Classify log items using Classificationbox by Machine Box - https://machinebox.io/

logclass.go

/*

	logclass

	1. Run Classificationbox (see https://machinebox.io/docs/classificationbox)
	2. Create a model and train it two classes with IDs: "noise" and "interesting"
	3. Execute this command and pipe logs through it

	Any logs that are not "noise" are passed through, others will be dimmed.

*/

func main() {
	if err := run(); err != nil {
		log.Fatalln(err)
	}
}

func run() error {
	var (
		cbAddr  = flag.String("cb", "http://localhost:8080", "Classificationbox address")
		modelID = flag.String("model", "", "ID of the model to use")
		in      = flag.String("in", "", "input file (default stdin)")
		out     = flag.String("out", "", "output file (default stdout)")
	)
	flag.Parse()
	if *modelID == "" {
		return errors.New("missing model ID")
	}
	cb := classificationbox.New(*cbAddr)
	ctx, cancel := context.WithCancel(context.Background())
	defer cancel()
	if err := boxutil.WaitForReady(ctx, cb); err != nil {
		return err
	}
	var r io.Reader
	if *in != "" {
		inFile, err := os.Open(*in)
		if err != nil {
			return errors.Wrap(err, "in")
		}
		defer inFile.Close()
		r = inFile
	} else {
		r = os.Stdin
	}
	var w io.Writer
	if *out != "" {
		outFile, err := os.Create(*out)
		if err != nil {
			return errors.Wrap(err, "out")
		}
		defer outFile.Close()
		w = outFile
	} else {
		w = os.Stdout
	}
	s := bufio.NewScanner(r)
	for s.Scan() {
		req := classificationbox.PredictRequest{
			Limit: 1, // we just need the top class
			Inputs: []classificationbox.Feature{
				classificationbox.FeatureText("logitem", s.Text()),
			},
		}
		ctxPredict, cancelPredict := context.WithTimeout(ctx, 250*time.Millisecond)
		resp, err := cb.Predict(ctxPredict, *modelID, req)
		if err != nil {
			cancelPredict()
			return err
		}
		cancelPredict()
		if len(resp.Classes) > 0 {
			if resp.Classes[0].ID != "noise" {
				io.WriteString(w, `\e[2m`) // dim text
			} else {
				io.WriteString(w, `\e[0m`) // normal text
			}
			if _, err := io.WriteString(w, s.Text()); err != nil {
				return err
			}
		}
	}
	if err := s.Err(); err != nil {
		return errors.Wrap(err, "scanner")
	}
	return nil
}