semvec/pypi-packages huggingface dataset:
LibJciHitachi 1.7.2
A library for controlling Jci Hitachi devices.
Requires Python: >=3.10

# Jci-Hitachi Library

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## Feature

A Python library for controlling Jci-Hitachi devices.

## Supported devices

*支援以下使用日立雲端模組(雲端智慧控)的機種與功能*

- Hitachi Air Conditioner 日立冷氣
  - Power 電源
  - Mode 運轉模式
  - Air speed 風速
  - Vertical wind swingable 導風板垂直擺動
  - Vertical wind direction 導風板垂直方向
  - Horizontal wind direction 導風板水平方向
  - Target temperature 目標溫度
  - Indoor temperature 室內溫度
  - Sleep timer 睡眠計時器
  - Freeze clean 凍結洗淨
  - Mold prevention 機體防霉
  - Energy saving 節電
  - Fast operation 快速運轉
  - Power consumption 用電統計
  - Monthly power consumption 月用電統計
  - Panel 顯示器亮度
- Hitachi Dehumidifier 日立除濕機
  - Power 電源
  - Mode 運轉模式
  - Air speed 風速
  - Wind swingable 導風板擺動
  - Side vent 側吹
  - Target humidity 目標濕度
  - Indoor humidity 室內溼度
  - Water full warning 滿水警示
  - Clean filter notify 濾網清潔通知
  - Mold prevention 機體防霉
  - Error code 錯誤代碼
  - PM2.5 value PM2.5數值
  - Sound control 聲音控制
  - Display brightness 顯示器亮度
  - Ion 負離子
  - Keypad Lock 按鍵鎖
  - Odor level 異味等級
  - Air cleaning filter setting 空氣清淨濾網設定
  - Power consumption 用電統計
  - Monthly power consumption 月用電統計
- Hitachi Heat Exchanger 日立全熱交換機
  - Power 電源
  - Mode 運轉模式
  - Breath mode 換氣模式
  - Air speed 風速
  - Indoor temperature 室內溫度
  - Error code 錯誤代碼
  - Air cleaning filter notification 空氣清淨濾網清潔通知
  - Front filter notification 前置濾網清潔通知
  - PM25 filter notification PM25濾網清潔通知

## Installation

### Python Library

    pip install LibJciHitachi

### Home Assistant Integration

See [JciHitachiHA](https://github.com/qqaatw/JciHitachiHA).

## Documentation

See [docs](https://libjcihitachi.readthedocs.io/en/latest/).

## Todo

1. PM 2.5 panel support.

## Acknowledgement

- @narensankar0529 - Assisting with the dehumidifier support.

## License

Apache License 2.0
websight 0.1.2
A Vision-First Architecture for Robust Web Agents
Requires Python: >=3.12

# Websight

Vision first browser agents based on Websight-7B, a custom 7B parameter model.

## Installation

```bash
pip install websight
# or
uv add websight
```

## Quickstart

Call the model directly on an image:

```python
from websight import websight_call

action = websight_call(
    prompt="Click the Login button",
    history=[],  # prior (reasoning, action) pairs if you have them
    image_base64="data:image/png;base64,<...>",
)
print(action.action)  # e.g., "click"
print(action.args)    # e.g., {"x": "175", "y": "514"}
```

## Reference

- websight.websight_call

```python
def websight_call(
    prompt: str,
    history: list[tuple[str, str]],
    image_base64: str,
    console: rich.console.Console | None = None,
    max_new_tokens: int = 1000,
) -> Action
```

Calls the Websight VLM with a screenshot and instruction, returning a structured `Action`.

- websight.Action

```python
class Action(BaseModel):
    action: str                # e.g. "click", "drag", "type", "scroll", ...
    args: dict[str, str]       # e.g. {"x": "175", "y": "514"}
    reasoning: str             # model rationale
```

- websight.Agent

```python
from websight.agent import Agent

agent = Agent(show_browser=False)
result = agent.run("Open https://example.com and search for 'websight'", max_iterations=10)
```

Basic Agent loop using Playwright: takes a screenshot, calls `websight_call`, parses and executes the predicted action, and repeats until it sees `finished(...)`.