Commands: Fix config options report in config/cli_flags_report.go

Signed-off-by: Michael Mayer <michael@photoprism.app>

internal/config/cli_flags_report.go

@@ -1,7 +1,5 @@
 package config
 
-import "strings"
-
 // Report returns global config values as a table for reporting.
 func (f CliFlags) Report() (rows [][]string, cols []string) {
 	cols = []string{"Environment", "CLI Flag", "Default", "Description"}
@@ -13,7 +11,7 @@ func (f CliFlags) Report() (rows [][]string, cols []string) {
 			continue
 		}
 
-		rows = append(rows, []string{strings.ReplaceAll(flag.EnvVar(), ",", ", "), flag.CommandFlag(), flag.Default(), flag.Usage()})
+		rows = append(rows, []string{flag.EnvVar(), flag.CommandFlag(), flag.Default(), flag.Usage()})
 	}
 
 	return rows, cols

internal/config/report_sections.go

@@ -7,6 +7,7 @@ type ReportSection struct {
 	Info  string
 }
 
+// see https://docs.photoprism.app/getting-started/config-options/#face-recognition
 var faceFlagsInfo = `!!! info ""
     To [recognize faces](https://docs.photoprism.app/user-guide/organize/people/), PhotoPrism first extracts crops from your images using a [library](https://github.com/esimov/pigo) based on [pixel intensity comparisons](https://dl.photoprism.app/pdf/publications/20140820-Pixel_Intensity_Comparisons.pdf). These are then fed into TensorFlow to compute [512-dimensional vectors](https://dl.photoprism.app/pdf/publications/20150101-FaceNet.pdf) for characterization. In the final step, the [DBSCAN algorithm](https://en.wikipedia.org/wiki/DBSCAN) attempts to cluster these so-called face embeddings, so they can be matched to persons with just a few clicks. A reasonable range for the similarity distance between face embeddings is between 0.60 and 0.70, with a higher value being more aggressive and leading to larger clusters with more false positives. To cluster a smaller number of faces, you can reduce the core to 3 or 2 similar faces.