%global _empty_manifest_terminate_build 0
Name:		python-spark-nlp-display
Version:	4.4
Release:	1
Summary:	Visualization package for Spark NLP
License:	Apache Software License
URL:		http://nlp.johnsnowlabs.com
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/af/27/b84525e77152b17ae6d2d39a0cee975a1d9d57ba2f8ad210649238696b31/spark-nlp-display-4.4.tar.gz
BuildArch:	noarch

Requires:	python3-spark-nlp
Requires:	python3-ipython
Requires:	python3-svgwrite
Requires:	python3-pandas
Requires:	python3-numpy

%description
# spark-nlp-display
A library for the simple visualization of different types of Spark NLP annotations. 

## Supported Visualizations:
- Dependency Parser
- Named Entity Recognition
- Entity Resolution
- Relation Extraction
- Assertion Status

## Complete Tutorial
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-display/blob/main/tutorials/Spark_NLP_Display.ipynb)

https://github.com/JohnSnowLabs/spark-nlp-display/blob/main/tutorials/Spark_NLP_Display.ipynb

### Requirements
- spark-nlp
- ipython
- svgwrite
- pandas
- numpy

### Installation
```bash
pip install spark-nlp-display
```

### How to use

### Databricks
#### For all modules, pass in the additional parameter "return_html=True" in the display function and use Databrick's function displayHTML() to render visualization as explained below:
```python
from sparknlp_display import NerVisualizer

ner_vis = NerVisualizer()

## To set custom label colors:
ner_vis.set_label_colors({'LOC':'#800080', 'PER':'#77b5fe'}) #set label colors by specifying hex codes

pipeline_result = ner_light_pipeline.fullAnnotate(text) ##light pipeline
#pipeline_result = ner_full_pipeline.transform(df).collect()##full pipeline

vis_html = ner_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
                    label_col='entities', #specify the entity column
                    document_col='document', #specify the document column (default: 'document')
                    labels=['PER'], #only allow these labels to be displayed. (default: [] - all labels will be displayed)
                    return_html=True)

displayHTML(vis_html)
```
![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/ner_viz.png)

### Jupyter

To save the visualization as html, provide the export file path: `save_path='./export.html'` for each visualizer.


#### Dependency Parser
```python
from sparknlp_display import DependencyParserVisualizer

dependency_vis = DependencyParserVisualizer()

pipeline_result = dp_pipeline.fullAnnotate(text)
#pipeline_result = dp_full_pipeline.transform(df).collect()##full pipeline

dependency_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe.
                       pos_col = 'pos', #specify the pos column
                       dependency_col = 'dependency', #specify the dependency column
                       dependency_type_col = 'dependency_type', #specify the dependency type column
                       save_path='./export.html' # optional - to save viz as html. (default: None)
                       )
```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/dp_viz.png)

#### Named Entity Recognition

```python
from sparknlp_display import NerVisualizer

ner_vis = NerVisualizer()

pipeline_result = ner_light_pipeline.fullAnnotate(text)
#pipeline_result = ner_full_pipeline.transform(df).collect()##full pipeline

ner_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
                    label_col='entities', #specify the entity column
                    document_col='document', #specify the document column (default: 'document')
                    labels=['PER'], #only allow these labels to be displayed. (default: [] - all labels will be displayed)
                    save_path='./export.html' # optional - to save viz as html. (default: None)
                    )

## To set custom label colors:
ner_vis.set_label_colors({'LOC':'#800080', 'PER':'#77b5fe'}) #set label colors by specifying hex codes

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/ner_viz.png)

#### Entity Resolution

```python
from sparknlp_display import EntityResolverVisualizer

er_vis = EntityResolverVisualizer()

pipeline_result = er_light_pipeline.fullAnnotate(text)

er_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
               label_col='entities', #specify the ner result column
               resolution_col = 'resolution',
               document_col='document', #specify the document column (default: 'document')
               save_path='./export.html' # optional - to save viz as html. (default: None)
               )

## To set custom label colors:
er_vis.set_label_colors({'TREATMENT':'#800080', 'PROBLEM':'#77b5fe'}) #set label colors by specifying hex codes

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/er_viz.png)

#### Relation Extraction
```python
from sparknlp_display import RelationExtractionVisualizer

re_vis = RelationExtractionVisualizer()

pipeline_result = re_light_pipeline.fullAnnotate(text)

re_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
               relation_col = 'relations', #specify relations column
               document_col = 'document', #specify document column
               show_relations=True, #display relation names on arrows (default: True)
               save_path='./export.html' # optional - to save viz as html. (default: None)
               )

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/re_viz.png)

#### Assertion Status
```python
from sparknlp_display import AssertionVisualizer

assertion_vis = AssertionVisualizer()

pipeline_result = ner_assertion_light_pipeline.fullAnnotate(text)

assertion_vis.display(pipeline_result[0], 
                      label_col = 'entities', #specify the ner result column
                      assertion_col = 'assertion', #specify assertion column
                      document_col = 'document', #specify the document column (default: 'document')
                      save_path='./export.html' # optional - to save viz as html. (default: None)
                      )
                      
## To set custom label colors:
assertion_vis.set_label_colors({'TREATMENT':'#008080', 'problem':'#800080'}) #set label colors by specifying hex codes

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/assertion_viz.png)




%package -n python3-spark-nlp-display
Summary:	Visualization package for Spark NLP
Provides:	python-spark-nlp-display
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
%description -n python3-spark-nlp-display
# spark-nlp-display
A library for the simple visualization of different types of Spark NLP annotations. 

