import pandas as pd
import streamlit as st
import streamlit.components.v1 as components
from transformers import AutoModelForTokenClassification, AutoTokenizer, pipeline

from src import GBRT, wikipedia_search, google_search


TYPE = {
    'LOC': ' location',
    'PER': ' person',
    'ORG': ' organization',
    'MISC': ''
}

COLOR = {
    'LOC': '#40E0D0',
    'PER': '#6495ED',
    'ORG': '#CCCCFF',
    'MISC': '#FF7F50'
}

# ---------------------------------------------------------------------------
# Loading models
# ---------------------------------------------------------------------------


@st.cache(allow_output_mutation=True, show_spinner=True)
def load_models():
    # NER
    tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
    bert_ner = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
    tagger = pipeline("token-classification", model=bert_ner, tokenizer=tokenizer,
                      device=0, aggregation_strategy="average")
    # NED
    model = GBRT()
    return model, tagger


# ---------------------------------------------------------------------------
# Page setup
# ---------------------------------------------------------------------------
st.set_page_config(layout="wide", page_title='Named Entity Disambiguation')
st.write("## Named Entity Disambiguation")
col1, col2 = st.columns(2)


# ---------------------------------------------------------------------------
# Candidate Generation
# ---------------------------------------------------------------------------
def get_candidates(mentions_tags):
    candidates = []
    cache = {}
    for mention, tag in mentions_tags:
        if (mention, tag) in cache.keys():
            candidates.append((mention, cache[(mention, tag)]))
        else:
            res1 = google_search(mention + TYPE[tag])
            res2 = wikipedia_search(mention, limit=10)
            cands = list(set(res1 + res2))
            cache[(mention, tag)] = cands
            candidates.append((mention, cands))
    return candidates


# ---------------------------------------------------------------------------
# Rendering Setup
# ---------------------------------------------------------------------------
def display_tag(text, typ, label):
    if label != 'NIL':
        label = "https://en.wikipedia.org/wiki/" + label
    return f"""
    <a style="margin: 0 5px; padding: 2px 4px; border-radius: 4px; text-decoration:none;
              background-color:{COLOR[typ]}; color: white; cursor:pointer" href={label} target="_blank">
        <span style="margin-right:3px">{text}</span>
        <span style="border-style:1px white solid; padding: 2px;">{typ}</span>
    </a>"""


# ---------------------------------------------------------------------------
# Full Pipeline
# ---------------------------------------------------------------------------
def main(text):
    ner_results = tagger(text)
    tagged, last_pos = '', 0

    with st.spinner('Generating Candidates'):
        mentions_cands = get_candidates([(res['word'], res['entity_group']) for res in ner_results])

    with st.spinner('Disambiguating Mentions'):
        preditions = model.link(mentions_cands, text)

    with st.spinner('Rendering Results'):
        for i, res in enumerate(ner_results):
            tag = display_tag(res['word'], res['entity_group'], preditions[i][1])
            tagged += text[last_pos:res['start']] + tag
            last_pos = res['end']
        tagged += text[last_pos:]

    with col2:
        st.write("### Disambiguated Text")
        components.html(f'<p style="line-height: 1.8; margin-top:30px; font-family: sans-serif">{tagged}</p>',
                        scrolling=True, height=500)

    df = pd.DataFrame(data=preditions, columns=['Mention', 'Prediction', 'Confidence'])
    st.write("**Additional Information**")
    st.dataframe(df)


if __name__ == '__main__': 
    model, tagger = load_models()
    with col1:
        st.write("### Input Text")
        user_input = st.text_area('Press Ctrl + Enter to update results',
                                  'George Washington went to Washington.', height=350)
        if user_input:
            main(user_input)