# 🚀 Quick Start Guide

## Installation & Launch (3 steps)

1. **Install dependencies:**
   ```bash
   pip install -r requirements.txt
   ```

2. **Launch the app:**
   ```bash
   python launch.py
   ```

3. **Open your browser** to http://localhost:7860

## Alternative Launch Methods

If the above doesn't work, try these:

```bash
# Method 1: Full startup script
python run.py

# Method 2: Direct app launch
python app.py

# Method 3: With dependency installation
python run.py --install
```

## First Time Usage

1. **Enter text** in the input box (try: "The quick brown fox jumps over the lazy dog.")
2. **Select a model** (default: gpt2)
3. **Choose model type** (decoder for GPT-like, encoder for BERT-like)
4. **Click "Analyze"**

You'll see:
- 🟢 Green tokens = Low perplexity (model is confident)
- 🔴 Red tokens = High perplexity (model is uncertain)

## Troubleshooting

**Common Issues:**

- **"Module not found"** → Run: `pip install -r requirements.txt`
- **"Model download failed"** → Check internet connection
- **"Launch failed"** → Try: `python launch.py` or `python app.py`
- **Out of memory** → Use smaller models like `distilgpt2` or `distilbert-base-uncased`

**GPU Support:**
- Automatically uses GPU if available
- Falls back to CPU if no GPU found

## Example Models to Try

**Decoder (GPT-like):**
- `gpt2` - Standard GPT-2
- `distilgpt2` - Smaller, faster
- `microsoft/DialoGPT-small` - Conversational

**Encoder (BERT-like):**
- `bert-base-uncased` - Standard BERT
- `distilbert-base-uncased` - Smaller, faster
- `roberta-base` - Improved BERT

## Need Help?

Run the test suite:
```bash
python test_app.py
```

Or try the command-line demo:
```bash
python demo.py
```

**Still having issues?** Check the full README.md for detailed instructions.

## ✅ Recent Updates

**Ultra-Simplified Interface!**
- Removed MLM probability slider for cleaner interface
- Removed iterations slider - single comprehensive analysis per run
- Encoder models now analyze all tokens for complete results
- Decoder models provide single-pass perplexity calculation
- Tokens are properly colored by perplexity (green=confident, red=uncertain)
- If you see black/white tokens, try refreshing the browser
- Test the colors with: `python simple_color_test.py` (creates color_test.html)