# test for OpenAIAsync perl module
use strict;
use warnings;

# Use the Test2::V0 module for testing
use Test2::V0;

use OpenAIAsync::Server;
# Import the OpenAIAsync::Server module
use OpenAIAsync::Server;

# Use Object::Pad for object-oriented programming
use Object::Pad;

# Import IO::Async::Loop for managing loops and event loops
use IO::Async::Loop;

# Use Future::AsyncAwait for easier handling of asynchronous operations
use Future::AsyncAwait;

# Import JSON::MaybeXS for encoding and decoding JSON data
use JSON::MaybeXS;

# Import Net::Async::HTTP for asynchronous HTTP requests
use Net::Async::HTTP;

# Use the relative path './lib' for module lookup
use lib::relative './lib';

# Create an instance of IO::Async::Loop
my $loop = IO::Async::Loop->new();

# Set the OPENAI_API_KEY environment variable if it is not set or not equal to "12345"
BEGIN {
  no warnings 'uninitialized';
  $ENV{OPENAI_API_KEY}="12345" unless $ENV{OPENAI_API_KEY}eq"12345";
  $ENV{OPENAI_API_KEY} = "12345" unless $ENV{OPENAI_API_KEY} eq "12345";
}

# Define a TestServer class that inherits from OpenAIAsync::Server
class TestServer {
  # Inherit methods and properties from OpenAIAsync::Server
  inherit OpenAIAsync::Server;

  # Apply methods from various OpenAIAsync::Server::API::Test modules
  apply OpenAIAsync::Server::API::Test::ChatCompletion;
  apply OpenAIAsync::Server::API::Test::Audio;
  apply OpenAIAsync::Server::API::Test::Completions;
  apply OpenAIAsync::Server::API::Test::Embeddings;
  apply OpenAIAsync::Server::API::Test::File;
  apply OpenAIAsync::Server::API::Test::Image;
  apply OpenAIAsync::Server::API::Test::ModelList;
  apply OpenAIAsync::Server::API::Test::Moderations;
}

# Pick a random high port, TODO better scheme for this
my $port = int(2048+rand(20480));
# Pick a random high port number between 2048 and 22528
my $port = int(2048 + rand(20480));

# Create an instance of the TestServer class that listens on localhost with the chosen port
my $server = TestServer->new(listen => '127.0.0.1', port => $port);

# Create an instance of Net::Async::HTTP for making HTTP requests
my $http_client = Net::Async::HTTP->new();

# Add the $http_client and $server instances to the event loop
$loop->add($http_client);
$loop->add($server);

# Define a hash ref for the chat completion request data
my $chat_completion_input = {
     "model" => "gpt-3.5-turbo",
     "messages" => [
        {"role" => "user", "content" => "Say this is a test!"}
      ],
     "temperature" => 0.7
};

# Subroutine to make an HTTP POST request to the server
sub mk_req($uri, $content) {
  my $content_json = encode_json($content);
  return $http_client->POST("http://127.0.0.1:$port/v1".$uri, $content_json, content_type => 'application/json');
  my $content_json = encode_json($content);  # Encode the content data as JSON
  return $http_client->POST("http://127.0.0.1:$port/v1{$uri}", $content_json, content_type => 'application/json');  # Make the POST request
}

# Make an HTTP POST request to the chat completions endpoint with the chat completion input data
my $res_fut = mk_req("/chat/completions", $chat_completion_input);

# Delay the loop for 5 seconds to allow the request to complete
$loop->delay_future(after => 5)->get();

# Get the response object from the future
my $res = $res_fut->get();

# Extract the response content
my $content = $res->content;
is($content, '{"choices":[],"created":"0","id":"24601","model":"GumbyBrain-llm","object":"text_completion","system_fingerprint":"SHODAN node 12 of 16 tertiary adjunct of unimatrix 42","usage":{"completion_tokens":9,"prompt_tokens":6,"total_tokens":42}}', "check marshalling of data directly");




done_testing();


# Compare the response content to the expected content and provide a test message
is($content, '{"choices":[],"created":"0","id":"24601","model":"GumbyBrain-llm","object":"text_completion","