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ff8e71f46d
* Update streamlit app [ci skip] * Add all labels by default * Tidy up and auto-format Co-authored-by: Ines Montani <ines@ines.io>
154 lines
4.6 KiB
Python
154 lines
4.6 KiB
Python
# coding: utf-8
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"""
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Example of a Streamlit app for an interactive spaCy model visualizer. You can
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either download the script, or point streamlit run to the raw URL of this
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file. For more details, see https://streamlit.io.
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Installation:
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pip install streamlit
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python -m spacy download en_core_web_sm
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python -m spacy download en_core_web_md
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python -m spacy download de_core_news_sm
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Usage:
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streamlit run streamlit_spacy.py
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"""
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from __future__ import unicode_literals
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import streamlit as st
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import spacy
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from spacy import displacy
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import pandas as pd
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SPACY_MODEL_NAMES = ["en_core_web_sm", "en_core_web_md", "de_core_news_sm"]
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DEFAULT_TEXT = "Mark Zuckerberg is the CEO of Facebook."
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HTML_WRAPPER = """<div style="overflow-x: auto; border: 1px solid #e6e9ef; border-radius: 0.25rem; padding: 1rem; margin-bottom: 2.5rem">{}</div>"""
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@st.cache(allow_output_mutation=True)
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def load_model(name):
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return spacy.load(name)
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@st.cache(allow_output_mutation=True)
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def process_text(model_name, text):
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nlp = load_model(model_name)
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return nlp(text)
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st.sidebar.title("Interactive spaCy visualizer")
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st.sidebar.markdown(
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"""
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Process text with [spaCy](https://spacy.io) models and visualize named entities,
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dependencies and more. Uses spaCy's built-in
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[displaCy](http://spacy.io/usage/visualizers) visualizer under the hood.
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"""
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)
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spacy_model = st.sidebar.selectbox("Model name", SPACY_MODEL_NAMES)
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model_load_state = st.info(f"Loading model '{spacy_model}'...")
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nlp = load_model(spacy_model)
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model_load_state.empty()
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text = st.text_area("Text to analyze", DEFAULT_TEXT)
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doc = process_text(spacy_model, text)
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if "parser" in nlp.pipe_names:
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st.header("Dependency Parse & Part-of-speech tags")
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st.sidebar.header("Dependency Parse")
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split_sents = st.sidebar.checkbox("Split sentences", value=True)
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collapse_punct = st.sidebar.checkbox("Collapse punctuation", value=True)
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collapse_phrases = st.sidebar.checkbox("Collapse phrases")
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compact = st.sidebar.checkbox("Compact mode")
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options = {
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"collapse_punct": collapse_punct,
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"collapse_phrases": collapse_phrases,
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"compact": compact,
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}
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docs = [span.as_doc() for span in doc.sents] if split_sents else [doc]
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for sent in docs:
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html = displacy.render(sent, options=options)
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# Double newlines seem to mess with the rendering
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html = html.replace("\n\n", "\n")
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if split_sents and len(docs) > 1:
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st.markdown(f"> {sent.text}")
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st.write(HTML_WRAPPER.format(html), unsafe_allow_html=True)
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if "ner" in nlp.pipe_names:
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st.header("Named Entities")
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st.sidebar.header("Named Entities")
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label_set = nlp.get_pipe("ner").labels
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labels = st.sidebar.multiselect(
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"Entity labels", options=label_set, default=list(label_set)
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)
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html = displacy.render(doc, style="ent", options={"ents": labels})
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# Newlines seem to mess with the rendering
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html = html.replace("\n", " ")
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st.write(HTML_WRAPPER.format(html), unsafe_allow_html=True)
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attrs = ["text", "label_", "start", "end", "start_char", "end_char"]
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if "entity_linker" in nlp.pipe_names:
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attrs.append("kb_id_")
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data = [
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[str(getattr(ent, attr)) for attr in attrs]
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for ent in doc.ents
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if ent.label_ in labels
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]
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df = pd.DataFrame(data, columns=attrs)
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st.dataframe(df)
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if "textcat" in nlp.pipe_names:
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st.header("Text Classification")
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st.markdown(f"> {text}")
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df = pd.DataFrame(doc.cats.items(), columns=("Label", "Score"))
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st.dataframe(df)
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vector_size = nlp.meta.get("vectors", {}).get("width", 0)
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if vector_size:
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st.header("Vectors & Similarity")
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st.code(nlp.meta["vectors"])
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text1 = st.text_input("Text or word 1", "apple")
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text2 = st.text_input("Text or word 2", "orange")
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doc1 = process_text(spacy_model, text1)
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doc2 = process_text(spacy_model, text2)
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similarity = doc1.similarity(doc2)
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if similarity > 0.5:
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st.success(similarity)
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else:
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st.error(similarity)
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st.header("Token attributes")
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if st.button("Show token attributes"):
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attrs = [
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"idx",
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"text",
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"lemma_",
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"pos_",
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"tag_",
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"dep_",
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"head",
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"ent_type_",
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"ent_iob_",
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"shape_",
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"is_alpha",
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"is_ascii",
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"is_digit",
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"is_punct",
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"like_num",
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]
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data = [[str(getattr(token, attr)) for attr in attrs] for token in doc]
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df = pd.DataFrame(data, columns=attrs)
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st.dataframe(df)
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st.header("JSON Doc")
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if st.button("Show JSON Doc"):
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st.json(doc.to_json())
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st.header("JSON model meta")
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if st.button("Show JSON model meta"):
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st.json(nlp.meta)
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