 | 内容 |
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| ------------ | -------------------------------- |
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| **课程名** | |
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| **年段** | 小 中 大 |
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| **教师姓名** | |
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| **时间** | 年 月 日 |
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| **地点** | |
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| **人数** | 男： 女： |
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| **时长** | 分钟 |
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| **天气预报** | 晴 雨 阴；温度 ℃ |
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| **器材准备** | |
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## 教学目标
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| 类型 | 目标 |
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| -------- | ------------ |
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| **体能目标** | |
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| **技能目标** | |
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| **情感目标** | |
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## 教学过程
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| 阶段 | 阶段 | 项目名称 | 引导语及教学方法 | 队形/站位/留意点 | 目标心率区间 | 时间（分） |
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| ---------- | -------- | ----------------------------- | ------------------------ | --------------------- | ------------ | ---------- |
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| **准备部分** | 热身 | | | | | 3 |
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| | 注意力游戏 | | | | | 3 |
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| **正课部分** | 基本素质练习及常规意识培养环节 | | | | | 5 |
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| | 复习环节 | | | | | 5 |
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| | 新授环节 | | | | | 8 |
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| **结束部分** | 社会性及情感目标游戏 | | | | | 4 |
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| | 整理放松 | | | | | 2 |
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请以专业体育教师的视角，提供详细的数据分析和教学建议。请直接输出报告内容，不要包含"好的"、"收到"、"作为一名..."等任何开场白或客套话。`, teachingPlanContent, heartRateContent)
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}
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type aiAnalysisResult struct {
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Content string
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InputTokens int
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OutputTokens int
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CacheHitTokens int
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CacheMissTokens int
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InputSizeBytes int
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OutputSizeBytes int
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}
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func callAIForAnalysis(prompt string) (*aiAnalysisResult, error) {
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sizeInBytes := len(prompt)
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sizeInKB := float64(sizeInBytes) / 1024.0
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log.Printf("=== 发送给 AI 的内容大小: %.2f KB (%d 字节) ===", sizeInKB, sizeInBytes)
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baseURL, apiKey, model, err := config.GetAIConfig()
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if err != nil {
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return nil, err
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}
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clientConfig := openai.DefaultConfig(apiKey)
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clientConfig.BaseURL = baseURL
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client := openai.NewClientWithConfig(clientConfig)
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resp, err := client.CreateChatCompletion(
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context.Background(),
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openai.ChatCompletionRequest{
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Model: model,
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Messages: []openai.ChatCompletionMessage{
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{
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Role: openai.ChatMessageRoleUser,
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Content: prompt,
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},
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},
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Temperature: 0.6,
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TopP: 0.6,
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MaxCompletionTokens: 4000,
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},
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)
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if err != nil {
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return nil, fmt.Errorf("API call failed: %w", err)
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}
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if len(resp.Choices) == 0 {
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return nil, fmt.Errorf("no choices returned from API")
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}
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content := resp.Choices[0].Message.Content
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cacheHitTokens := 0
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if resp.Usage.PromptTokensDetails != nil {
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cacheHitTokens = resp.Usage.PromptTokensDetails.CachedTokens
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}
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return &aiAnalysisResult{
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Content: content,
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InputTokens: resp.Usage.PromptTokens,
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OutputTokens: resp.Usage.CompletionTokens,
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CacheHitTokens: cacheHitTokens,
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CacheMissTokens: resp.Usage.PromptTokens - cacheHitTokens,
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InputSizeBytes: len(prompt),
