import os
import datetime
import requests
import textwrap
from offres_emploi import Api
from offres_emploi.utils import dt_to_str_iso
from dash import Dash, html, dcc, callback, Output, Input, dash_table, State, _dash_renderer, clientside_callback
import dash_bootstrap_components as dbc
import plotly.express as px
import plotly.graph_objects as go
import dash_mantine_components as dmc
from dash_iconify import DashIconify
import pandas as pd
from dotenv import load_dotenv
_dash_renderer._set_react_version("18.2.0")
import plotly.io as pio
from langchain_community.llms import HuggingFaceEndpoint
from langchain_core.prompts import ChatPromptTemplate, PromptTemplate
from langchain.schema.output_parser import StrOutputParser
from pinecone import Pinecone
from bs4 import BeautifulSoup

from flask import Flask

server = Flask(__name__)
# external JavaScript files
external_scripts = [
    'https://datacipen-eventia.hf.space/copilot/index.js'
]
# Create a customized version of the plotly_dark theme with a modified background color
custom_plotly_dark_theme = {
    "layout": {
        "paper_bgcolor": "#1E1E1E",  # Update the paper background color
        "plot_bgcolor": "#1E1E1E",   # Update the plot background color
        "font": {
            "color": "#FFFFFF"       # Update the font color
        },
        "xaxis": {
            "gridcolor": "#333333",  # Update the x-axis grid color
            "zerolinecolor": "#666666"  # Update the x-axis zero line color
        },
        "yaxis": {
            "gridcolor": "#333333",  # Update the y-axis grid color
            "zerolinecolor": "#666666"  # Update the y-axis zero line color
        }
    }
}

# Apply the customized theme to your Plotly figures
pio.templates["custom_plotly_dark"] = custom_plotly_dark_theme
pio.templates.default = "custom_plotly_dark"

load_dotenv()

def removeTags(all):
  for data in all(['style', 'script']):
    data.decompose()
  return ''.join(all.stripped_strings)

def htmlToDataframe(htmlTable):
  data = []
  list_header = []
  soup = BeautifulSoup(htmlTable,'html.parser')
  header = soup.find_all("table")[0].find("tr")
  for items in header:
    try:
      list_header.append(items.get_text())
    except:
      continue
  HTML_data = soup.find_all("table")[0].find_all("tr")[1:]
  for element in HTML_data:
    sub_data = []
    for sub_element in element:
      try:
        sub_data.append(sub_element.get_text())
      except:
          continue
    data.append(sub_data)
  dataFrame = pd.DataFrame(data = data, columns = list_header)
  return dataFrame

def getSavoirFaireFromHTMLMetier(url):
  response = requests.get(url)
  soup = BeautifulSoup(response.text, "html.parser")
  allSavoirFaire = soup.select('ul[data-cy="liste-savoir-faire-metier"] > li')
  if len(allSavoirFaire) != 0:
    allSF = "<table><tr><td>Savoir-faire</td><td>Libelle</td><td>Type</td><td>Categorie</td></tr>"
    for i in range(0,len(allSavoirFaire)):
      blockSavoirFaire = allSavoirFaire[i]
      try:
        soupSavoirFaire = BeautifulSoup(str(blockSavoirFaire), "html.parser")
        titleSavoirFaire = soupSavoirFaire.select('h4.fm-block-form-title')
        descriptSavoirFaire = soupSavoirFaire.select('div.fm-block-form-collapse-content')
        if removeTags(titleSavoirFaire[0]) != None:
          for j in range(0,len(descriptSavoirFaire)):
            ssblockSavoirFaire = descriptSavoirFaire[j]
            soupssSavoirFaire = BeautifulSoup(str(ssblockSavoirFaire), "html.parser")
            sstitleSavoirFaire = soupssSavoirFaire.select('h5.fm-block-form-subtitle')
            listSavoirFaire = soupssSavoirFaire.select('ul.list-unstyled > li')
            if len(listSavoirFaire) != 0:
              for k in range(0,len(listSavoirFaire)):
                blockListSavoirFaire = removeTags(listSavoirFaire[k])
                allSF += "<tr><td>" + removeTags(titleSavoirFaire[0]) + "</td><td>" + blockListSavoirFaire + "</td><td>" + removeTags(sstitleSavoirFaire[0]) + "</td><td>1</td></tr>"
      except:
        print("Pas de Savoir-Faire!")
    allSF += "</table>"
  return allSF

