path: root/python-langhuan.spec

%global _empty_manifest_terminate_build 0
Name:		python-langhuan
Version:	0.1.12
Release:	1
Summary:	Language Human Annotation
License:	Apache Software License 2.0
URL:		https://github.com/raynardj/langhuan
Source0:	https://mirrors.aliyun.com/pypi/web/packages/e9/ab/5bb42a04d5bb5d16d089fb90f3a621147c03c65f393528ca9c0fb1653e05/langhuan-0.1.12.tar.gz
BuildArch:	noarch


%description
# LangHuAn
> **Lang**uage **Hu**man **An**notations, a frontend for tagging AI project labels, drived by pandas dataframe data.

> From Chinese word **琅嬛[langhuan]** (Legendary realm where god curates books)

Here's a [5 minutes youtube video](https://www.youtube.com/watch?v=Nwh6roiX_9I) explaining how langhuan works

[![Introduction Video](https://raw.githubusercontent.com/raynardj/langhuan/main/docs/imgs/ner1.jpg)](https://www.youtube.com/watch?v=Nwh6roiX_9I)

## Installation
```shell
pip install langhuan
```

## Minimun configuration walk through
> langhuan start a flask application from **pandas dataframe** 🐼 !

### Simplest configuration for **NER** task 🚀

```python
from langhuan import NERTask

app = NERTask.from_df(
    df, text_col="description",
    options=["institution", "company", "name"])
app.run("0.0.0.0", port=5000)
```

### Simplest configuration for **Classify** task 🚀
```python
from langhuan import ClassifyTask

app = ClassifyTask.from_df(
    df, text_col="comment",
    options=["positive", "negative", "unbiased", "not sure"])
app.run("0.0.0.0", port=5000)
```
![classification image](https://raw.githubusercontent.com/raynardj/langhuan/main/docs/imgs/classify1.jpg)

## Frontend
> You can visit following pages for this app.

### Tagging
```http://[ip]:[port]/``` is for our hard working taggers to visit.

### Admin
```http://[ip]:[port]/admin``` is a page where you can 👮🏽‍♂️:
* See the progress of each user.
* Force save the progress, (or it will only save according to ```save_frequency```, default 42 entries)
* Download the tagged entries

## Advanced settings
#### Validation
You can set minimun verification number: ```cross_verify_num```, aka, how each entry will be validated, default is 1

If you set ```cross_verify_num``` to 2, and you have 5 taggers, each entry will be seen by 2 taggers

```python
app = ClassifyTask.from_df(
    df, text_col="comment",
    options=["positive", "negative", "unbiased", "not sure"],
    cross_verify_num=2,)
```

#### Preset the tagging
You can set a column in dataframe, eg. called ```guessed_tags```, to preset the tagging result.

Each cell can contain the format of tagging result, eg. 
```json
{"tags":[
    {"text": "Genomicare Bio Tech", "offset":32, "label":"company"},
    {"text": "East China University of Politic Science & Law", "offset":96, "label":"company"},
    ]}
```

Then you can run the app with preset tag column
```python
app = NERTask.from_df(
    df, text_col="description",
    options=["institution", "company", "name"],
    preset_tag_col="guessed_tags")
app.run("0.0.0.0", port=5000)
```

#### Order strategy
The order of which text got tagged first is according to order_strategy.

Default is set to ```"forward_match"```, you can try ```pincer``` or ```trident```
![order strategies](https://raw.githubusercontent.com/raynardj/langhuan/main/docs/imgs/strategies.jpg)

Assume the order_by_column is set to the prediction of last batch of deep learning model:
- trident means the taggers tag the most confident positive, most confident negative, most unsure ones first.

#### Load History
If your service stopped, you can recover the progress from cache.

Previous cache will be at ```$HOME/.cache/langhuan/{task_name}```

You can change the save_frequency to suit your task, default is 42 entries.

```python
app = NERTask.from_df(
    df, text_col="description",
    options=["institution", "company", "name"],
    save_frequency=128,
    load_history=True,
    task_name="task_NER_210123_110327"
    )
```

#### Admin Control
> This application assumes internal use within organization, hence the mininum security. If you set admin_control, all the admin related page will require ```adminkey```, the key will appear in the console prompt

```python
app = NERTask.from_df(
    df, text_col="description",
    options=["institution", "company", "name"],
    admin_control=True,
    )
```

#### From downloaded data => pytorch dataset
> For downloaded NER data tags, you can create a dataloader with the json file automatically:
* [pytorch + huggingface tokenizer](https://raynardj.github.io/langhuan/docs/loader)
* tensorflow + huggingface tokenizer, development pending

#### Gunicorn support
This is a **light weight** solution. When move things to gunicorn, multithreads is acceptable, but multiworkers will cause chaos.

```shell
gunicorn --workers=1 --threads=5 app:app
```

## Compatibility 💍
Well, this library hasn't been tested vigorously against many browsers with many versions, so far
* compatible with chrome, firefox, safari if version not too old.



