diff --git a/examples/streamlit_spacy.py b/examples/streamlit_spacy.py
new file mode 100644
index 000000000..f99f7bf18
--- /dev/null
+++ b/examples/streamlit_spacy.py
@@ -0,0 +1,153 @@
+# coding: utf-8
+"""
+Example of a Streamlit app for an interactive spaCy model visualizer. You can
+either download the script, or point streamlit run to the raw URL of this
+file. For more details, see https://streamlit.io.
+
+Installation:
+pip install streamlit
+python -m spacy download en_core_web_sm
+python -m spacy download en_core_web_md
+python -m spacy download de_core_news_sm
+
+Usage:
+streamlit run streamlit_spacy.py
+"""
+from __future__ import unicode_literals
+
+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)