def getSavoirFromHTMLMetier(url):
  response = requests.get(url)
  soup = BeautifulSoup(response.text, "html.parser")
  allSavoirFaire = soup.select('ul[data-cy="liste-savoir-metier"] > li')
  if len(allSavoirFaire) != 0:
    allSF = "<table><tr><td>Savoir-faire</td><td>Libelle</td><td>Categorie</td></tr>"
    for i in range(0,len(allSavoirFaire)):
      blockSavoirFaire = allSavoirFaire[i]
      try:
        soupSavoirFaire = BeautifulSoup(str(blockSavoirFaire), "html.parser")
        titleSavoirFaire = soupSavoirFaire.select('h4.fm-block-form-title')
        descriptSavoirFaire = soupSavoirFaire.select('div.fm-block-form-collapse-content')
        if removeTags(titleSavoirFaire[0]) != None:
          for j in range(0,len(descriptSavoirFaire)):
            ssblockSavoirFaire = descriptSavoirFaire[j]
            soupssSavoirFaire = BeautifulSoup(str(ssblockSavoirFaire), "html.parser")
            listSavoirFaire = soupssSavoirFaire.select('ul.list-unstyled > li')
            if len(listSavoirFaire) != 0:
              for k in range(0,len(listSavoirFaire)):
                blockListSavoirFaire = removeTags(listSavoirFaire[k])
                allSF += "<tr><td>" + removeTags(titleSavoirFaire[0]) + "</td><td>" + blockListSavoirFaire + "</td><td>1</td></tr>"
      except:
        print("Pas de Savoir-Faire!")
    allSF += "</table>"
  return allSF

def getContextFromHTMLMetier(url):
  response = requests.get(url)
  soup = BeautifulSoup(response.text, "html.parser")
  allContext = soup.select('div[data-cy="liste-contextes"] > div.fm-context')
  count = 0
  if len(allContext) != 0:
    allSF = "<table><tr><td>Savoir-faire</td><td>Libelle</td><td>Categorie</td></tr>"
    for i in range(0,len(allContext)):
      count = count + 1
      blockContext = allContext[i]
      try:
        soupContext = BeautifulSoup(str(blockContext), "html.parser")
        titleSavoirFaire = soupContext.select('h3.fm-context-title')
        descriptSavoirFaire = soupContext.select('ul > li')
        if removeTags(titleSavoirFaire[0]) != None:
          for j in range(0,len(descriptSavoirFaire)):
            ssblockSavoirFaire = descriptSavoirFaire[j]
            if len(ssblockSavoirFaire) != 0:
              allSF += "<tr><td>" + removeTags(titleSavoirFaire[0]) + "</td><td>" + removeTags(ssblockSavoirFaire) + "</td><td>1</td></tr>"
      except:
        print("Pas de Savoir-Faire!")
    allSF += "</table>"
  return allSF

def datavisualisation_skills_context(df, template, paper_bgcolor, plot_bgcolor, title_template, codeRome):
    train = df
    array_df = list(df.columns)
    if any(x == "Type" for x in array_df):
        df1 = train.groupby(['Savoir-faire', 'Type'])['Categorie'].count().reset_index()
        df1.columns = ['source', 'target', 'value']
        df2 = train.groupby(['Type', 'Libelle'])['Categorie'].count().reset_index()
        df2.columns = ['source', 'target', 'value']
        all_links = pd.concat([df1, df2], axis=0)
    else:
        df1 = train.groupby(['Savoir-faire', 'Libelle'])['Categorie'].count().reset_index()
        df1.columns = ['source', 'target', 'value']
        all_links = df1

