How to use the streamlit.markdown function in streamlit

def write():
    """Used to write the page in the app.py file"""
    with st.spinner("Loading About ..."):
        ast.shared.components.title_awesome(" - About")
        st.markdown(
            """## Contributions

This an open source project and you are very welcome to **contribute** your awesome
comments, questions, resources and apps as
[issues](https://github.com/MarcSkovMadsen/awesome-streamlit/issues) or
[pull requests](https://github.com/MarcSkovMadsen/awesome-streamlit/pulls)
to the [source code](https://github.com/MarcSkovMadsen/awesome-streamlit).

For more details see the [Contribute](https://github.com/marcskovmadsen/awesome-streamlit#contribute) section of the README file.

## The Developer

This project is developed by Marc Skov Madsen. You can learn more about me at
[datamodelsanalytics.com](https://datamodelsanalytics.com).

Feel free to reach out if you wan't to join the project as a developer. You can find my contact details at [datamodelsanalytics.com](https://datamodelsanalytics.com).

if model.hqic <= best_model_hqic:no_of_lower_metrics += 1
                            if no_of_lower_metrics >= 2:
                                best_model_aic = np.round(model.aic,0)
                                best_model_bic = np.round(model.bic,0)
                                best_model_hqic = np.round(model.hqic,0)
                                best_model_order = (p_, d, q_, P_, D, Q_, s)
                                current_best_model = model
                                resid = np.round(np.expm1(current_best_model.resid).mean(), 3)
                                models.append(model)
                                #st.markdown("------------------")
                                #st.markdown("**Best model so far**: SARIMA {}".format(best_model_order)) 
                                #st.markdown("**AIC**: {} **BIC**: {} **HQIC**: {} **Resid**: {}".format(best_model_aic, best_model_bic, best_model_hqic, resid))
                        except:
                            pass
    st.success('Grid Search done!')
    st.markdown('')
    st.markdown('### Best model results')
    st.text(current_best_model.summary())                
    #return current_best_model, models, best_model_order
    return best_model_order

"Caraballo, S.A. (1999) Automatic construction of a hypernym-labeled noun hierarchy. In Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL'99), College Park, Maryland, pp. 120-126.",
        ],
    )

    user_text = st.text_input(label="Enter a citation string", value=text_selected)
    parse_button_clicked = st.button("Parse Citation")


elif model_selected == "I2B2 Clinical Notes Tagging":
    st.title("I2B2 Clinical Notes Tagging")
    st.markdown(
        "Clinical Natural Language Processing helps in identifying salient information from clinical notes."
        "Here, we have trained a neural network model on the **i2b2: Informatics for Integrating Biology and the Bedside** dataset."
        "This dataset has manual annotation for the problems identified, the treatments and tests suggested."
    )
    st.markdown(
        "**MODEL**: We trained a Bi-LSTM model with a CRF on top. We also included Elmo Embedding in the first layer"
    )
    text_selected = st.selectbox(
        label="Select an Example Clinical Note",
        options=[
            "Chest x - ray showed no evidence of cardiomegaly .",
            "Prostrate cancer and Renal failure",
            "Continue with Risperdal as per new psychiatrist",
        ],
    )
    st.markdown("---")
    user_text = st.text_input(label="Enter Clinical Notes", value=text_selected)
    parse_button_clicked = st.button("Parse Clinical Notes")

if parse_button_clicked:
    text_selected = user_text

import streamlit as st
import requests

st.title("Citation Intent Classification")

st.markdown(
    "Identify the intent behind citing another scholarly document helps "
    "in fine-grain analysis of documents. Some citations refer to the "
    "methodology in another document, some citations may refer to other works"
    "for background knowledge and some might compare and contrast their methods with another work. "
    "Citation Intent Classification models classify such intents."
)
st.markdown(
    "**MODEL DESCRIPTION: ** This model is similar to [Arman Cohan et al](https://arxiv.org/pdf/1904.01608.pdf). We do not perform multi-task learning, but include "
    "ELMo Embeddings in the model."
)

