# YouTube Video Summarizer

A .NET 8 console application that fetches YouTube video transcripts and produces structured summaries using an LLM (Ollama or OpenAI).

---

## Prerequisites

- [.NET 8 SDK](https://dotnet.microsoft.com/download)
- A **YouTube Data API v3** key → [Google Cloud Console](https://console.cloud.google.com)
- **Local Ollama** (Recommended) or an **OpenAI API key**.

---

## Setup

```bash
# 1. Clone / copy the project
cd YoutubeSummarizer

# 2. Copy the example config and fill in your keys
cp appsettings.example.json appsettings.json
nano appsettings.json   # or your editor of choice

# 3. Restore packages
dotnet restore

# 4. Run
dotnet run
```

---

## Google Cloud Setup (YouTube API Key)

1. Go to [console.cloud.google.com](https://console.cloud.google.com)
2. Create or select a project
3. **APIs & Services → Library** → search "YouTube Data API v3" → Enable
4. **APIs & Services → Credentials → Create Credentials → API key**
5. (Optional but recommended) Restrict the key to only the YouTube Data API v3

> Free quota: **10,000 units/day**. Each video lookup costs ~3 units. You can summarize thousands of videos before hitting the limit.

---

## Configuration Reference

| Key | Description | Default |
|---|---|---|
| `YouTube:ApiKey` | Your YouTube Data API v3 key | *(required)* |
| `LLM:BaseUrl` | API endpoint | `http://localhost:11434/v1` |
| `LLM:ApiKey` | API key (any for Ollama) | `ollama` |
| `LLM:Model` | Chat model to use | `qwen3:14b` |
| `LLM:MaxTokens` | Max tokens in summary response | `1500` |
| `LLM:TimeoutSeconds` | Max time for LLM generation | `300` |
| `Summarizer:ChunkWordLimit` | Words per chunk for long videos | `3000` |
| `Summarizer:ShowTranscript` | Print raw transcript before summary | `false` |

---

## Architecture

```
Program.cs
│  Main loop → parses URL → calls pipeline
│
├── YouTubeService
│     ├── ExtractVideoId()      — URL parsing
│     ├── GetVideoMetadataAsync() — YouTube Data API v3 (Videos.list)
│     └── GetTranscriptAsync()   — Caption list + timedtext download
│
├── SummarizerService
│     ├── SummarizeAsync()      — Routes to single-pass or chunked
│     ├── SinglePassSummarize() — One OpenAI call for short videos
│     └── ChunkedSummarize()    — Map-reduce for long videos
│
└── ConsoleRenderer             — All terminal output / formatting
```

### Caption Quality Transparency

The app tracks how the transcript was obtained and flags it accordingly:

| Source | Label | Warning shown? |
|---|---|---|
| Owner-published captions | `✓ Owner-published` | No |
| Community-contributed | `✓ Community captions` | Minor note |
| Auto-generated (ASR) | `~ Auto-generated` | Yes — accuracy caveat |
| No captions (metadata only) | `✗ Metadata only` | Yes — limited accuracy |

### Long Video Strategy

Videos with transcripts exceeding `ChunkWordLimit` words use a **map-reduce** approach:

1. **Split** — transcript divided into overlapping chunks (200-word overlap preserves context at boundaries)
2. **Map** — each chunk summarized independently
3. **Reduce** — chunk summaries combined into a final coherent summary

This handles hour-long lectures, conference talks, and podcasts without hitting model context limits.

---

## Environment Variable Overrides

You can override `appsettings.json` values with environment variables, useful for CI or Docker:

```bash
export YouTube__ApiKey="your-key"
export LLM__ApiKey="ollama"
dotnet run
```

Note the double-underscore `__` as the section separator (standard .NET configuration convention).