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OutputSizeBytes: len(content),
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}, nil
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}
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// @Summary AI分析
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// @Description 上传心率CSV和教案文件，通过AI生成课堂分析报告，支持流式和非流式输出
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// @Tags AI分析
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// @Accept multipart/form-data
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// @Produce json
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// @Param heart_rate_data formData file true "心率数据CSV文件"
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// @Param step_data formData file false "步数数据CSV文件(analysis_type为heart_rate_with_steps时必填)"
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// @Param teaching_plan formData file false "教案DOCX文件(teaching_plan_source为upload/wechat时必填)"
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// @Param analysis_type formData string false "分析类型: heart_rate_only(默认) | heart_rate_with_steps"
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// @Param teaching_plan_source formData string false "教案来源: upload(默认) | cloud | wechat"
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// @Param regionid formData string false "区域ID"
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// @Param trainid formData string false "训练ID"
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// @Param lesson_plan_id formData string false "云端教案ID(teaching_plan_source=cloud时必填)"
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// @Param stream formData string false "是否流式输出: true | false"
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// @Success 200 {object} SwagAPIResponse "分析成功"
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// @Failure 400 {object} SwagAPIResponse "请求参数错误"
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// @Router /train-records/analysis-by-ai [post]
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func (tc *TrainingController) AnalyzeByAI(c *gin.Context) {
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form, err := c.MultipartForm()
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if err != nil {
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log.Printf("Error parsing multipart form: %v", err)
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c.JSON(http.StatusBadRequest, gin.H{"error": fmt.Sprintf("Failed to parse form: %v", err)})
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return
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}
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csvFiles := form.File["heart_rate_data"]
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stepFiles := form.File["step_data"]
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analysisType := c.PostForm("analysis_type")
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teachingPlanSource := c.PostForm("teaching_plan_source")
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regionIDStr := c.PostForm("regionid")
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trainID := c.PostForm("trainid")
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streamStr := c.PostForm("stream")
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useStream := streamStr == "true"
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if analysisType == "" {
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analysisType = analysisTypeHeartRateOnly
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}
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if teachingPlanSource == "" {
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teachingPlanSource = sourceUpload
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}
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if len(csvFiles) == 0 {
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c.JSON(http.StatusBadRequest, gin.H{"error": "Missing required file: heart_rate_data (.csv)"})
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return
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}
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if analysisType == analysisTypeHeartRateWithSteps && len(stepFiles) == 0 {
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c.JSON(http.StatusBadRequest, gin.H{"error": "Missing required file: step_data (.csv) for heart_rate_with_steps"})
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return
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}
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uploadTime := time.Now().UnixMilli()
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heartRateFileHeader := csvFiles[0]
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teachingPlanContent, teachingPlanSize, err := resolveTeachingPlanContent(c, form, teachingPlanSource)
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if err != nil {
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log.Printf("Error resolving teaching plan: %v", err)
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if errors.Is(err, gorm.ErrRecordNotFound) {
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c.JSON(http.StatusNotFound, gin.H{"error": "Cloud teaching plan file not found"})
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return
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}
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c.JSON(http.StatusBadRequest, gin.H{"error": err.Error()})
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return
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}
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heartRateContent, err := readCSVContent(heartRateFileHeader)
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if err != nil {
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log.Printf("Error reading heart rate file (%s): %v", heartRateFileHeader.Filename, err)
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c.JSON(http.StatusInternalServerError, gin.H{"error": fmt.Sprintf("Failed to process heart rate file: %v", err)})
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return
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}
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stepContent := ""
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var stepFileSize int64 = 0
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if analysisType == analysisTypeHeartRateWithSteps {
