How to use the streamlit.sidebar.slider function in streamlit

def object_detector_ui():
    st.sidebar.markdown("# Model")
    confidence_threshold = st.sidebar.slider("Confidence threshold", 0.0, 1.0, 0.5, 0.01)
    overlap_threshold = st.sidebar.slider("Overlap threshold", 0.0, 1.0, 0.3, 0.01)
    return confidence_threshold, overlap_threshold

#filename = st.sidebar.text_input('Enter the filename of a csv-file.')

    agree = st.sidebar.checkbox('Display raw data.')
    if agree:
        st.dataframe(data)
    samples = data.transcription
    text_labels  = [label_name.lower() for label_name in data.medical_specialty]
    labels = enc.fit_transform(np.array(text_labels))
    labels = np.ravel(labels)
    unique_values, counts = np.unique(labels, return_counts=True)
    relative_counts = counts/np.sum(counts)
    st.write("The initial data set contains",np.shape(unique_values)[0],"classes and",data.shape[0],"samples.")

    # EXTRACT SAMPLES AND LABELS.
    st.sidebar.header("Preprocessing class distributions.")
    treshhold_to_consider = st.sidebar.slider("Minimum fraction of class in data set in order to be considered.", min_value=0.01, max_value=0.1, value=0.02, step=0.01)
    classes_to_consider = unique_values[relative_counts>=treshhold_to_consider]

    index_to_consider = np.empty((labels.shape[0]),dtype="bool")
    for i,label in enumerate(labels):
        if label in classes_to_consider:
            index_to_consider[i] = True
        else:
            index_to_consider[i] = False

    # EXTRACT CLASSES
    labels = labels[index_to_consider]
    samples = samples[index_to_consider]
    unique_values, counts = np.unique(labels, return_counts=True)
    relative_counts = counts/np.sum(counts)
    label_names = enc.inverse_transform(unique_values)

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")
    if sma:
        period = st.sidebar.slider(
            "SMA period", min_value=5, max_value=500, value=20, step=1
        )
        data[f"SMA {period}"] = data["Adj Close"].rolling(period).mean()
        data2[f"SMA {period}"] = data[f"SMA {period}"].reindex(data2.index)

def sidebar_settings():
    """Add selection section for setting setting the max-width and padding
    of the main block container"""
    st.sidebar.header("Bokeh Experiments")
    max_width_100_percent = st.sidebar.checkbox("Max-width?", False)
    if not max_width_100_percent:
        max_width = st.sidebar.slider("Select max-width in px", 100, 2000, 1200, 100)
    else:
        max_width = 1200

    _set_block_container_style(max_width, max_width_100_percent)

# Show plots
plot_menu_title = st.sidebar.markdown('### Charts')
plot_menu_text = st.sidebar.text('Select which charts you want to see')
show_absolute_plot = sidebar_menus('absolute')
show_seasonal_decompose = sidebar_menus('seasonal')
show_adfuller_test = sidebar_menus('adfuller')
show_train_prediction = sidebar_menus('train_predictions')
show_test_prediction = sidebar_menus('test_predictions')
force_transformation = sidebar_menus('force_transformations') # You can force a transformation technique

difference_size = None
seasonal_difference_size = None

if ('Custom Difference') in force_transformation:
    # If the user selects a custom transformation, enable the difference options
    difference_size = st.sidebar.slider('Difference size: ', 0, 30, 1)
    seasonal_difference_size = st.sidebar.slider('Seasonal Difference size: ', 0, 30, 1)

plot_adfuller_result = False
if show_adfuller_test:
    plot_adfuller_result = True

# Transform DataFrame to a Series
df = transform_time_series(df, ds_column, data_frequency, y)

# Show the historical plot?
if show_absolute_plot:
    st.markdown('# Historical data ')
    df[y].plot(color='green')
    plt.title('Absolute historical data')
    st.pyplot()