    unique_source_target = list(pd.unique(all_links[['source', 'target']].values.ravel('K')))
    mapping_dict = {k: v for v, k in enumerate(unique_source_target)}

    all_links['source'] = all_links['source'].map(mapping_dict)
    all_links['target'] = all_links['target'].map(mapping_dict)

    links_dict = all_links.to_dict(orient='list')
    #Sankey Diagram Code 
    colors = [
            "blue","blueviolet","brown","burlywood","cadetblue",
            "chartreuse","chocolate","coral","cornflowerblue",
            "cornsilk","crimson","cyan","darkblue","darkcyan",
            "darkgoldenrod","darkgray","darkgrey","darkgreen",
            "darkkhaki","darkmagenta","darkolivegreen","darkorange",
            "darkorchid","darkred","darksalmon","darkseagreen",
            "darkslateblue","darkslategray","darkslategrey",
            "darkturquoise","darkviolet","deeppink","deepskyblue",
            "dimgray","dimgrey","dodgerblue","firebrick",
            "floralwhite","forestgreen","fuchsia","gainsboro",
            "ghostwhite","gold","goldenrod","gray","grey","green",
            "greenyellow","honeydew","hotpink","indianred","indigo",
            "ivory","khaki","lavender","lavenderblush","lawngreen",
            "lemonchiffon","lightblue","lightcoral","lightcyan",
            "lightgoldenrodyellow","lightgray","lightgrey",
            "lightgreen","lightpink","lightsalmon","lightseagreen",
            "lightskyblue","lightslategray","lightslategrey",
            "lightsteelblue","lightyellow", "lime","limegreen",
            "linen","magenta","maroon","mediumaquamarine",
            "mediumblue","mediumorchid","mediumpurple",
            "mediumseagreen","mediumslateblue","mediumspringgreen",
            "mediumturquoise","mediumvioletred","midnightblue",
            "mintcream","mistyrose","moccasin","navajowhite","navy",
            "oldlace","olive","olivedrab","orange","orangered",
            "orchid","palegoldenrod","palegreen","paleturquoise",
            "palevioletred","papayawhip","peachpuff","peru","pink",
            "plum","powderblue","purple","red","rosybrown",
            "royalblue","rebeccapurple","saddlebrown","salmon",
            "sandybrown","seagreen","seashell","sienna","silver",
            "skyblue","slateblue","slategray","slategrey","snow",
            "aliceblue","antiquewhite","aqua","aquamarine","azure",
            "beige","bisque","black","blanchedalmond"
    ]
    array_label_rome = searchByRome(codeRome)
    fig = go.Figure(data=[go.Sankey(
        node = dict(
            pad = 15,
            thickness = 20,
            line = dict(color = "black", width = 0.5),
            label = unique_source_target,
            color = colors
        ),
        link = dict(
            source = links_dict["source"],
            target = links_dict["target"],
            value = links_dict["value"],
            color="lightgrey"
        ))]).update_layout(template=template, paper_bgcolor=paper_bgcolor, plot_bgcolor=plot_bgcolor, title_text=title_template + " du code ROME : " + array_label_rome[0]['label'], font_size=10,width=1000, height=800)
    return fig

def datavisualisation_chiffres_cles_emplois(url):
    response = requests.get(url)
    soup = BeautifulSoup(response.text, "lxml")

