import gradio as gr

INTRO_MD = """
# 🕵️ Codenames LLM Challenge

Welcome! This is a coding challenge where you build a **guesser bot** for the board game
[Codenames](https://en.wikipedia.org/wiki/Codenames_(board_game)).

An LLM-powered **spymaster** will give you one-word clues. Your job is to write the
**guesser** that picks the right words on the board.

---

## How the game works

The board has **25 words** split into hidden roles:

| Role | Count | Meaning |
|------|-------|---------|
| 🟥 RED | 9 | Your team's words — guess these! |
| 🟦 BLUE | 8 | Opponent words — avoid these |
| 💀 ASSASSIN | 8 | Instant loss if revealed |

Each round:
1. The **spymaster** gives a one-word clue + a number (e.g. `"ocean 3"`)
2. Your **guesser** can make up to `number + 1` guesses
3. A round ends when you reveal a BLUE word, reach the max guesses, or return `None`
4. The game ends when **all RED words are found** (win) or the **assassin is revealed** (loss)

Your score is based on the number of rounds needed — fewer is better.

---

## Two steps to participate
"""

STEP1_MD = """
## Step 1 — Clone the repository

Open a terminal and run:

```bash
git clone https://huggingface.co/spaces/LLM-course/codenames
cd codenames
```

Then set up the Python environment:

```bash
uv venv
source .venv/bin/activate        # on Windows: .venv\\Scripts\\activate
uv pip install -r requirements.txt
```

*(Optional but recommended)* Pre-build the embedding cache so runs are faster:

```bash
python -m codenames.cli init-cache
```
"""

STEP2_MD = """
## Step 2 — Implement `my_guesser.py`

Open `my_guesser.py` and fill in the `guesser` function:

```python
# my_guesser.py
from codenames.embedder import EmbeddingIndex
from codenames.dictionary import load_dictionary

_dictionary = load_dictionary()
_index = EmbeddingIndex(_dictionary)

def guesser(clue: str, board_state: list[str]) -> str | None:
    \"\"\"
    Args:
        clue:        The spymaster's one-word clue (always from the 420-word dictionary)
        board_state: List of unrevealed words currently on the board

    Returns:
        A word from board_state to guess, or None to stop guessing this round.
    \"\"\"
    # TODO: replace this with your smart logic!
    return board_state[0]
```

### Tips
- Use **embedding similarity** between the clue and board words — `_index.vector(word)`
  returns a numpy vector for any dictionary word.
- Return `None` when you're not confident: it's safer than hitting a BLUE or ASSASSIN word.
- The clue is always a word from the shared 420-word dictionary (case-insensitive).

### Test your guesser

```bash
python -m codenames.cli challenge my_guesser.py --seed 42 --output log.json
```

Options:
- `--seed` — fix the board for reproducibility
- `--max-rounds` — limit rounds (default 10)
- `--output` — save a detailed JSON log with clues, guesses and the final result

The JSON log is useful for inspecting mistakes and improving your strategy.
"""

RULES_MD = """
## Full rules summary

- Board: 25 words, 9 RED / 8 BLUE / 8 ASSASSIN (challenge / single-team mode)
- Clue + number are given each round by the LLM spymaster
- Guesser may guess up to `number + 1` times per round
- Revealing a BLUE word ends the round immediately
- Revealing the ASSASSIN ends the game immediately (loss)
- **Goal:** reveal all 9 RED words in as few rounds as possible

## Dictionary

All clue words and board words are drawn from a **fixed list of 420 Codenames words**
(see `codenames/data/codenames_dict.txt`). Your guesser receives words from this list
and must return a word from the current `board_state`.
"""

with gr.Blocks(title="Codenames LLM Challenge") as demo:
    gr.Markdown(INTRO_MD)

    with gr.Tabs():
        with gr.TabItem("🛠️ Step 1 — Clone"):
            gr.Markdown(STEP1_MD)

        with gr.TabItem("💡 Step 2 — Implement"):
            gr.Markdown(STEP2_MD)

        with gr.TabItem("📋 Full Rules"):
            gr.Markdown(RULES_MD)

if __name__ == "__main__":
    demo.launch()