## Supported Visualizations:
- Dependency Parser
- Named Entity Recognition
- Entity Resolution
- Relation Extraction
- Assertion Status

## Complete Tutorial
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-display/blob/main/tutorials/Spark_NLP_Display.ipynb)

https://github.com/JohnSnowLabs/spark-nlp-display/blob/main/tutorials/Spark_NLP_Display.ipynb

### Requirements
- spark-nlp
- ipython
- svgwrite
- pandas
- numpy

### Installation
```bash
pip install spark-nlp-display
```

### How to use

### Databricks
#### For all modules, pass in the additional parameter "return_html=True" in the display function and use Databrick's function displayHTML() to render visualization as explained below:
```python
from sparknlp_display import NerVisualizer

ner_vis = NerVisualizer()

## To set custom label colors:
ner_vis.set_label_colors({'LOC':'#800080', 'PER':'#77b5fe'}) #set label colors by specifying hex codes

pipeline_result = ner_light_pipeline.fullAnnotate(text) ##light pipeline
#pipeline_result = ner_full_pipeline.transform(df).collect()##full pipeline

vis_html = ner_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
                    label_col='entities', #specify the entity column
                    document_col='document', #specify the document column (default: 'document')
                    labels=['PER'], #only allow these labels to be displayed. (default: [] - all labels will be displayed)
                    return_html=True)

displayHTML(vis_html)
```
![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/ner_viz.png)

### Jupyter

To save the visualization as html, provide the export file path: `save_path='./export.html'` for each visualizer.


#### Dependency Parser
```python
from sparknlp_display import DependencyParserVisualizer

dependency_vis = DependencyParserVisualizer()

pipeline_result = dp_pipeline.fullAnnotate(text)
#pipeline_result = dp_full_pipeline.transform(df).collect()##full pipeline

dependency_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe.
                       pos_col = 'pos', #specify the pos column
                       dependency_col = 'dependency', #specify the dependency column
                       dependency_type_col = 'dependency_type', #specify the dependency type column
                       save_path='./export.html' # optional - to save viz as html. (default: None)
                       )
```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/dp_viz.png)

#### Named Entity Recognition

```python
from sparknlp_display import NerVisualizer

ner_vis = NerVisualizer()

pipeline_result = ner_light_pipeline.fullAnnotate(text)
#pipeline_result = ner_full_pipeline.transform(df).collect()##full pipeline

ner_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
                    label_col='entities', #specify the entity column
                    document_col='document', #specify the document column (default: 'document')
                    labels=['PER'], #only allow these labels to be displayed. (default: [] - all labels will be displayed)
                    save_path='./export.html' # optional - to save viz as html. (default: None)
                    )

## To set custom label colors:
ner_vis.set_label_colors({'LOC':'#800080', 'PER':'#77b5fe'}) #set label colors by specifying hex codes

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/ner_viz.png)

#### Entity Resolution

```python
from sparknlp_display import EntityResolverVisualizer

er_vis = EntityResolverVisualizer()

pipeline_result = er_light_pipeline.fullAnnotate(text)

er_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
               label_col='entities', #specify the ner result column
               resolution_col = 'resolution',
               document_col='document', #specify the document column (default: 'document')
               save_path='./export.html' # optional - to save viz as html. (default: None)
               )

## To set custom label colors:
er_vis.set_label_colors({'TREATMENT':'#800080', 'PROBLEM':'#77b5fe'}) #set label colors by specifying hex codes

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/er_viz.png)

#### Relation Extraction
```python
from sparknlp_display import RelationExtractionVisualizer

re_vis = RelationExtractionVisualizer()

pipeline_result = re_light_pipeline.fullAnnotate(text)

re_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
               relation_col = 'relations', #specify relations column
               document_col = 'document', #specify document column
               show_relations=True, #display relation names on arrows (default: True)
               save_path='./export.html' # optional - to save viz as html. (default: None)
               )

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/re_viz.png)

#### Assertion Status
```python
from sparknlp_display import AssertionVisualizer

assertion_vis = AssertionVisualizer()

pipeline_result = ner_assertion_light_pipeline.fullAnnotate(text)

assertion_vis.display(pipeline_result[0], 
                      label_col = 'entities', #specify the ner result column
                      assertion_col = 'assertion', #specify assertion column
                      document_col = 'document', #specify the document column (default: 'document')
                      save_path='./export.html' # optional - to save viz as html. (default: None)
                      )
                      
## To set custom label colors:
assertion_vis.set_label_colors({'TREATMENT':'#008080', 'problem':'#800080'}) #set label colors by specifying hex codes

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/assertion_viz.png)




%package help
Summary:	Development documents and examples for spark-nlp-display
Provides:	python3-spark-nlp-display-doc
%description help
# spark-nlp-display
A library for the simple visualization of different types of Spark NLP annotations. 

