Streamlit + spaCy

Install

pip install streamlit
pip install spacy
python -m spacy download en_core_web_sm
python -m spacy download en_core_web_md
python -m spacy download de_core_news_sm

streamlit_spacy.py

import streamlit as st
import spacy
from spacy import displacy
import pandas as pd


SPACY_MODEL_NAMES = ["en_core_web_sm", "en_core_web_md", "de_core_news_sm"]
DEFAULT_TEXT = "Mark Zuckerberg is the CEO of Facebook."
HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>"""


@st.cache(ignore_hash=True)
def load_model(name):
    return spacy.load(name)


@st.cache(ignore_hash=True)
def process_text(model_name, text):
    nlp = load_model(model_name)
    return nlp(text)


st.sidebar.title("Interactive spaCy visualizer")
st.sidebar.markdown(
    """
Process text with [spaCy](https://spacy.io) models and visualize named entities,
dependencies and more. Uses spaCy's built-in
[displaCy](http://spacy.io/usage/visualizers) visualizer under the hood.
"""
)

spacy_model = st.sidebar.selectbox("Model name", SPACY_MODEL_NAMES)
model_load_state = st.info(f"Loading model '{spacy_model}'...")
nlp = load_model(spacy_model)
model_load_state.empty()

text = st.text_area("Text to analyze", DEFAULT_TEXT)
doc = process_text(spacy_model, text)

if "parser" in nlp.pipe_names:
    st.header("Dependency Parse & Part-of-speech tags")
    st.sidebar.header("Dependency Parse")
    split_sents = st.sidebar.checkbox("Split sentences", value=True)
    collapse_punct = st.sidebar.checkbox("Collapse punctuation", value=True)
    collapse_phrases = st.sidebar.checkbox("Collapse phrases")
    compact = st.sidebar.checkbox("Compact mode")
    options = {
        "collapse_punct": collapse_punct,
        "collapse_phrases": collapse_phrases,
        "compact": compact,
    }
    docs = [span.as_doc() for span in doc.sents] if split_sents else [doc]
    for sent in docs:
        html = displacy.render(sent, options=options)
        # Double newlines seem to mess with the rendering
        html = html.replace("\n\n", "\n")
        if split_sents and len(docs) > 1:
            st.markdown(f"> {sent.text}")
        st.write(HTML_WRAPPER.format(html), unsafe_allow_html=True)

if "ner" in nlp.pipe_names:
    st.header("Named Entities")
    st.sidebar.header("Named Entities")
    default_labels = ["PERSON", "ORG", "GPE", "LOC"]
    labels = st.sidebar.multiselect(
        "Entity labels", nlp.get_pipe("ner").labels, default_labels
    )
    html = displacy.render(doc, style="ent", options={"ents": labels})
    # Newlines seem to mess with the rendering
    html = html.replace("\n", " ")
    st.write(HTML_WRAPPER.format(html), unsafe_allow_html=True)
    attrs = ["text", "label_", "start", "end", "start_char", "end_char"]
    if "entity_linker" in nlp.pipe_names:
        attrs.append("kb_id_")
    data = [
        [str(getattr(ent, attr)) for attr in attrs]
        for ent in doc.ents
        if ent.label_ in labels
    ]
    df = pd.DataFrame(data, columns=attrs)
    st.dataframe(df)


if "textcat" in nlp.pipe_names:
    st.header("Text Classification")
    st.markdown(f"> {text}")
    df = pd.DataFrame(doc.cats.items(), columns=("Label", "Score"))
    st.dataframe(df)


vector_size = nlp.meta.get("vectors", {}).get("width", 0)
if vector_size:
    st.header("Vectors & Similarity")
    st.code(nlp.meta["vectors"])
    text1 = st.text_input("Text or word 1", "apple")
    text2 = st.text_input("Text or word 2", "orange")
    doc1 = process_text(spacy_model, text1)
    doc2 = process_text(spacy_model, text2)
    similarity = doc1.similarity(doc2)
    if similarity > 0.5:
        st.success(similarity)
    else:
        st.error(similarity)

st.header("Token attributes")

if st.button("Show token attributes"):
    attrs = [
        "idx",
        "text",
        "lemma_",
        "pos_",
        "tag_",
        "dep_",
        "head",
        "ent_type_",
        "ent_iob_",
        "shape_",
        "is_alpha",
        "is_ascii",
        "is_digit",
        "is_punct",
        "like_num",
    ]
    data = [[str(getattr(token, attr)) for attr in attrs] for token in doc]
    df = pd.DataFrame(data, columns=attrs)
    st.dataframe(df)


st.header("JSON Doc")
if st.button("Show JSON Doc"):
    st.json(doc.to_json())

st.header("JSON model meta")
if st.button("Show JSON model meta"):
    st.json(nlp.meta)

Nice example!

The ignore_hash argument has been renamed to allow_output_mutation here. This should be changed in lines 12 and 17.