animtoreal 2025.12.21143241
This new package enables users to input a description or details about an animated character and receive a structured summary or step-by-step plan to bring that character into the real world, such as
License: MIT
Requires Python: >=3.9

# animtoreal
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`animtoreal` is a lightweight Python package that turns a textual description of an animated character into a structured, step‑by‑step plan for bringing that character into the real world.  
It extracts key information such as props, costumes, interactive experiences and other production details using a large language model, without requiring any media files.

> **Why use animtoreal?**  
> - Quickly generate a production‑ready spec from a creative brief.  
> - Leverage any LLM – the package ships with a default free‑tier LLM (`ChatLLM7`) but you can plug in OpenAI, Anthropic, Google Gemini, or any LangChain‑compatible model.  
> - No heavy dependencies or UI required – just a single function call.

---

## 📦 Installation

```bash
pip install animtoreal
```

---

## 🚀 Quick Start

```python
from animtoreal import animtoreal

user_input = """
I want to create a small floating silver robot that can interact with children.
It should have smooth metal panels, a small LED face that displays simple emotions,
and a lightweight adjustable arm that can point at objects. The robot must be
battery powered, easy to clean, and have a friendly safety char.
"""

# Using the built‑in default LLM (ChatLLM7)
response = animtoreal(user_input)
print(response)
```

`response` will be a list of strings, each representing a structured element of the production plan.

---

## 📚 Function Signature

```python
animtoreal(
    user_input: str,
    api_key: Optional[str] = None,
    llm: Optional[BaseChatModel] = None
) -> List[str]
```

| Parameter | Type | Description |
|-----------|------|-------------|
| `user_input` | `str` | Text description of the character. |
| `llm` | `Optional[BaseChatModel]` | A LangChain ChatModel instance. If omitted, the default `ChatLLM7` is used. |
| `api_key` | `Optional[str]` | API key for LLM7. If omitted, the package will look for `LLM7_API_KEY` in the environment, and fall back to `"None"` (for the free tier). |

---

## 🔗 Using Your Own LLM

`animtoreal` is agnostic to the underlying LLM.  
Below are quick examples with popular providers.

### OpenAI

```python
from langchain_openai import ChatOpenAI
from animtoreal import animtoreal

llm = ChatOpenAI()  # defaults to your OPENAI_API_KEY env var
response = animtoreal(user_input, llm=llm)
```

### Anthropic

```python
from langchain_anthropic import ChatAnthropic
from animtoreal import animtoreal

llm = ChatAnthropic()
response = animtoreal(user_input, llm=llm)
```

### Google Gemini

```python
from langchain_google_genai import ChatGoogleGenerativeAI
from animtoreal import animtoreal

llm = ChatGoogleGenerativeAI()
response = animtoreal(user_input, llm=llm)
```

---

## 🔒 LLM7 API Key & Rate Limits

- The default free tier of **LLM7** is usually sufficient for most small‑to‑medium projects.  
- If you need higher limits, provide your key via an environment variable:

  ```bash
  export LLM7_API_KEY="your_real_api_key"
  ```

  or pass it directly:

  ```python
  response = animtoreal(user_input, api_key="your_real_api_key")
  ```

- Obtain a free key at: <https://token.llm7.io/>

---

## 🤝 Contributing & Issues

- **GitHub Issues**: <https://github.com/chigwell/animtoreal/issues>  

Feel free to open an issue if you encounter bugs or have feature requests.

---

## 📄 Licensing

This project is open source under the MIT License. (Adapt as appropriate.)

---

## 👤 Maintainer

- **Eugene Evstafev**  
  [hi@euegne.plus](mailto:hi@euegne.plus)  
  GitHub: [@chigwell](https://github.com/chigwell)

---

Happy creating! 🎨🦾