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stepFileHeader := stepFiles[0]
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stepFileSize = stepFileHeader.Size
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stepContent, err = readCSVContent(stepFileHeader)
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if err != nil {
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log.Printf("Error reading step file (%s): %v", stepFileHeader.Filename, err)
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c.JSON(http.StatusInternalServerError, gin.H{"error": fmt.Sprintf("Failed to process step file: %v", err)})
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return
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}
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}
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originalFileSize := heartRateFileHeader.Size + teachingPlanSize + stepFileSize
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compressedContentSize := int64(len(heartRateContent)) + int64(len(teachingPlanContent)) + int64(len(stepContent))
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prompt := buildAnalysisPrompt(teachingPlanContent, heartRateContent, analysisType, stepContent)
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startTime := time.Now()
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var regionID *uint32
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if regionIDStr != "" {
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if parsed, err := strconv.ParseUint(regionIDStr, 10, 32); err == nil {
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id := uint32(parsed)
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regionID = &id
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}
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}
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if useStream {
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tc.streamAIAnalysis(c, prompt, regionID, trainID, teachingPlanSource, analysisType,
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originalFileSize, compressedContentSize, uploadTime)
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return
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}
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analysisResult, err := callAIForAnalysis(prompt)
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if err != nil {
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log.Printf("Error calling AI for analysis: %v", err)
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c.JSON(http.StatusInternalServerError, gin.H{"error": fmt.Sprintf("AI analysis failed: %v", err)})
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return
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}
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durationMs := time.Since(startTime).Milliseconds()
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saveAnalysisRecord(analysisResult.Content, analysisResult.InputTokens, analysisResult.OutputTokens,
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analysisResult.CacheHitTokens, analysisResult.CacheMissTokens,
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analysisResult.InputSizeBytes, analysisResult.OutputSizeBytes,
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regionID, trainID, teachingPlanSource, analysisType,
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originalFileSize, compressedContentSize, uploadTime, durationMs)
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c.JSON(http.StatusOK, gin.H{
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"status": "success",
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"data": analysisResult.Content,
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})
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}
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type streamCollector struct {
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fullContent string
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inputTokens int
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outputTokens int
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cacheHitTokens int
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cacheMissTokens int
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}
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func newStreamCollector() *streamCollector {
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return &streamCollector{}
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}
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func (sc *streamCollector) add(delta string) {
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sc.fullContent += delta
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}
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func (sc *streamCollector) updateUsage(usage *openai.Usage) {
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sc.inputTokens = usage.PromptTokens
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sc.outputTokens = usage.CompletionTokens
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if usage.PromptTokensDetails != nil {
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sc.cacheHitTokens = usage.PromptTokensDetails.CachedTokens
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}
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sc.cacheMissTokens = sc.inputTokens - sc.cacheHitTokens
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}
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func (tc *TrainingController) streamAIAnalysis(c *gin.Context, prompt string,
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regionID *uint32, trainID, sourceType, analysisType string,
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originalFileSize, compressedContentSize int64, uploadTime int64) {
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sizeInBytes := len(prompt)
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sizeInKB := float64(sizeInBytes) / 1024.0
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log.Printf("=== 发送给流式 AI 的内容大小: %.2f KB (%d 字节) ===", sizeInKB, sizeInBytes)
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c.Writer.Header().Set("Content-Type", "text/event-stream")
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c.Writer.Header().Set("Cache-Control", "no-cache")
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c.Writer.Header().Set("Connection", "keep-alive")
