stream-shorts/src/StreamShorts.Console/Program.cs

using System.Globalization;
using System.Resources;
using System.Text;
using System.Text.Json;
using System.Text.Json.Serialization;

using FFMpegCore;
using FFMpegCore.Enums;
using FFMpegCore.Pipes;

using NAudio.Wave;


using Whisper.net;
using Whisper.net.Ggml;

Log.Logger = new LoggerConfiguration()
  .WriteTo.File(
    formatter: new CompactJsonFormatter(),
    path: Path.Combine(AppContext.BaseDirectory, "logs", "log.jsonl"),
    rollingInterval: RollingInterval.Day
  )
  .Enrich.FromLogContext()
  .MinimumLevel.Verbose()
  .MinimumLevel.Override("Microsoft", LogEventLevel.Fatal)
  .CreateLogger();

try
{
  var appName = Assembly.GetExecutingAssembly().GetName().Name;
  Log.Information("Starting {AppName}", appName);

  await Host.CreateDefaultBuilder(args)
    .ConfigureLogging(static l => l.ClearProviders())
    .ConfigureServices(static (_, services) =>
    {
      services.AddSingleton(AnsiConsole.Console);
      services.AddSingleton<IFileSystem, FileSystem>();
      services.AddSingleton<IAudioExtractor, AudioExtractor>();
      services.AddSingleton<IAudioConverter, AudioConverter>();
    })
    .BuildApp()
    .RunAsync(args);

  Log.Information("{AppName} has completed successfully.", appName);
}
catch (Exception ex)
{
  Log.Fatal(ex, "An unhandled exception occurred during execution.");
  throw;
}
finally
{
  await Log.CloseAndFlushAsync();
}

// // Step 3: Split the wave stream into 2 minute segments
// waveStream.Position = 0;
// var segmentDuration = TimeSpan.FromMinutes(2);
// var segments = new List<MemoryStream>();
// using var waveReader = new WaveFileReader(waveStream);
// var segmentCount = (int)Math.Ceiling(waveReader.TotalTime.TotalMilliseconds / segmentDuration.TotalMilliseconds);

// Directory.CreateDirectory("segments");

// foreach (var i in Enumerable.Range(0, segmentCount))
// {
//   waveStream.Position = 0;
//   using var segmentWaveReader = new WaveFileReader(waveStream);
//   var segment = segmentWaveReader.ToSampleProvider()
//     .Skip(i * segmentDuration)
//     .Take(segmentDuration);
//   var segmentProvider = segment.ToWaveProvider16();
//   var segmentStream = new MemoryStream();
//   WaveFileWriter.WriteWavFileToStream(segmentStream, segmentProvider);
//   segmentStream.Position = 0;
//   segments.Add(segmentStream);
// }

// // Step 4: Transcribe each segment using Whisper
// using var modelMemoryStream = new MemoryStream();
// var model = await WhisperGgmlDownloader.Default.GetGgmlModelAsync(GgmlType.TinyEn);
// await model.CopyToAsync(modelMemoryStream);
// var whisperFactory = WhisperFactory.FromBuffer(modelMemoryStream.ToArray());
// using var whisperProcessor = whisperFactory.CreateBuilder()
//   .WithLanguage("en")
//   .Build();

// var completeTranscription = new StringBuilder();

// foreach (var (i, segment) in segments.Select((s, index) => (index, s)))
// {
//   var durationOffset = TimeSpan.FromMilliseconds(i * segmentDuration.TotalMilliseconds);
//   var segmentTranscription = new StringBuilder();

//   await foreach (var result in whisperProcessor.ProcessAsync(segment, CancellationToken.None))
//   {
//     var startTime = result.Start + durationOffset;
//     var endTime = result.End + durationOffset;
//     segmentTranscription.AppendLine(CultureInfo.CurrentCulture, $"[{startTime:hh\\:mm\\:ss} - {endTime:hh\\:mm\\:ss}] {result.Text}");
//   }

//   completeTranscription.Append(segmentTranscription);
// }

// // Step 5: Send the transcription to LLM for analysis
// // TODO: Explore this prompt further...seems break
// // when transcription is long
// var prompt = $$"""
// I need your help to transform my YouTube live stream transcript into engaging YouTube Shorts. Act as my content editor and pinpoint **all potential candidate segments** that are perfect for short-form video. I'm looking for clips that are:

//   * **Funny:** Moments that will make viewers laugh.
//   * **Informative:** Sections packed with valuable information or tips.
//   * **Insightful:** Portions offering unique perspectives or 'aha\!' moments.

