from typing import List

import numpy as np
import pandas as pd
import streamlit as st
from sentence_transformers import SentenceTransformer, util
from st_aggrid import AgGrid, GridOptionsBuilder, JsCode

st.set_page_config(layout='wide')

@st.cache(allow_output_mutation=True)
def load_model():
    """Load pretrained model from SentenceTransformer"""
    return SentenceTransformer('minilm_sbert')

def semantic_search(model: SentenceTransformer,
                    query: str,
                    corpus_embeddings: List) -> pd.DataFrame:
    """Perform semantic search on the corpus"""
    query_embeddings = model.encode(sentences=query,
                                    batch_size=128,
                                    show_progress_bar=False,
                                    convert_to_tensor=True,
                                    normalize_embeddings=True)

    hits = util.semantic_search(query_embeddings,
                                corpus_embeddings,
                                top_k=len(corpus_embeddings),
                                score_function=util.dot_score)

    return pd.DataFrame(hits[0])

def get_similarity_score(model: SentenceTransformer,
                         data: pd.DataFrame,
                         query: str,
                         corpus_embeddings: List) -> pd.DataFrame:
    """Get similarity score for each data point and sort by similarity score and last day"""
    hits = semantic_search(model, query, corpus_embeddings)
    result = pd.merge(data, hits, left_on='ID', right_on='corpus_id')
    result['Last Day'] = pd.to_datetime(result['Last Day'], format='%d/%m/%Y').dt.date
    result.sort_values(by=['score', 'Last Day'], ascending=[False, True], inplace=True)
    return result

@st.cache(ttl=2*3600)
def create_embedding(model: SentenceTransformer,
                     data: pd.DataFrame,
                     key: str) -> List:
    "Maps job title from the corpus to a 384 dimensional vector embeddings"
    corpus_sentences = data[key].astype(str).tolist()
    corpus_embeddings = model.encode(sentences=corpus_sentences,
                                     batch_size=128,
                                     show_progress_bar=False,
                                     convert_to_tensor=True,
                                     normalize_embeddings=True)
    return corpus_embeddings

def load_dataset(columns: List[str]) -> pd.DataFrame:
    """Load real-time dataset from google sheets"""
    sheet_id = '1KeuPPVw9gueNmMrQXk1uGFlY9H1vvhErMLiX_ZVRv_Y'
    sheet_name = 'Form Response 3'.replace(' ', '%20')
    url = f'https://docs.google.com/spreadsheets/d/{sheet_id}/gviz/tq?tqx=out:csv&sheet={sheet_name}'
    data = pd.read_csv(url)
    data  = data.iloc[: , :7]
    data.columns = columns
    data.insert(0, 'ID', range(len(data)))
    data['Full Name'] = data['Full Name'].str.title()
    data['LinkedIn Profile'] = data['LinkedIn Profile'].str.lower()
    data['LinkedIn Profile'] = np.where(data['LinkedIn Profile'].str.startswith('www.linkedin.com'),
                                        "https://" + data['LinkedIn Profile'],
                                        data['LinkedIn Profile'])
    data['LinkedIn Profile'] = np.where(data['LinkedIn Profile'].str.startswith('linkedin.com'),
                                        "https://www." + data['LinkedIn Profile'],
                                        data['LinkedIn Profile'])
    return data

def show_aggrid_table(result: pd.DataFrame):
    """Show interactive table from similarity result"""
    gb = GridOptionsBuilder.from_dataframe(result)
    gb.configure_pagination(paginationAutoPageSize=True)
    gb.configure_side_bar()
    gb.configure_default_column(min_column_width=200)
    gb.configure_selection('multiple', use_checkbox=True, groupSelectsChildren="Group checkbox select children")
    gb.configure_column(field='LinkedIn Profile',
                        headerName='LinkedIn Profile',
                        cellRenderer=JsCode('''function(params) {return `<a href=${params.value} target="_blank">${params.value}</a>`}'''))

    grid_options = gb.build()

    grid_response = AgGrid(
        dataframe=result,
        gridOptions=grid_options,
        height=1100,
        fit_columns_on_grid_load=True,
        data_return_mode='AS_INPUT',
        update_mode='VALUE_CHANGED',
        theme='light',
        enable_enterprise_modules=True,
        allow_unsafe_jscode=True,
    )

def show_heading():
    """Show heading made using streamlit"""
    st.title('@ecommurz Talent Search Engine')
    st.markdown('''
        <div align="left">

        [![Maintainer](https://img.shields.io/badge/maintainer-temandata-blue)](https://temandata.com/)
        [![Open Source? Yes!](https://badgen.net/badge/Open%20Source%20%3F/Yes%21/blue?icon=github)](https://github.com/teman-data/ecommurz-talent-search-engine)
        ![visitor badge](https://visitor-badge.glitch.me/badge?page_id=temandata_ecommurz-talent-search-engine)

        </div>
    ''', unsafe_allow_html=True)
    st.write('This app lets you search and sort talent by job title or relevant job descriptions from ecommurz talent list in real-time.')

def main():
    """Main Function"""
    show_heading()

    columns = ['Timestamp', 'Full Name', 'Company', 'Previous Role',
               'Experience (months)', 'Last Day', 'LinkedIn Profile']
    data = load_dataset(columns)
    model = load_model()
    corpus_embeddings = create_embedding(model, data, 'Previous Role')

    job_title = st.text_input('Insert the job title below:', '')
    submitted = st.button('Submit')

    if submitted:
        print(job_title + ',' + str(pd.Timestamp.now()))

        # Logs data to text file
        with open('logs.txt', 'a', encoding='utf-8') as f:
            f.write(job_title + ',' + str(pd.Timestamp.now()) + '\n')

        st.info(f'Showing most similar results for {job_title}...')
        result = get_similarity_score(model, data, job_title, corpus_embeddings)
        result = result[columns]
        show_aggrid_table(result)

if __name__ == '__main__':
    main()