from typing import Any

import pandas as pd
import streamlit as st
from spacy import displacy

import edsnlp
import edsnlp.pipes as eds
from edsnlp.utils.filter import filter_spans

DEFAULT_TEXT = """\
Motif :
Le patient est admis le 29 août pour des difficultés respiratoires.

Antécédents familiaux :
Le père du patient n'est pas asthmatique.

HISTOIRE DE LA MALADIE
Le patient dit avoir de la toux depuis trois jours. \
Elle a empiré jusqu'à nécessiter un passage aux urgences.
A noter deux petits kystes bénins de 1 et 2cm biopsiés en 2005.

Priorité: 2 (établie par l'IAO à l'entrée)

adicaps ABCD0A12 et ABCD0A13

Conclusion
Possible infection au coronavirus. Prescription de paracétomol pour la fièvre.\
"""

REGEX = """
# RegEx and terms matcher
nlp.add_pipe(
    eds.matcher(
        regex=dict(custom=r"{custom_regex}"),
        attr="NORM",
    ),
)
"""

CODE = """
import edsnlp, edsnlp.pipes as eds

# Declare the pipeline
nlp = edsnlp.blank("eds")

# General-purpose components
nlp.add_pipe(eds.normalizer())
nlp.add_pipe(eds.sentences())
{pipes}
# Qualifier pipes
nlp.add_pipe(eds.negation())
nlp.add_pipe(eds.family())
nlp.add_pipe(eds.hypothesis())
nlp.add_pipe(eds.rspeech())

# Define the note text
text = {text}

# Apply the pipeline
doc = nlp(text)

# Explore matched elements
doc.ents
"""

PIPES = {
    "Drugs": "drugs",
    "CIM10": "cim10",
    "Dates": "dates",
    "Quantities": "quantities",
    "Charlson": "charlson",
    "SOFA": "sofa",
    "Elston & Ellis": "elston_ellis",
    "TNM": "tnm",
    "Priority": "emergency_priority",
    "CCMU": "emergency_ccmu",
    "GEMSA": "emergency_gemsa",
    "Covid": "covid",
    "Adicap": "adicap",
    "Diabetes": "diabetes",
    "Tobacco": "tobacco",
    "AIDS": "aids",
    "Lymphoma": "lymphoma",
    "Leukemia": "leukemia",
    "Solid Tumor": "solid_tumor",
    "CKD": "ckd",
    "Hemiplegia": "hemiplegia",
    "Liver Disease": "liver_disease",
    "Peptic Ulcer Disease": "peptic_ulcer_disease",
    "Connective Tissue Disease": "connective_tissue_disease",
    "COPD": "copd",
    "Dementia": "dementia",
    "Cerebrovascular Accident": "cerebrovascular_accident",
    "Peripheral Vascular Disease": "peripheral_vascular_disease",
    "Congestive Heart Failure": "congestive_heart_failure",
    "Myocardial Infarction": "myocardial_infarction",
    "Alcohol": "alcohol",
}


@st.cache_resource()
def load_model(custom_regex: str, **enabled):
    pipes = []

    # Declare the pipeline
    nlp = edsnlp.blank("eds")
    nlp.add_pipe(eds.normalizer())
    nlp.add_pipe(eds.sentences())

    for title, name in PIPES.items():
        if name == "drugs":
            if enabled["drugs"]:
                if enabled["fuzzy_drugs"]:
                    nlp.add_pipe(eds.drugs(term_matcher="simstring"))
                    pipes.append('nlp.add_pipe(eds.drugs(term_matcher="simstring"))')
                else:
                    nlp.add_pipe(eds.drugs())
                    pipes.append("nlp.add_pipe(eds.drugs())")

        else:
            if enabled[name]:
                nlp.add_pipe(f"eds.{name}")
                pipes.append(f"nlp.add_pipe(eds.{name}())")

    if pipes:
        pipes.insert(0, "# Entity extraction pipes")

    if custom_regex:
        nlp.add_pipe(
            eds.matcher(
                regex=dict(custom=custom_regex),
                attr="NORM",
            ),
        )

        regex = REGEX.format(custom_regex=custom_regex)

    else:
        regex = ""

    nlp.add_pipe(eds.negation())
    nlp.add_pipe(eds.family())
    nlp.add_pipe(eds.hypothesis())
    nlp.add_pipe(eds.rspeech())

    return nlp, pipes, regex


st.set_page_config(
    page_title="EDS-NLP Demo",
    page_icon="📄",
)

st.title("EDS-NLP")

st.warning(
    "You should **not** put sensitive data in the example, as this application "
    "**is not secure**."
)

st.sidebar.header("About")
st.sidebar.markdown(
    "EDS-NLP is a contributive effort maintained by AP-HP's Data Science team. "
    "Have a look at the "
    "[documentation](https://aphp.github.io/edsnlp/) for "
    "more information on the available components."
)

st.sidebar.header("Pipeline")
st.sidebar.markdown(
    "This example runs a simplistic pipeline detecting a few synonyms for "
    "COVID-related entities.\n\n"
    "You can add or remove pre-defined pipeline components, and see how "
    "the pipeline reacts. You can also search for your own custom RegEx."
)

st.sidebar.header("Custom RegEx")
st_custom_regex = st.sidebar.text_input(
    "Regular Expression:",
    r"asthmatique|difficult[ée]s?\srespiratoires?",
)
st.sidebar.markdown("The RegEx you defined above is detected under the `custom` label.")