    alldemandeurs = ''
    allsalaires = ''
    alldifficultes = ''
    allrepartitions = ''
    allentreprises = ''
    allembauches = soup.select('p.population_category')
    allnumembauchesfirst = soup.select('p.population_main-num.data')
    allnumembauches = removeTags(allnumembauchesfirst[0]).split('\xa0')
    allnumembauches = ''.join(allnumembauches)
    allnumoffres = removeTags(allnumembauchesfirst[1]).split('\xa0')
    allnumoffres = ''.join(allnumoffres)
    alldetailembauches = soup.select('p.hiring_text.ng-star-inserted')
    allnumevolutionembauches = soup.select('p.main.ng-star-inserted')
    alldetailevolutionembauches = soup.select('p.population_bubble-title')
    alldemandeurs = "<table><tr><td>Indicateur</td><td>Valeur</td></tr><tr><td>" + removeTags(allembauches[0]) + " (" + removeTags(alldetailembauches[0]) + ");"
    if len(alldetailevolutionembauches) >= 1 and len(allnumevolutionembauches) >= 1:
        alldemandeurs += "\nÉvolution demandeurs d'emploi (" + removeTags(alldetailevolutionembauches[0]) + ": " + removeTags(allnumevolutionembauches[0]) + ")</td>"
    else:
        alldemandeurs += "</td>"
    alldemandeurs += "<td>" + allnumembauches + "</td></tr>"
    alldemandeurs += "<tr><td>" + removeTags(allembauches[1]) + " (" + removeTags(alldetailembauches[1]) + ");"
    if len(alldetailevolutionembauches) >= 2 and len(allnumevolutionembauches) >= 2:
        alldemandeurs += "\nÉvolution offres d'emploi (" + removeTags(alldetailevolutionembauches[1]) + ": " + removeTags(allnumevolutionembauches[1]) + ")</td>"
    else:
        alldemandeurs += "</td>"
    alldemandeurs += "<td>" + allnumoffres + "</td></tr>"
    alldemandeurs += "</table>"

    allFAP = soup.select('tr.sectorTable__line.ng-star-inserted')
    allcategorie = soup.select('td.sectorTable__cell')
    alltypesalaires = soup.select('th.sectorTable__cell')
    allFAPsalaires = soup.select('p.sectorTable__cellValue')
    if len(allFAPsalaires) >= 3:
        allsalaires = "<table><tr><td>categorie</td><td>emploi</td><td>salaire</td></tr>"
        allsalaires += "<tr><td>" + removeTags(alltypesalaires[1]) + "</td><td>" + removeTags(allcategorie[0]) + "</td><td>" + removeTags(allFAPsalaires[0]).replace('\xa0','').replace(' ','').replace('€','') + "</td></tr>"
        allsalaires += "<tr><td>" + removeTags(alltypesalaires[2]) + "</td><td>" + removeTags(allcategorie[0]) + "</td><td>" + removeTags(allFAPsalaires[1]).replace('\xa0','').replace(' ','').replace('€','') + "</td></tr>"
        allsalaires += "<tr><td>" + removeTags(alltypesalaires[3]) + "</td><td>" + removeTags(allcategorie[0]) + "</td><td>" + removeTags(allFAPsalaires[2]).replace('\xa0','').replace(' ','').replace('€','') + "</td></tr>"
        if len(allFAP) >= 2 and len(allFAPsalaires) == 6:
            allsalaires += "<tr><td>" + removeTags(alltypesalaires[1]) + "</td><td>" + removeTags(allcategorie[4]) + "</td><td>" + removeTags(allFAPsalaires[3]).replace('\xa0','').replace(' ','').replace('€','') + "</td></tr>"
            allsalaires += "<tr><td>" + removeTags(alltypesalaires[2]) + "</td><td>" + removeTags(allcategorie[4]) + "</td><td>" + removeTags(allFAPsalaires[4]).replace('\xa0','').replace(' ','').replace('€','') + "</td></tr>"
            allsalaires += "<tr><td>" + removeTags(alltypesalaires[3]) + "</td><td>" + removeTags(allcategorie[4]) + "</td><td>" + removeTags(allFAPsalaires[5]).replace('\xa0','').replace(' ','').replace('€','') + "</td></tr>"
        allsalaires += "</table>"

    alltypedifficultes = soup.select('.tabs-main-content_persp-col2-bar.ng-star-inserted')
    alldifficulte = soup.select('p.horizontal-graph_title')
    allpcdifficulte = soup.select('div.horizontal-graph_data')
    alldifficultes = "<table><tr><td>Indicateur</td><td>Valeur</td></tr>"
    for i in range(0,len(alltypedifficultes)):
        alldifficultes += "<tr><td>" + removeTags(alldifficulte[i]) + "</td><td>" + removeTags(allpcdifficulte[i]).replace('Pour le territoire principal FRANCE pour les ' + removeTags(alldifficulte[i]),'').replace('%','') + "</td></tr>"
    alldifficultes += "</table>"