// For each suggested short, please provide:

//   * The **start time** of the initial segment and the **end time** of the final segment. The duration of each short should be no longer than 3 minutes, but **aim for durations between 15 seconds and 60 seconds**. However, the short **must be as long as necessary to capture the complete thought or idea**, even if it means exceeding the target range or extending slightly to capture all necessary dialogue.
//   * A concise **title** that grabs attention.
//   * A brief **description** highlighting the short's content and its appeal.
//   * An **explanation** of why this particular segment is suitable for a YouTube Short, focusing on its potential for discoverability and engagement.

// Please format your response as a JSON array of objects with the following structure:

// ```json
// {
//   ""title"": ""string"",
//   "start_time": "string",
//   "end_time": "string",
//   "description": "string",
//   "explanation": "string"
// }
// ```

// Here is the transcript of my YouTube live stream:

// {{completeTranscription}}
// """;

// if (string.IsNullOrWhiteSpace(apiKey))
// {
//   throw new InvalidOperationException("GeminiApiKey is not configured in appsettings.json.");
// }

// using var client = new HttpClient()
// {
//   Timeout = TimeSpan.FromMinutes(30)
// };

// var requestUrl = $"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent?key={apiKey}";
// using var request = new HttpRequestMessage(HttpMethod.Post, requestUrl)
// {
//   Content = new StringContent(
//     JsonSerializer.Serialize(new
//     {
//       contents = new[]
//       {
//         new
//         {
//           role = "user",
//           parts = new[]
//           {
//             new
//             {
//               text = prompt
//             }
//           },
//         }
//       },
//       generationConfig = new
//       {
//         responseMimeType = "application/json",
//       }
//     }),
//     Encoding.UTF8,
//     "application/json"
//   )
// };
// var response = await client.SendAsync(request);
// var responseContent = await response.Content.ReadAsStringAsync();
// var responseJson = JsonSerializer.Deserialize<LLMResponse>(responseContent);
// var candidatesText = responseJson?
//   .Candidates?
//   .FirstOrDefault()?
//   .Content
//   .Parts?.FirstOrDefault()?
//   .Text;
// var analysis = JsonSerializer.Deserialize<List<LLMAnalysis>>(candidatesText ?? string.Empty);

// if (analysis is null)
// {
//   Console.WriteLine(resourceManager.GetString("LLMAnalysisFailed", CultureInfo.CurrentCulture));
//   return;
// }

// foreach (var result in analysis)
// {
//   var fileName = string.Concat(result.Title.Split(Path.GetInvalidFileNameChars()));
//   await FFMpeg.SubVideoAsync(
//     args[0],
//     $"{fileName}.mp4",
//     result.StartTime,
//     result.EndTime
//   );
// }

// // Step 6: Use analysis to generate a short video

// record LLMAnalysis(
//   [property: JsonPropertyName("title")]
//   string Title,
//   [property: JsonPropertyName("description")]
//   string Description,
//   [property: JsonPropertyName("explanation")]
//   string Explanation,
//   [property: JsonPropertyName("start_time")]
//   TimeSpan StartTime,
//   [property: JsonPropertyName("end_time")]
//   TimeSpan EndTime
// );

// record LLMResponse(
//   [property: JsonPropertyName("candidates")]
//   Candidate[] Candidates
// );

// record Candidate(
//   [property: JsonPropertyName("content")]
//   Content Content
// );

// record Content(
//   [property: JsonPropertyName("parts")]
//   Part[] Parts
// );

// record Part(
//   [property: JsonPropertyName("text")]
//   string Text
// );