st.sidebar.subheader("Pipeline Components")
st_pipes = {}

st_pipes["cim10"] = st.sidebar.checkbox("CIM10 (loading can be slow)", value=False)
st_drugs_container = st.sidebar.columns([1, 2])
st_pipes["drugs"] = st_drugs_container[0].checkbox("Drugs", value=True)
st_fuzzy_drugs = st_drugs_container[1].checkbox(
    "Fuzzy drugs search", value=True, disabled=not st_pipes["drugs"]
)
for title, name in PIPES.items():
    if name == "drugs" or name == "cim10":
        continue
    st_pipes[name] = st.sidebar.checkbox(title, value=True)
st.sidebar.markdown(
    "These are just a few of the components provided out-of-the-box by EDS-NLP. "
    "See the [documentation](https://aphp.github.io/edsnlp/latest/pipes/) "
    "for detail."
)

model_load_state = st.info("Loading model...")

nlp, pipes, regex = load_model(
    fuzzy_drugs=st_fuzzy_drugs,
    custom_regex=st_custom_regex,
    **st_pipes,
)

model_load_state.empty()

st.header("Enter a text to analyse:")
text = st.text_area(
    "Modify the following text and see the pipeline react :",
    DEFAULT_TEXT,
    height=375,
)

doc = nlp(text)
doc.ents = filter_spans(
    (*doc.ents, *doc.spans.get("dates", []), *doc.spans.get("quantities", []))
)

st.header("Visualisation")

st.markdown(
    "The pipeline extracts simple entities using a dictionnary of RegEx (see the "
    "[Export the pipeline section](#export-the-pipeline) for more information)."
)

category20 = [
    "#1f77b4",
    "#aec7e8",
    "#ff7f0e",
    "#ffbb78",
    "#2ca02c",
    "#98df8a",
    "#d62728",
    "#ff9896",
    "#9467bd",
    "#c5b0d5",
    "#8c564b",
    "#c49c94",
    "#e377c2",
    "#f7b6d2",
    "#7f7f7f",
    "#c7c7c7",
    "#bcbd22",
    "#dbdb8d",
    "#17becf",
    "#9edae5",
]

labels = [
    "date",
    "covid",
    "drug",
    "cim10",
    "emergency_priority",
    "sofa",
    "charlson",
    "size",
    "weight",
    "adicap",
]

colors = {label: cat for label, cat in zip(labels, category20)}
colors["custom"] = "linear-gradient(90deg, #aa9cfc, #fc9ce7)"
options = {
    "colors": colors,
}

html = displacy.render(doc, style="ent", options=options)
html = html.replace("line-height: 2.5;", "line-height: 2.25;")
html = (
    '<div style="padding: 10px; border: solid 2px; border-radius: 10px; '
    f'border-color: #afc6e0;">{html}</div>'
)
st.write(html, unsafe_allow_html=True)

data = []
for ent in doc.ents:
    d = dict(
        start=ent.start_char,
        end=ent.end_char,
        text=ent.text,
        label=ent.label_,
        normalized_value=str(ent._.value or ""),
        negation="YES" if ent._.negation else "NO",
        family="YES" if ent._.family else "NO",
        hypothesis="YES" if ent._.hypothesis else "NO",
        reported_speech="YES" if ent._.reported_speech else "NO",
    )

    data.append(d)

st.header("Entity qualification")


def color_qualifiers(val: Any) -> str:
    """
    Add color to qualifiers.

    Parameters
    ----------
    val : Any
        DataFrame value

    Returns
    -------
    str
        style
    """
    if val == "NO":
        return "color: #dc3545;"
    elif val == "YES":
        return "color: #198754;"
    return ""


if data:
    df = pd.DataFrame.from_records(data)
    df.normalized_value = df.normalized_value.replace({"None": ""})

    df = df.style.applymap(color_qualifiers)

    st.dataframe(df)

else:
    st.markdown("You pipeline did not match any entity...")

pipes_text = ""

if pipes:
    pipes_text += "\n" + "\n".join(pipes) + "\n"
if regex:
    pipes_text += regex

code = CODE.format(
    pipes=pipes_text,
    text=f'"""\n{text}\n"""',
)

st.header("Export the pipeline")
st.markdown(
    "The code below recreates the pipeline. Copy and paste it "
    "in a Jupyter Notebook to interact with it."
)
with st.expander("Show the runnable code"):
    st.markdown(f"```python\n{code}\n```\n\nThis code runs as is.")