This is an HTTP service for natural language guessing of input texts. Run it as "perl lang_detection_server.pl" and open a URL like http://localhost:8080/?text=This+is+just+a+test+string . TextCat source code courtesy of Gertjan van Noord.

lang_detection_server.pl

# This is a sloppy HTTP server version of TextCat, an n-gram based natural language guesser
# written by Gertjan van Noord in 1997.
# More info: http://odur.let.rug.nl/~vannoord/TextCat/
#
# TextCat was distributed under the GNU Lesser General Public License.
#
# You need the language model files (LM folder from Gertjan's distribution) in a directory.
# Set the variable $opt_d to point to that directory.


package MyServer;
use base qw(HTTP::Server::Simple::CGI);
use strict;
use vars qw($opt_d $opt_f $opt_h $opt_i $opt_l $opt_n $opt_s $opt_t $opt_v $opt_u $opt_a);
my $non_word_characters='0-9\s';

$opt_a = 10;
$opt_d = '/path/to/textcat/LM';
$opt_f = 0;
$opt_t = 400;
$opt_u = 1.05;


# read language models once
opendir DIR, "$opt_d" or die "directory $opt_d: $!\n";
our @languages = sort(grep { s/\.lm// && -r "$opt_d/$_.lm" } readdir(DIR));
closedir DIR;
@languages or die "sorry, can't read any language models from $opt_d\n" .
    "language models must reside in files with .lm ending\n";



sub create_lm {
	my $ngram;
	($_,$ngram) = @_;
	my $word;
	foreach $word (split("[$non_word_characters]+")) {
		$word = "_" . $word . "_";
		my $len = length($word);
		my $flen=$len;
		my $i;
		for ($i=0;$i<$flen;$i++) {
			$$ngram{substr($word,$i,5)}++ if $len > 4;
			$$ngram{substr($word,$i,4)}++ if $len > 3;
			$$ngram{substr($word,$i,3)}++ if $len > 2;
			$$ngram{substr($word,$i,2)}++ if $len > 1;
			$$ngram{substr($word,$i,1)}++;
			$len--;
		}
	}
	map { my $key=$_; if ($$ngram{$key} <= $opt_f) 
		{ delete $$ngram{$key}; }; } keys %$ngram;
	my @sorted = sort { $$ngram{$b} <=> $$ngram{$a} } keys %$ngram;
	splice(@sorted,$opt_t) if (@sorted > $opt_t); 
	return @sorted;
}


sub classify {
	my ($input) = @_;
	my %results = ();
	my $maxp = $opt_t;

	my @unknown = create_lm($input);
	my $language;
	foreach $language (@languages) {
		# loads the language model into hash %$language.
		my %ngram=();
		my $rang=1;
		open(LM,"$opt_d/$language.lm") || die "cannot open $language.lm: $!\n";
		while (<LM>) {
			chomp;
			if (/^[^$non_word_characters]+/o) {
				$ngram{$&} = $rang++;
			}
		}
		close(LM);
		# compares the language model with input ngrams list
		my ($i,$p)=(0,0);
		while ($i < @unknown) {
			if ($ngram{$unknown[$i]}) {
				$p=$p+abs($ngram{$unknown[$i]}-$i);
			} else { 
				$p = $p + $maxp; 
			}
			++$i;
		}
		$results{$language} = $p;
	}
	my @results = sort { $results{$a} <=> $results{$b} } keys %results;
	#print join("\n",map { "$_\t $results{$_}"; } @results),"\n" if $opt_v;
	my $a = $results{$results[0]};
	my @answers=(shift(@results));
	while (@results && $results{$results[0]} < ($opt_u *$a)) {
		@answers = (@answers, shift(@results));
	}
	if (@answers > $opt_a) {
		return ('unknown');
	} else {
		return @answers;
	}
}

sub handle_request {
	my ($self, $cgi) = @_;
	print join(',', classify($cgi->param('text')));
}



MyServer->new->run();