st.markdown("---")

st.write("**The Labels can be one of: **")
st.write(
    """<span style="display:inline-block; border: 1px solid #0077B6; border-radius: 5px; padding: 5px; background-color: #0077B6; color: white; margin: 5px;">
        RESULT
    </span>
    <span style="display:inline-block; border: 1px solid #0077B6; border-radius: 5px; padding: 5px; background-color: #0077B6; color: white; margin: 5px;">
        BACKGROUND
    </span>
    <span style="display:inline-block; border: 1px solid #0077B6; border-radius: 5px; padding: 5px; background-color: #0077B6; color: white; margin: 5px;">
        METHOD</span>

def sallery_predictor_component():
    """## Sallery Predictor Component

    A user can input some of his developer features like years of experience and he will get a
    prediction of his sallery
    """
    st.markdown("## App 2: Salary Predictor For Techies")

    experience = st.number_input("Years of Experience")
    test_score = st.number_input("Aptitude Test score")
    interview_score = st.number_input("Interview Score")

    if st.button("Predict"):
        model = get_pickle(MODEL_PKL_FILE)
        features = [experience, test_score, interview_score]
        final_features = [np.array(features)]
        prediction = model.predict(final_features)
        st.balloons()
        st.success(f"Your Salary per anum is: Ghc {prediction[0]:.0f}")

"Visualize the annotations using [displaCy](https://spacy.io/usage/visualizers) "
    "and view stats about the datasets."
)
data_file = st.sidebar.selectbox("Dataset", FILES)
data = load_data(data_file)
n_no_ents = 0
n_total_ents = 0

st.header(f"{data_file} ({len(data)})")
for eg in data:
    row = {"text": eg["text"], "ents": eg.get("spans", [])}
    n_total_ents += len(row["ents"])
    if not row["ents"]:
        n_no_ents += 1
    html = displacy.render(row, **SETTINGS).replace("\n\n", "\n")
    st.markdown(HTML_WRAPPER.format(html), unsafe_allow_html=True)

st.sidebar.markdown(
    f"""
| `{data_file}` | |
| --- | ---: |

def crawl(self, seed):

        self.frontier.enqueue(seed)

        while len(self.frontier):

            # Pull off next url
            url = self.frontier.dequeue()
            status = "Crawling: {}".format(url)
            print(status)
            st.markdown(status)

            url_data = crawl_url(url)

            if url_data and url_data['status'] == 200:

                canonical = url_data['meta']['canonical']

                doc_hash = self.urllookup.update_hashed(url, canonical=canonical)

                doc_data = {'url': url,
                            'domain':url_data['domain'] ,
                            'title': url_data['title'],
                            'description': url_data['meta']['description'],
                            'content': url_data['content'],
                            'html': url_data['html']
                            }

def bert_data(indexer):
    fn = 'bert.pkl'
    loaded = storage.load_pickle(fn)
    if loaded:
        st.markdown('Loaded saved BERT file.')
        return loaded
    indexer.build_bert_embeddings_st()
    storage.save_pickle(fn, indexer.bert)
    return indexer.bert

def streamlit_dataframe(results):
    st.subheader("Streamlit Dataframe (st.dataframe)")
    number_of_rows, number_of_columns, style = select_number_of_rows_and_columns(
        results, key="st.dataframe"
    )
    filter_table = filter_results(results, number_of_rows, number_of_columns, style)
    st.dataframe(filter_table)

    st.markdown(
        """
Pros

import streamlit as st

st.markdown("This **markdown** is awesome! :sunglasses:")

st.markdown("This <b>HTML tag</b> is escaped!")

st.markdown("This <b>HTML tag</b> is not escaped!", unsafe_allow_html=True)

st.markdown("[text]")

st.markdown("[link](href)")

st.markdown("[][]")

st.markdown("Inline math with $\KaTeX$")

st.markdown(
    """
$$