    alltyperepartitions = soup.select('div.hiring-contract_legende_item.ng-star-inserted')
    allrepartition = soup.select('p.hiring-contract_legende_item_label')
    allpcrepartition = soup.select('span.hiring-contract_legende_item-first')
    allrepartitions = "<table><tr><td>Indicateur</td><td>Valeur</td></tr>"
    for i in range(0,len(alltyperepartitions)):
        allrepartitions += "<tr><td>" + removeTags(allrepartition[i]).replace('(' + removeTags(allpcrepartition[i]) + ')','') + "</td><td>" + removeTags(allpcrepartition[i]).replace('%','').replace(',','.') + "</td></tr>"
    allrepartitions += "</table>"

    allentrepriserepartitions = soup.select('div.horizontal-graph_pattern.sm-bubble_wrapper > span')
    allentreprise = soup.select('span.sr-only')
    allpcentreprise = soup.select('span.data.ng-star-inserted')
    allentreprises = "<table><tr><td>Indicateur</td><td>Valeur</td></tr>"
    for i in range(0,len(allentrepriserepartitions)):
        allentreprises += "<tr><td>" + removeTags(allentrepriserepartitions[i])[0:-4] + "</td><td>" + removeTags(allentrepriserepartitions[i])[-4:].replace('%','').replace(',','.') + "</td></tr>"
    allentreprises += "</table>"

    return [alldemandeurs, allsalaires, alldifficultes, allrepartitions, allentreprises]