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c.Writer.WriteHeader(http.StatusOK)
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|
||
flusher, ok := c.Writer.(http.Flusher)
|
||
if !ok {
|
||
log.Printf("streaming not supported")
|
||
c.JSON(http.StatusInternalServerError, gin.H{"error": "streaming not supported"})
|
||
return
|
||
}
|
||
|
||
baseURL, apiKey, model, err := config.GetAIConfig()
|
||
if err != nil {
|
||
sendSSEError(c, err.Error())
|
||
return
|
||
}
|
||
|
||
clientConfig := openai.DefaultConfig(apiKey)
|
||
clientConfig.BaseURL = baseURL
|
||
client := openai.NewClientWithConfig(clientConfig)
|
||
|
||
stream, err := client.CreateChatCompletionStream(
|
||
c.Request.Context(),
|
||
openai.ChatCompletionRequest{
|
||
Model: model,
|
||
Messages: []openai.ChatCompletionMessage{
|
||
{Role: openai.ChatMessageRoleUser, Content: prompt},
|
||
},
|
||
Temperature: 0.6,
|
||
TopP: 0.6,
|
||
MaxCompletionTokens: 4000,
|
||
Stream: true,
|
||
StreamOptions: &openai.StreamOptions{
|
||
IncludeUsage: true,
|
||
},
|
||
},
|
||
)
|
||
if err != nil {
|
||
sendSSEError(c, fmt.Sprintf("stream failed: %v", err))
|
||
return
|
||
}
|
||
defer stream.Close()
|
||
|
||
startTime := time.Now()
|
||
collector := newStreamCollector()
|
||
|
||
for {
|
||
response, recvErr := stream.Recv()
|
||
if recvErr != nil {
|
||
if recvErr == io.EOF {
|
||
break
|
||
}
|
||
sendSSEError(c, fmt.Sprintf("stream recv error: %v", recvErr))
|
||
return
|
||
}
|
||
if len(response.Choices) > 0 {
|
||
delta := response.Choices[0].Delta.Content
|
||
collector.add(delta)
|
||
sendSSEData(c, map[string]interface{}{"content": delta})
|
||
flusher.Flush()
|
||
}
|
||
if response.Usage != nil {
|
||
collector.updateUsage(response.Usage)
|
||
}
|
||
}
|
||
|
||
durationMs := time.Since(startTime).Milliseconds()
|
||
|
||
saveAnalysisRecord(collector.fullContent, collector.inputTokens, collector.outputTokens,
|
||
collector.cacheHitTokens, collector.cacheMissTokens,
|
||
len(prompt), len(collector.fullContent),
|
||
regionID, trainID, sourceType, analysisType,
|
||
originalFileSize, compressedContentSize, uploadTime, durationMs)
|
||
|
||
sendSSEData(c, map[string]interface{}{
|
||
"done": true,
|
||
"inputTokens": collector.inputTokens,
|
||
"outputTokens": collector.outputTokens,
|
||
"cacheHitTokens": collector.cacheHitTokens,
|
||
})
|
||
flusher.Flush()
|
||
}
|
||
|
||
func sendSSEData(c *gin.Context, data map[string]interface{}) {
|
||
b, _ := json.Marshal(data)
|
||
fmt.Fprintf(c.Writer, "data: %s\n\n", string(b))
|
||
}
|
||
|
||
func sendSSEError(c *gin.Context, msg string) {
|
||
b, _ := json.Marshal(map[string]string{"error": msg})
|
||
fmt.Fprintf(c.Writer, "data: %s\n\n", string(b))
|
||
if flusher, ok := c.Writer.(http.Flusher); ok {
|
||
flusher.Flush()
|
||
}
|
||
}
|
||
|
||
func saveAnalysisRecord(content string, inputTokens, outputTokens, cacheHitTokens, cacheMissTokens,
|
||
inputSizeBytes, outputSizeBytes int,
|
||
regionID *uint32, trainID, sourceType, analysisType string,
|
||
originalFileSize, compressedContentSize int64, uploadTime int64, durationMs int64) {
|
||
|
||
var pricing models.AIPricingConfig
|
||
var costJSON string
|
||
var totalCost float64
|
||
if err := config.DB.First(&pricing).Error; err == nil {
|
||
cacheMissPrice := pricing.CacheMissPricePerMillion
|
||
if cacheMissPrice == 0 {
|
||
cacheMissPrice = pricing.InputPricePerMillion
|
||
}
|
||
cacheHitPrice := pricing.CacheHitPricePerMillion
|
||
if cacheHitPrice == 0 {
|
||
cacheHitPrice = pricing.InputPricePerMillion
|
||
}
|
||
cacheHitCost := float64(cacheHitTokens) * cacheHitPrice / 1_000_000
|
||
cacheMissCost := float64(cacheMissTokens) * cacheMissPrice / 1_000_000
|
||
outputCost := float64(outputTokens) * pricing.OutputPricePerMillion / 1_000_000
|
||
totalCost = cacheHitCost + cacheMissCost + outputCost
|
||
|
||
costInfo := map[string]interface{}{
|
||
"pricingName": pricing.Name,
|
||
"provider": pricing.Provider,
|
||
"inputPricePerMillion": pricing.InputPricePerMillion,
|
||
"cacheHitPricePerMillion": cacheHitPrice,
|
||
"cacheMissPricePerMillion": cacheMissPrice,
|
||
"outputPricePerMillion": pricing.OutputPricePerMillion,
|
||
"cacheHitCost": cacheHitCost,
|
||
"cacheMissCost": cacheMissCost,
|
||
"outputCost": outputCost,
|
||
}
|
||
if b, err := json.Marshal(costInfo); err == nil {
|
||
costJSON = string(b)
|
||
}
|
||
}
|
||
|
||
record := models.AIAnalysisRecord{
|
||
RegionID: regionID,
|
||
TrainId: trainID,
|
||
SourceType: sourceType,
|
||
AnalysisType: analysisType,
|
||
AnalysisResult: content,
|
||
CostJSON: costJSON,
|
||
TotalCost: totalCost,
|
||
InputTokens: inputTokens,
|
||
OutputTokens: outputTokens,
|
||
CacheHitTokens: cacheHitTokens,
|
||
CacheMissTokens: cacheMissTokens,
|
||
InputSizeBytes: inputSizeBytes,
|
||
OutputSizeBytes: outputSizeBytes,
|
||
DurationMs: durationMs,
|
||
OriginalFileSize: originalFileSize,
|
||
CompressedContentSize: compressedContentSize,
|
||
UploadTime: uploadTime,
|
||
}
|
||
if err := config.DB.Create(&record).Error; err != nil {
|
||
log.Printf("Failed to save analysis record: %v", err)
|
||
}
|
||
}
|
||
|
||
func resolveTeachingPlanContent(c *gin.Context, form *multipart.Form, source string) (string, int64, error) {
|
||
switch strings.ToLower(strings.TrimSpace(source)) {
|
||
case sourceUpload:
|
||
docxFiles := form.File["teaching_plan"]
|
||
if len(docxFiles) == 0 {
|
||
return "", 0, fmt.Errorf("Missing required file: teaching_plan (.docx)")
|
||
}
|
||
content, err := readDocxContent(docxFiles[0])
|
||
return content, docxFiles[0].Size, err
|
||
case sourceWechat:
|
||
docxFiles := form.File["teaching_plan"]
|
||
if len(docxFiles) == 0 {
|
||
return "", 0, fmt.Errorf("Missing required file: teaching_plan (.docx)")
|
||
}
|
||
content, err := readDocxContent(docxFiles[0])
|
||
return content, docxFiles[0].Size, err
|
||
case sourceCloud:
|
||
lessonPlanID := c.PostForm("lesson_plan_id")
|
||
if strings.TrimSpace(lessonPlanID) == "" {
|
||
return "", 0, fmt.Errorf("missing required field: lesson_plan_id")
|
||
}
|
||
var fileRecord models.AppFile
|
||
if err := config.DB.Where("id = ? AND file_type = ?", lessonPlanID, models.AppFileTypeLessonPlan).First(&fileRecord).Error; err != nil {
|
||
return "", 0, err
|
||
}
|
||
content, err := readDocxContentFromPath(fileRecord.FilePath)
|
||
return content, fileRecord.FileSize, err
|
||
default:
|
||
return "", 0, fmt.Errorf("invalid teaching_plan_source, expected %s, %s or %s", sourceUpload, sourceWechat, sourceCloud)
|
||
}
|
||
}
|