%package -n python3-langhuan
Summary:	Language Human Annotation
Provides:	python-langhuan
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-langhuan
# LangHuAn
> **Lang**uage **Hu**man **An**notations, a frontend for tagging AI project labels, drived by pandas dataframe data.

> From Chinese word **琅嬛[langhuan]** (Legendary realm where god curates books)

Here's a [5 minutes youtube video](https://www.youtube.com/watch?v=Nwh6roiX_9I) explaining how langhuan works

[![Introduction Video](https://raw.githubusercontent.com/raynardj/langhuan/main/docs/imgs/ner1.jpg)](https://www.youtube.com/watch?v=Nwh6roiX_9I)

## Installation
```shell
pip install langhuan
```

## Minimun configuration walk through
> langhuan start a flask application from **pandas dataframe** 🐼 !

### Simplest configuration for **NER** task 🚀

```python
from langhuan import NERTask

app = NERTask.from_df(
    df, text_col="description",
    options=["institution", "company", "name"])
app.run("0.0.0.0", port=5000)
```

### Simplest configuration for **Classify** task 🚀
```python
from langhuan import ClassifyTask

app = ClassifyTask.from_df(
    df, text_col="comment",
    options=["positive", "negative", "unbiased", "not sure"])
app.run("0.0.0.0", port=5000)
```
![classification image](https://raw.githubusercontent.com/raynardj/langhuan/main/docs/imgs/classify1.jpg)

## Frontend
> You can visit following pages for this app.

### Tagging
```http://[ip]:[port]/``` is for our hard working taggers to visit.

### Admin
```http://[ip]:[port]/admin``` is a page where you can 👮🏽‍♂️:
* See the progress of each user.
* Force save the progress, (or it will only save according to ```save_frequency```, default 42 entries)
* Download the tagged entries

## Advanced settings
#### Validation
You can set minimun verification number: ```cross_verify_num```, aka, how each entry will be validated, default is 1

If you set ```cross_verify_num``` to 2, and you have 5 taggers, each entry will be seen by 2 taggers

```python
app = ClassifyTask.from_df(
    df, text_col="comment",
    options=["positive", "negative", "unbiased", "not sure"],
    cross_verify_num=2,)
```

#### Preset the tagging
You can set a column in dataframe, eg. called ```guessed_tags```, to preset the tagging result.

Each cell can contain the format of tagging result, eg. 
```json
{"tags":[
    {"text": "Genomicare Bio Tech", "offset":32, "label":"company"},
    {"text": "East China University of Politic Science & Law", "offset":96, "label":"company"},
    ]}
```

Then you can run the app with preset tag column
```python
app = NERTask.from_df(
    df, text_col="description",
    options=["institution", "company", "name"],
    preset_tag_col="guessed_tags")
app.run("0.0.0.0", port=5000)
```

#### Order strategy
The order of which text got tagged first is according to order_strategy.

Default is set to ```"forward_match"```, you can try ```pincer``` or ```trident```
![order strategies](https://raw.githubusercontent.com/raynardj/langhuan/main/docs/imgs/strategies.jpg)

Assume the order_by_column is set to the prediction of last batch of deep learning model:
- trident means the taggers tag the most confident positive, most confident negative, most unsure ones first.

#### Load History
If your service stopped, you can recover the progress from cache.

Previous cache will be at ```$HOME/.cache/langhuan/{task_name}```

You can change the save_frequency to suit your task, default is 42 entries.

```python
app = NERTask.from_df(
    df, text_col="description",
    options=["institution", "company", "name"],
    save_frequency=128,
    load_history=True,
    task_name="task_NER_210123_110327"
    )
```

#### Admin Control
> This application assumes internal use within organization, hence the mininum security. If you set admin_control, all the admin related page will require ```adminkey```, the key will appear in the console prompt

```python
app = NERTask.from_df(
    df, text_col="description",
    options=["institution", "company", "name"],
    admin_control=True,
    )
```

#### From downloaded data => pytorch dataset
> For downloaded NER data tags, you can create a dataloader with the json file automatically:
* [pytorch + huggingface tokenizer](https://raynardj.github.io/langhuan/docs/loader)
* tensorflow + huggingface tokenizer, development pending

#### Gunicorn support
This is a **light weight** solution. When move things to gunicorn, multithreads is acceptable, but multiworkers will cause chaos.

```shell
gunicorn --workers=1 --threads=5 app:app
```

## Compatibility 💍
Well, this library hasn't been tested vigorously against many browsers with many versions, so far
* compatible with chrome, firefox, safari if version not too old.



