spacy-streamlit: spaCy building blocks for Streamlit apps

This package contains utilities for visualizing spaCy models and building interactive spaCy-powered apps with Streamlit. It includes various building blocks you can use in your own Streamlit app, like visualizers for syntactic dependencies, named entities, text classification, semantic similarity via word vectors, token attributes, and more.

🚀 Quickstart

You can install spacy-streamlit from pip:

pip install spacy-streamlit

The package includes building blocks that call into Streamlit and set up all the required elements for you. You can either use the individual components directly and combine them with other elements in your app, or call the visualize function to embed the whole visualizer.

# streamlit_app.py
import spacy_streamlit

models = ["en_core_web_sm", "en_core_web_md"]
default_text = "Sundar Pichai is the CEO of Google."
spacy_streamlit.visualize(models, default_text)

You can then run your app with streamlit run streamlit_app.py.

📦 Example: `01_out-of-the-box.py`

Use the embedded visualizer with custom settings out-of-the-box.

streamlit run https://raw.githubusercontent.com/explosion/spacy-streamlit/master/examples/01_out-of-the-box.py

👑 Example: `02_custom.py`

Use individual components in your existing app.

streamlit run https://raw.githubusercontent.com/explosion/spacy-streamlit/master/examples/02_custom.py

🎛 API

Visualizer components

These functions can be used in your Streamlit app. They call into streamlit under the hood and set up the required elements.

`function` `visualize`

Embed the full visualizer with selected components.

import spacy_streamlit

models = ["en_core_web_sm", "/path/to/model"]
default_text = "Sundar Pichai is the CEO of Google."
visualizers = ["ner", "textcat"]
spacy_streamlit.visualize(models, default_text, visualizers)

Argument	Type	Description
`models`	List[str]	Names of loadable spaCy models (paths or package names). The models become selectable via a dropdown.
`default_text`	str	Default text to analyze on load. Defaults to `""`.
`visualizers`	List[str]	Names of visualizers to show. Defaults to `["parser", "ner", "textcat", "similarity", "tokens"]`.
`ner_labels`	Optional[List[str]]	NER labels to include. If not set, all labels present in the `"ner"` pipeline component will be used.
`ner_attrs`	List[str]	Span attributes shown in table of named entities. See `visualizer.py` for defaults.
`token_attrs`	List[str]	Token attributes to show in token visualizer. See `visualizer.py` for defaults.
`similarity_texts`	Tuple[str, str]	The default texts to compare in the similarity visualizer. Defaults to `("apple", "orange")`.
`show_json_doc`	bool	Show button to toggle JSON representation of the `Doc`. Defaults to `True`.
`show_model_meta`	bool	Show button to toggle model `meta.json`. Defaults to `True`.
`sidebar_title`	Optional[str]	Title shown in the sidebar. Defaults to `None`.
`sidebar_description`	Optional[str]	Description shown in the sidebar. Accepts Markdown-formatted text.
`show_logo`	bool	Show the spaCy logo in the sidebar. Defaults to `True`.
`color`	Optional[str]	Experimental: Primary color to use for some of the main UI elements (`None` to disable hack). Defaults to `"#09A3D5"`.

`function` `visualize_parser`

Visualize the dependency parse and part-of-speech tags using spaCy's displacy visualizer.

import spacy
from spacy_streamlit import visualize_parser

nlp = spacy.load("en_core_web_sm")
doc = nlp("This is a text")
visualize_parser(doc)

Argument	Type	Description
`doc`	`Doc`	The spaCy `Doc` object to visualize.
keyword-only
`title`	Optional[str]	Title of the visualizer block.
`sidebar_title`	Optional[str]	Title of the config settings in the sidebar.

`function` `visualize_ner`

Visualize the named entities in a Doc using spaCy's displacy visualizer.

import spacy
from spacy_streamlit import visualize_ner

nlp = spacy.load("en_core_web_sm")
doc = nlp("Sundar Pichai is the CEO of Google.")
visualize_ner(doc, labels=nlp.get_pipe("ner").labels)

Argument	Type	Description
`doc`	`Doc`	The spaCy `Doc` object to visualize.
keyword-only
`labels`	Sequence[str]	The labels to show in the labels dropdown.
`attrs`	List[str]	The span attributes to show in entity table.
`show_table`	bool	Whether to show a table of entities and their attributes. Defaults to `True`.
`title`	Optional[str]	Title of the visualizer block.
`sidebar_title`	Optional[str]	Title of the config settings in the sidebar.

`function` `visualize_textcat`

Visualize text categories predicted by a trained text classifier.

import spacy
from spacy_streamlit import visualize_textcat

nlp = spacy.load("./my_textcat_model")
doc = nlp("This is a text about a topic")
visualize_textcat(doc)

Argument	Type	Description
`doc`	`Doc`	The spaCy `Doc` object to visualize.
keyword-only
`title`	Optional[str]	Title of the visualizer block.

`visualize_similarity`

Visualize semantic similarity using the model's word vectors. Will show a warning if no vectors are present in the model.

import spacy
from spacy_streamlit import visualize_similarity

nlp = spacy.load("en_core_web_lg")
visualize_similarity(nlp, ("pizza", "fries"))

Argument	Type	Description
`nlp`	`Language`	The loaded `nlp` object with vectors.
`default_texts`	Tuple[str, str]	The default texts to compare on load. Defaults to `("apple", "orange")`.
keyword-only
`threshold`	float	Threshold for what's considered "similar". If the similarity score is greater than the threshold, the result is shown as similar. Defaults to `0.5`.
`title`	Optional[str]	Title of the visualizer block.

`function` `visualize_tokens`

Visualize the tokens in a Doc and their attributes.

import spacy
from spacy_streamlit import visualize_tokens

nlp = spacy.load("en_core_web_sm")
doc = nlp("This is a text")
visualize_tokens(doc, attrs=["text", "pos_", "dep_", "ent_type_"])

Argument	Type	Description
`doc`	`Doc`	The spaCy `Doc` object to visualize.
keyword-only
`attrs`	List[str]	The names of token attributes to use. See `visualizer.py` for defaults.
`title`	Optional[str]	Title of the visualizer block.

Cached helpers

These helpers attempt to cache loaded models and created Doc objects.

`function` `process_text`

Process a text with a model of a given name and create a Doc object. Calls into the load_model helper to load the model.

import streamlit as st
from spacy_streamlit import process_text

spacy_model = st.sidebar.selectbox("Model name", ["en_core_web_sm", "en_core_web_md"])
text = st.text_area("Text to analyze", "This is a text")
doc = process_text(spacy_model, text)

Argument	Type	Description
`model_name`	str	Loadable spaCy model name. Can be path or package name.
`text`	str	The text to process.
RETURNS	`Doc`	The processed document.

`function` `load_model`

Load a spaCy model from a path or installed package and return a loaded nlp object.

import streamlit as st
from spacy_streamlit import load_model

spacy_model = st.sidebar.selectbox("Model name", ["en_core_web_sm", "en_core_web_md"])
nlp = load_model(spacy_model)

Argument	Type	Description
`name`	str	Loadable spaCy model name. Can be path or package name.
RETURNS	`Language`	The loaded `nlp` object.

discdiver/spacy-streamlit

spacy-streamlit: spaCy building blocks for Streamlit apps

🚀 Quickstart

📦 Example: 01_out-of-the-box.py

👑 Example: 02_custom.py

🎛 API

Visualizer components

function visualize

function visualize_parser

function visualize_ner

function visualize_textcat

visualize_similarity

function visualize_tokens