How to use the streamlit.sidebar function in streamlit

def write():
    """Writes the page in gallery.py"""
    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.
    """
    )
    st.write("Author: [Ines Montani](https://gist.github.com/ines)")
    st.write(
        "Source: [Github](https://gist.github.com/ines/b320cb8441b590eedf19137599ce6685)"
    )

    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()

dset = get_state(config)
    dset.expand = True
    st.title("Dataset Explorer: {}".format(type(dset).__name__))

    input_method = st.sidebar.selectbox(
        "Index selection method", ["Slider", "Number input", "Sample"]
    )
    if input_method == "Slider":
        idx = st.sidebar.slider("Index", 0, len(dset) - 1, 0)
    elif input_method == "Number input":
        idx = st.sidebar.number_input("Index", 0, len(dset) - 1, 0)
    elif input_method == "Sample":
        idx = 0
        if st.sidebar.button("Sample"):
            idx = np.random.choice(len(dset))
        st.sidebar.text("Index: {}".format(idx))

    show_example(dset, idx, config)

    st.header("config")
    cfg_string = pp2mkdtable(config, jupyter_style=True)
    cfg = st.markdown(cfg_string)

@st.cache(allow_output_mutation=True)
def load_vectors(path):
    return Sense2Vec().from_disk(path)


st.sidebar.title("sense2vec")
st.sidebar.markdown(
    "Explore semantic similarities of multi-word phrases using "
    "[`sense2vec`](https://github.com/explosion/sense2vec/)."
)

word = st.sidebar.text_input("Word", DEFAULT_WORD)
sense_dropdown = st.sidebar.empty()
n_similar = st.sidebar.slider("Max number of similar entries", 1, 100, value=20, step=1)
show_senses = st.sidebar.checkbox("Distinguish results by sense")
vectors_path = st.sidebar.selectbox("Vectors", SENSE2VEC_PATHS)

if not vectors_path:
    st.error(
        f"""
#### No vectors available
You can pass one or more paths to this
script on the command line. For example:
```bash
streamlit run {sys.argv[0]} /path/to/sense2vec /path/to/other_sense2vec

""" ) else: s2v = load_vectors(vectors_path)

def intro():
    st.sidebar.success("Select a demo above.")

    st.markdown(
        """
        Streamlit is an open-source app framework built specifically for

components = load_data()
    title = st.empty()
    st.sidebar.title("Options")

    def label(symbol):
        a = components.loc[symbol]
        return symbol + ' - ' + a.Security

    if st.sidebar.checkbox('View companies list'):
        st.dataframe(components[['Security',
                                 'GICS Sector',
                                 'Date first added',
                                 'Founded']])

    st.sidebar.subheader('Select asset')
    asset = st.sidebar.selectbox('Click below to select a new asset',
                                 components.index.sort_values(), index=3,
                                 format_func=label)
    title.title(components.loc[asset].Security)
    if st.sidebar.checkbox('View company info', True):
        st.table(components.loc[asset])
    data0 = load_quotes(asset)
    data = data0.copy().dropna()
    data.index.name = None

    section = st.sidebar.slider('Number of quotes', min_value=30,
                        max_value=min([2000, data.shape[0]]),
                        value=500,  step=10)

    data2 = data[-section:]['Adj Close'].to_frame('Adj Close')

    sma = st.sidebar.checkbox('SMA')

"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")
    label_set = nlp.get_pipe("ner").labels
    labels = st.sidebar.multiselect("Entity labels", label_set, label_set)
    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)

"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
        ]

def process_text(model_name, text):
    nlp = load_model(model_name)
    print ("model loaded!")
    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,

st.write(
        "Source: [Github](https://gist.github.com/ines/b320cb8441b590eedf19137599ce6685)"
    )

    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)