## Supported Visualizations:
- Dependency Parser
- Named Entity Recognition
- Entity Resolution
- Relation Extraction
- Assertion Status

## Complete Tutorial
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/JohnSnowLabs/spark-nlp-display/blob/main/tutorials/Spark_NLP_Display.ipynb)

https://github.com/JohnSnowLabs/spark-nlp-display/blob/main/tutorials/Spark_NLP_Display.ipynb

### Requirements
- spark-nlp
- ipython
- svgwrite
- pandas
- numpy

### Installation
```bash
pip install spark-nlp-display
```

### How to use

### Databricks
#### For all modules, pass in the additional parameter "return_html=True" in the display function and use Databrick's function displayHTML() to render visualization as explained below:
```python
from sparknlp_display import NerVisualizer

ner_vis = NerVisualizer()

## To set custom label colors:
ner_vis.set_label_colors({'LOC':'#800080', 'PER':'#77b5fe'}) #set label colors by specifying hex codes

pipeline_result = ner_light_pipeline.fullAnnotate(text) ##light pipeline
#pipeline_result = ner_full_pipeline.transform(df).collect()##full pipeline

vis_html = ner_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
                    label_col='entities', #specify the entity column
                    document_col='document', #specify the document column (default: 'document')
                    labels=['PER'], #only allow these labels to be displayed. (default: [] - all labels will be displayed)
                    return_html=True)

displayHTML(vis_html)
```
![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/ner_viz.png)

### Jupyter

To save the visualization as html, provide the export file path: `save_path='./export.html'` for each visualizer.


#### Dependency Parser
```python
from sparknlp_display import DependencyParserVisualizer

dependency_vis = DependencyParserVisualizer()

pipeline_result = dp_pipeline.fullAnnotate(text)
#pipeline_result = dp_full_pipeline.transform(df).collect()##full pipeline

dependency_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe.
                       pos_col = 'pos', #specify the pos column
                       dependency_col = 'dependency', #specify the dependency column
                       dependency_type_col = 'dependency_type', #specify the dependency type column
                       save_path='./export.html' # optional - to save viz as html. (default: None)
                       )
```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/dp_viz.png)

#### Named Entity Recognition

```python
from sparknlp_display import NerVisualizer

ner_vis = NerVisualizer()

pipeline_result = ner_light_pipeline.fullAnnotate(text)
#pipeline_result = ner_full_pipeline.transform(df).collect()##full pipeline

ner_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
                    label_col='entities', #specify the entity column
                    document_col='document', #specify the document column (default: 'document')
                    labels=['PER'], #only allow these labels to be displayed. (default: [] - all labels will be displayed)
                    save_path='./export.html' # optional - to save viz as html. (default: None)
                    )

## To set custom label colors:
ner_vis.set_label_colors({'LOC':'#800080', 'PER':'#77b5fe'}) #set label colors by specifying hex codes

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/ner_viz.png)

#### Entity Resolution

```python
from sparknlp_display import EntityResolverVisualizer

er_vis = EntityResolverVisualizer()

pipeline_result = er_light_pipeline.fullAnnotate(text)

er_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
               label_col='entities', #specify the ner result column
               resolution_col = 'resolution',
               document_col='document', #specify the document column (default: 'document')
               save_path='./export.html' # optional - to save viz as html. (default: None)
               )

## To set custom label colors:
er_vis.set_label_colors({'TREATMENT':'#800080', 'PROBLEM':'#77b5fe'}) #set label colors by specifying hex codes

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/er_viz.png)

#### Relation Extraction
```python
from sparknlp_display import RelationExtractionVisualizer

re_vis = RelationExtractionVisualizer()

pipeline_result = re_light_pipeline.fullAnnotate(text)

re_vis.display(pipeline_result[0], #should be the results of a single example, not the complete dataframe
               relation_col = 'relations', #specify relations column
               document_col = 'document', #specify document column
               show_relations=True, #display relation names on arrows (default: True)
               save_path='./export.html' # optional - to save viz as html. (default: None)
               )

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/re_viz.png)

#### Assertion Status
```python
from sparknlp_display import AssertionVisualizer

assertion_vis = AssertionVisualizer()

pipeline_result = ner_assertion_light_pipeline.fullAnnotate(text)

assertion_vis.display(pipeline_result[0], 
                      label_col = 'entities', #specify the ner result column
                      assertion_col = 'assertion', #specify assertion column
                      document_col = 'document', #specify the document column (default: 'document')
                      save_path='./export.html' # optional - to save viz as html. (default: None)
                      )
                      
## To set custom label colors:
assertion_vis.set_label_colors({'TREATMENT':'#008080', 'problem':'#800080'}) #set label colors by specifying hex codes

```

![title](https://raw.githubusercontent.com/JohnSnowLabs/spark-nlp-display/main/assets/assertion_viz.png)




%prep
%autosetup -n spark-nlp-display-4.4

%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-spark-nlp-display -f filelist.lst
%dir %{python3_sitelib}/*

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

%changelog
* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 4.4-1
- Package Spec generated