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")
    if sma:
        period = st.sidebar.slider(
            "SMA period", min_value=5, max_value=500, value=20, step=1
        )
        data[f"SMA {period}"] = data["Adj Close"].rolling(period).mean()
        data2[f"SMA {period}"] = data[f"SMA {period}"].reindex(data2.index)

    sma2 = st.sidebar.checkbox("SMA2")
    if sma2:
        period2 = st.sidebar.slider(
            "SMA2 period", min_value=5, max_value=500, value=100, step=1
        )
        data[f"SMA2 {period2}"] = data["Adj Close"].rolling(period2).mean()
        data2[f"SMA2 {period2}"] = data[f"SMA2 {period2}"].reindex(data2.index)

    st.subheader("Chart")
    st.line_chart(data2)

def main():

    st.title('Crawling and Rendering in Python')

    st.sidebar.markdown('## Indexing Options')
    i_type       = st.sidebar.radio('Term Frequency type?',('bm25', 'tfidf'))
    title_boost  = st.sidebar.slider('How much of a boost to give titles?', 1, 5, 2, 1)

    st.sidebar.markdown('## Search Options')
    search_query    = st.sidebar.text_input('Search Query', '')
    sim_weight      = st.sidebar.slider('How much weight to give to term similarity?', 0.0, 1.0, 0.5, 0.1)
    pr_weight       =  st.sidebar.slider('How much weight to give to PageRank?', 0.0, 1.0, 0.5, 0.1)
    bert_weight     = st.sidebar.slider('How much weight to give to bert?', 0.0, 1.0, 0.5, 0.1)


    st.markdown('## Crawling')
    # Crawling (First Wave)
    crawler = crawl_data(cfg.crawler_seed)

    st.markdown('## Rendering')
    # Rendering (Second Wave)
    crawler = render_data(crawler)

    st.markdown('## Indexing')
    # Build the index
    indexer = index_data(crawler, i_type, title_boost)

plot_menu_title = st.sidebar.markdown('### Charts')
plot_menu_text = st.sidebar.text('Select which charts you want to see')
show_absolute_plot = sidebar_menus('absolute')
show_seasonal_decompose = sidebar_menus('seasonal')
show_adfuller_test = sidebar_menus('adfuller')
show_train_prediction = sidebar_menus('train_predictions')
show_test_prediction = sidebar_menus('test_predictions')
force_transformation = sidebar_menus('force_transformations') # You can force a transformation technique

difference_size = None
seasonal_difference_size = None

if ('Custom Difference') in force_transformation:
    # If the user selects a custom transformation, enable the difference options
    difference_size = st.sidebar.slider('Difference size: ', 0, 30, 1)
    seasonal_difference_size = st.sidebar.slider('Seasonal Difference size: ', 0, 30, 1)

plot_adfuller_result = False
if show_adfuller_test:
    plot_adfuller_result = True

# Transform DataFrame to a Series
df = transform_time_series(df, ds_column, data_frequency, y)

# Show the historical plot?
if show_absolute_plot:
    st.markdown('# Historical data ')
    df[y].plot(color='green')
    plt.title('Absolute historical data')
    st.pyplot()

# Show decomposition plot

def object_detector_ui():
    st.sidebar.markdown("# Model")
    confidence_threshold = st.sidebar.slider("Confidence threshold", 0.0, 1.0, 0.5, 0.01)
    overlap_threshold = st.sidebar.slider("Overlap threshold", 0.0, 1.0, 0.3, 0.01)
    return confidence_threshold, overlap_threshold

def main():
    """A Reactive View of the KickstarterDashboard"""
    kickstarter_df = get_kickstarter_df()
    kickstarter_dashboard = KickstarterDashboard(kickstarter_df=kickstarter_df)
    st.markdown(__doc__)
    st.info(INFO)

    options = get_categories()
    categories_selected = st.multiselect("Select Categories", options=options)
    if not categories_selected and kickstarter_dashboard.categories:
        kickstarter_dashboard.categories = []
    else:
        kickstarter_dashboard.categories = categories_selected

    st.sidebar.title("Selections")
    x_range = st.sidebar.slider("Select create_at range", 2009, 2018, (2009, 2018))
    y_range = st.sidebar.slider("Select usd_pledged", 0.0, 5.0, (0.0, 5.0))
    filter_df = KickstarterDashboard.filter_on_categories(kickstarter_df, categories_selected)
    filter_df = kickstarter_dashboard.filter_on_ranges(
        filter_df, (pd.Timestamp(x_range[0], 1, 1), pd.Timestamp(x_range[1], 12, 31)), y_range
    )
    kickstarter_dashboard.scatter_df = filter_df

    st.bokeh_chart(hv.render(kickstarter_dashboard.scatter_plot_view()))
    st.bokeh_chart(hv.render(kickstarter_dashboard.bar_chart_view()))