%package help
Summary:	Development documents and examples for langhuan
Provides:	python3-langhuan-doc
%description help
# LangHuAn
> **Lang**uage **Hu**man **An**notations, a frontend for tagging AI project labels, drived by pandas dataframe data.

> From Chinese word **琅嬛[langhuan]** (Legendary realm where god curates books)

Here's a [5 minutes youtube video](https://www.youtube.com/watch?v=Nwh6roiX_9I) explaining how langhuan works

[![Introduction Video](https://raw.githubusercontent.com/raynardj/langhuan/main/docs/imgs/ner1.jpg)](https://www.youtube.com/watch?v=Nwh6roiX_9I)

## Installation
```shell
pip install langhuan
```

## Minimun configuration walk through
> langhuan start a flask application from **pandas dataframe** 🐼 !

### Simplest configuration for **NER** task 🚀

```python
from langhuan import NERTask

app = NERTask.from_df(
    df, text_col="description",
    options=["institution", "company", "name"])
app.run("0.0.0.0", port=5000)
```

### Simplest configuration for **Classify** task 🚀
```python
from langhuan import ClassifyTask

app = ClassifyTask.from_df(
    df, text_col="comment",
    options=["positive", "negative", "unbiased", "not sure"])
app.run("0.0.0.0", port=5000)
```
![classification image](https://raw.githubusercontent.com/raynardj/langhuan/main/docs/imgs/classify1.jpg)

## Frontend
> You can visit following pages for this app.

### Tagging
```http://[ip]:[port]/``` is for our hard working taggers to visit.

### Admin
```http://[ip]:[port]/admin``` is a page where you can 👮🏽‍♂️:
* See the progress of each user.
* Force save the progress, (or it will only save according to ```save_frequency```, default 42 entries)
* Download the tagged entries

## Advanced settings
#### Validation
You can set minimun verification number: ```cross_verify_num```, aka, how each entry will be validated, default is 1

If you set ```cross_verify_num``` to 2, and you have 5 taggers, each entry will be seen by 2 taggers

```python
app = ClassifyTask.from_df(
    df, text_col="comment",
    options=["positive", "negative", "unbiased", "not sure"],
    cross_verify_num=2,)
```

#### Preset the tagging
You can set a column in dataframe, eg. called ```guessed_tags```, to preset the tagging result.

Each cell can contain the format of tagging result, eg. 
```json
{"tags":[
    {"text": "Genomicare Bio Tech", "offset":32, "label":"company"},
    {"text": "East China University of Politic Science & Law", "offset":96, "label":"company"},
    ]}
```

Then you can run the app with preset tag column
```python
app = NERTask.from_df(
    df, text_col="description",
    options=["institution", "company", "name"],
    preset_tag_col="guessed_tags")
app.run("0.0.0.0", port=5000)
```

#### Order strategy
The order of which text got tagged first is according to order_strategy.

Default is set to ```"forward_match"```, you can try ```pincer``` or ```trident```
![order strategies](https://raw.githubusercontent.com/raynardj/langhuan/main/docs/imgs/strategies.jpg)

Assume the order_by_column is set to the prediction of last batch of deep learning model:
- trident means the taggers tag the most confident positive, most confident negative, most unsure ones first.

#### Load History
If your service stopped, you can recover the progress from cache.

Previous cache will be at ```$HOME/.cache/langhuan/{task_name}```

You can change the save_frequency to suit your task, default is 42 entries.

```python
app = NERTask.from_df(
    df, text_col="description",
    options=["institution", "company", "name"],
    save_frequency=128,
    load_history=True,
    task_name="task_NER_210123_110327"
    )
```

#### Admin Control
> This application assumes internal use within organization, hence the mininum security. If you set admin_control, all the admin related page will require ```adminkey```, the key will appear in the console prompt

```python
app = NERTask.from_df(
    df, text_col="description",
    options=["institution", "company", "name"],
    admin_control=True,
    )
```

#### From downloaded data => pytorch dataset
> For downloaded NER data tags, you can create a dataloader with the json file automatically:
* [pytorch + huggingface tokenizer](https://raynardj.github.io/langhuan/docs/loader)
* tensorflow + huggingface tokenizer, development pending

#### Gunicorn support
This is a **light weight** solution. When move things to gunicorn, multithreads is acceptable, but multiworkers will cause chaos.

```shell
gunicorn --workers=1 --threads=5 app:app
```

## Compatibility 💍
Well, this library hasn't been tested vigorously against many browsers with many versions, so far
* compatible with chrome, firefox, safari if version not too old.



%prep
%autosetup -n langhuan-0.1.12

%build
%py3_build

%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
	find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
	find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
	find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
	find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
	find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .

%files -n python3-langhuan -f filelist.lst
%dir %{python3_sitelib}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1.12-1
- Package Spec generated