feat: wip on implementing gemini analyzer

src/StreamShorts.Library/Analysis/GeminiAnalyzer.cs

@@ -0,0 +1,88 @@
+using System.Text;
+using System.Text.Json;
+using System.Text.Json.Serialization;
+
+using NAudio.CoreAudioApi;
+
+using StreamShorts.Library.Transcription;
+
+namespace StreamShorts.Library.Analysis;
+
+public sealed class GeminiAnalyzer(
+  IHttpClientFactory httpClientFactory,
+  string apiKey
+) : ITranscriptAnalyzer
+{
+  private readonly IHttpClientFactory _httpClientFactory = httpClientFactory ?? throw new ArgumentNullException(nameof(httpClientFactory));
+  private readonly string _apiKey = apiKey ?? throw new ArgumentNullException(nameof(apiKey));
+
+  public async Task<TranscriptAnalysis> AnalyzeAsync(IEnumerable<TranscriptionSegment> segments)
+  {
+    using var client = _httpClientFactory.CreateClient();
+    client.Timeout = TimeSpan.FromMinutes(5);
+
+    // TODO: Load prompt from resource file
+    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).ConfigureAwait(false);
+    var responseContent = await response.Content.ReadAsStringAsync().ConfigureAwait(false);
+    var responseJson = JsonSerializer.Deserialize<LLMResponse>(responseContent);
+    var candidatesText = responseJson?
+      .Candidates?
+      .FirstOrDefault()?
+      .Content
+      .Parts?.FirstOrDefault()?
+      .Text;
+    
+    var clips = JsonSerializer.Deserialize<List<ShortClip>>(candidatesText ?? string.Empty);
+    return new TranscriptAnalysis(clips ?? []);
+  }
+}
+
+// TODO: Sort this shit out
+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
+);

src/StreamShorts.Library/Analysis/ITranscriptAnalyzer.cs

@@ -0,0 +1,8 @@
+using StreamShorts.Library.Transcription;
+
+namespace StreamShorts.Library.Analysis;
+
+public interface ITranscriptAnalyzer
+{
+  Task<TranscriptAnalysis> AnalyzeAsync(IEnumerable<TranscriptionSegment> segments);
+}

src/StreamShorts.Library/Analysis/ShortClip.cs

@@ -0,0 +1,16 @@
+using System.Text.Json.Serialization;
+
+namespace StreamShorts.Library.Analysis;
+
+public record ShortClip(
+  [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
+);

src/StreamShorts.Library/Analysis/TranscriptAnalysis.cs

@@ -0,0 +1,6 @@
+namespace StreamShorts.Library.Analysis;
+
+public class TranscriptAnalysis(IEnumerable<ShortClip> shortClips)
+{
+  public IEnumerable<ShortClip> ShortClips { get; init; } = shortClips;
+}

src/StreamShorts.Library/Resources/DefaultAnalysisPrompt.md

@@ -0,0 +1,28 @@
+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:
+
+{0}

src/StreamShorts.Library/StreamShorts.Library.csproj

@@ -1,6 +1,7 @@
 <Project Sdk="Microsoft.NET.Sdk">
   <ItemGroup>
     <PackageReference Include="FFMpegCore" />
+    <PackageReference Include="Microsoft.Extensions.Http" />
     <PackageReference Include="NAudio" />
     <PackageReference Include="Whisper.net.AllRuntimes" />
   </ItemGroup>