semvec/pypi-packages huggingface dataset:
cemir-print 202231005.2
A colorful printing library for Python

```python
from cemir_print.ccprint import ccprint
```

```python
ccprint("Hello, World!")
ccprint([1, 2, 3, 4, 5, 6,{"name": "İzmir", "plaka": 35}])
ccprint(42)
ccprint(False)
```

```python
ccprint({
    "name": "muslu yüksektepe",
    "age": 30,
    "city": "İstanbul",
    "whereareyoufrom": "Türkiye",
    "are_you_ok": True,
    "listem": [1, 3.14, 3, "ceylan", "emir", "muslu", "cem", {"age": 35, "number":{"float": 35.58}}]
})

```

![Örnek Çıktı](https://raw.githubusercontent.com/muslu/cemir_print/main/output.png)

[](https://)
dict-path 1.1.1
Python library to work with complicated nested dict
Requires Python: >=3.6

# DictPath
Extended dict for Python. 
Make easy to extract complicated nested dict.

![repo-size](https://img.shields.io/github/repo-size/maztohir/dict-path)
![license](https://img.shields.io/github/license/maztohir/dict-path)

## Installation
```bash
pip3 install dict-path
```

## Getting and setting value with path

#### No more boilerplate code to extract or inject nested dict
##### What you do previously
```python
test_dict = {'foo1':{'foo2':{'foo3':{'foo4':'bar'}}}}
foo1 = test_dict['foo1']
if foo1:
   foo2 = foo1['foo2']
   if foo2:
       foo3 = foo2['foo3']
       if foo3:
           foo4 = foo3['foo4'] #finally, get the result: bar1
```
##### What you can do NOW
```python
test_dict = {'foo1':{'foo2':{'foo3':{'foo4':'bar'}}}}
data = DictPath(test_dict)
data.get('foo1/foo2/foo3/foo4') #result: bar
```

##### Set value with easy
```python
data.set('foo1/foo2/foo3/foo5', 'bar1')
data.get('foo1/foo2/foo3/foo5') #result: bar1
```

#### Do not want to use new Object? no worries, we have a method that you can call directly

```python
from dict_path import extract_dict, inject_dict

test_dict = {'foo1':{'foo2':{'foo3':{'foo4':'bar'}}}}
extract_dict(test_dict, 'foo1/foo2/foo3/foo4') #result: bar

inject_dict(test_dict, 'foo1/foo2/foo3/foo5', 'bar1')}
extract_dict(test_dict, 'foo1/foo2/foo3/foo5') #result: bar1
```

## Concept
#### DictPath is basically a normal python dict, nothing different
```python
# A DictPath keeps a reference to the original initializing dict:

normal_dict = {'foo1':{'foo2':{'foo3':{'foo4':'bar'}}}}
dic_path = DictPath(normal_dict)
> normal_dict == dic_path
---> True
> dic_path.dict is normal_dict
---> True
```

#### You can also get a deep copy:
```python
joe = DictPath(user, deepcopy=True)
> joe == user
---> True
> joe.dict is user
---> False
```

#### Invalid path will return None
```python
from dict_path import DictPath

test_dict = {'foo1':{'foo2':{'foo3':{'foo4':'bar'}}}}
data = DictPath(test_dict)
data.get('foo1/foo2/foo3/foo4/foo6')
#result: None
```
#### Set up unknown path will create an actual dict
```python
from dict_path import DictPath

test_dict = {'foo1':{'foo2':{'foo3':{'foo4':'bar'}}}}
data = DictPath(test_dict)
data.set('foo1/foo2/foo3/foo5/foo6/foo7/foo8/', 'bar1')
data.get('foo1/foo2/foo3/foo5/foo6/foo7/foo8/')
#result: bar1
```

#### Its also working with array
Note: For now its only working with get or extract_dict.
```python
from dict_path import extract_dict, DictPath

test_dict = {'foo1':{'foo2':[{'foo3':'bar'},{'foo4':[[{'foo5': 'bar'}]]}]}}
data = DictPath(test_dict)
data.get('foo1/foo2/1/foo4/0/0/foo5/')
#result: bar

extract_dict(test_dict, 'foo1/foo2/1/foo4/0/0/foo5/')
```


## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License
[MIT](https://choosealicense.com/licenses/mit/)
deepbrainz-interpreter 0.42.41
An Open Source version of ChatGPT Code Interpreter

<img src="https://github.com/ricklamers/gpt-code-ui/assets/1309307/9ad4061d-2e26-4407-9431-109b650fb022" alt="GPT-Code logo" width=240 />

An open source implementation of OpenAI's ChatGPT [Code interpreter](https://openai.com/blog/chatgpt-plugins#code-interpreter).

Simply ask the OpenAI model to do something and it will generate & execute the code for you.

Read the [blog post](https://ricklamers.io/posts/gpt-code) to find out more.

## Community
Judah Cooper offered to start & curate a Discord community. Join [here](https://discord.gg/ZmTQwpkYu6).

## Installation

Open a terminal and run:

```
pip install gpt-code-ui
gptcode
```

In order to make basic dependencies available it's recommended to run the following `pip` install
in the Python environment that is used in the shell where you run `gptcode`:

```sh
pip install "numpy>=1.24,<1.25" "dateparser>=1.1,<1.2" "pandas>=1.5,<1.6" "geopandas>=0.13,<0.14" "tabulate>=0.9.0<1.0" "PyPDF2>=3.0,<3.1" "pdfminer>=20191125,<20191200" "pdfplumber>=0.9,<0.10" "matplotlib>=3.7,<3.8"
```

## User interface
<img src="https://github.com/ricklamers/gpt-code-ui/assets/1309307/c29c504a-a7ed-4ae0-9360-d7224bc3e3d6" alt="GPT-Code logo" width="100%" />
 
## Features
- File upload
- File download
- Context awareness (it can refer to your previous messages)
- Generate code
- Run code (Python kernel)
- Model switching (GPT-3.5 and GPT-4)

## Misc.
### Using .env for OpenAI key
You can put a .env in the working directory to load the `OPENAI_API_KEY` environment variable.

### Configurables
Set the `API_PORT`, `WEB_PORT`, `SNAKEMQ_PORT` variables to override the defaults.

Set `OPENAI_BASE_URL` to change the OpenAI API endpoint that's being used (note this environment variable includes the protocol `https://...`).

You can use the `.env.example` in the repository (make sure you `git clone` the repo to get the file first).

For Azure OpenAI Services, there are also other configurable variables like deployment name. See `.env.azure-example` for more information.
Note that model selection on the UI is currently not supported for Azure OpenAI Services.

```
cp .env.example .env
vim .env
gptcode
```

### Docker
[localagi](https://github.com/localagi) took the effort of bundling the Python package in a Docker container. Check it out here: [gpt-code-ui-docker](https://github.com/localagi/gpt-code-ui-docker).

## Contributing
Please do and have a look at the [contributions guide](.github/CONTRIBUTING.md)! This should be a community initiative. I'll try my best to be responsive.


Thank you for your interest in this project!