def localisation():
    ListCentroids = [
        { "ID": "01", "Longitude": 5.3245259, "Latitude":46.0666003 },
        { "ID": "02", "Longitude": 3.5960246, "Latitude": 49.5519632 },
        { "ID": "03", "Longitude": 3.065278, "Latitude": 46.4002783 },
        { "ID": "04", "Longitude": 6.2237688, "Latitude": 44.1105837 },
        { "ID": "05", "Longitude": 6.2018836, "Latitude": 44.6630487 },
    	{ "ID": "06", "Longitude": 7.0755745, "Latitude":43.9463082 },
        { "ID": "07", "Longitude": 4.3497308, "Latitude": 44.7626044 },
        { "ID": "08", "Longitude": 4.6234893, "Latitude": 49.6473884 },
        { "ID": "09", "Longitude": 1.6037147, "Latitude": 42.9696091 },
        { "ID": "10", "Longitude": 4.1394954, "Latitude": 48.2963286 },
    	{ "ID": "11", "Longitude": 2.3140163, "Latitude": 43.1111427 },
    	{ "ID": "12", "Longitude": 2.7365234, "Latitude": 44.2786323 },
    	{ "ID": "13", "Longitude": 5.0515492, "Latitude": 43.5539098 },
    	{ "ID": "14", "Longitude": -0.3930779, "Latitude": 49.1024215 },
    	{ "ID": "15", "Longitude": 2.6367657, "Latitude": 44.9643217 },
    	{ "ID": "16", "Longitude": 0.180475, "Latitude": 45.706264 },
    	{ "ID": "17", "Longitude": -0.7082589, "Latitude": 45.7629699 },
    	{ "ID": "18", "Longitude": 2.5292424, "Latitude": 47.0926687 },
    	{ "ID": "19", "Longitude": 1.8841811, "Latitude": 45.3622055 },
    	{ "ID": "2A", "Longitude": 8.9906834, "Latitude": 41.8619761 },
    	{ "ID": "2B", "Longitude": 9.275489, "Latitude": 42.372014 },
    	{ "ID": "21", "Longitude": 4.7870471, "Latitude": 47.4736746 },
    	{ "ID": "22", "Longitude": -2.9227591, "Latitude": 48.408402 },
    	{ "ID": "23", "Longitude": 2.0265508, "Latitude": 46.0837382 },
    	{ "ID": "24", "Longitude": 0.7140145, "Latitude": 45.1489678 },
    	{ "ID": "25", "Longitude": 6.3991355, "Latitude": 47.1879451 },
    	{ "ID": "26", "Longitude": 5.1717552, "Latitude": 44.8055408 },
    	{ "ID": "27", "Longitude": 0.9488116, "Latitude": 49.1460288 },
    	{ "ID": "28", "Longitude": 1.2793491, "Latitude": 48.3330017 },
    	{ "ID": "29", "Longitude": -4.1577074, "Latitude": 48.2869945 },
    	{ "ID": "30", "Longitude": 4.2650329, "Latitude": 43.9636468 },
    	{ "ID": "31", "Longitude": 1.2728958, "Latitude": 43.3671081 },
    	{ "ID": "32", "Longitude": 0.4220039, "Latitude": 43.657141 },
    	{ "ID": "33", "Longitude": -0.5760716, "Latitude": 44.8406068 },
    	{ "ID": "34", "Longitude": 3.4197556, "Latitude": 43.62585 },
    	{ "ID": "35", "Longitude": -1.6443812, "Latitude": 48.1801254 },
    	{ "ID": "36", "Longitude": 1.6509938, "Latitude": 46.7964222 },
    	{ "ID": "37", "Longitude": 0.7085619, "Latitude": 47.2802601 },
    	{ "ID": "38", "Longitude": 5.6230772, "Latitude": 45.259805 },
    	{ "ID": "39", "Longitude": 5.612871, "Latitude": 46.7398138 },
    	{ "ID": "40", "Longitude": -0.8771738, "Latitude": 44.0161251 },
    	{ "ID": "41", "Longitude": 1.3989178, "Latitude": 47.5866519 },
    	{ "ID": "42", "Longitude": 4.2262355, "Latitude": 45.7451186 },
    	{ "ID": "43", "Longitude": 3.8118151, "Latitude": 45.1473029 },
    	{ "ID": "44", "Longitude": -1.7642949, "Latitude": 47.4616509 },
    	{ "ID": "45", "Longitude": 2.2372695, "Latitude": 47.8631395 },
    	{ "ID": "46", "Longitude": 1.5732157, "Latitude": 44.6529284 },
    	{ "ID": "47", "Longitude": 0.4788052, "Latitude": 44.4027215 },
    	{ "ID": "48", "Longitude": 3.4991239, "Latitude": 44.5191573 },
    	{ "ID": "49", "Longitude": -0.5136056, "Latitude": 47.3945201 },
    	{ "ID": "50", "Longitude": -1.3203134, "Latitude": 49.0162072 },
    	{ "ID": "51", "Longitude": 4.2966555, "Latitude": 48.9479636 },
    	{ "ID": "52", "Longitude": 5.1325796, "Latitude": 48.1077196 },
    	{ "ID": "53", "Longitude": -0.7073921, "Latitude": 48.1225795 },
    	{ "ID": "54", "Longitude": 6.144792, "Latitude": 48.7995163 },
    	{ "ID": "55", "Longitude": 5.2888292, "Latitude": 49.0074545 },
    	{ "ID": "56", "Longitude": -2.8746938, "Latitude": 47.9239486 },
    	{ "ID": "57", "Longitude": 6.5610683, "Latitude": 49.0399233 },
    	{ "ID": "58", "Longitude": 3.5544332, "Latitude": 47.1122301 },
    	{ "ID": "59", "Longitude": 3.2466616, "Latitude": 50.4765414 },
    	{ "ID": "60", "Longitude": 2.4161734, "Latitude": 49.3852913 },
    	{ "ID": "61", "Longitude": 0.2248368, "Latitude": 48.5558919 },
    	{ "ID": "62", "Longitude": 2.2555152, "Latitude": 50.4646795 },
    	{ "ID": "63", "Longitude": 3.1322144, "Latitude": 45.7471805 },
    	{ "ID": "64", "Longitude": -0.793633, "Latitude": 43.3390984 },
    	{ "ID": "65", "Longitude": 0.1478724, "Latitude": 43.0526238 },
    	{ "ID": "66", "Longitude": 2.5239855, "Latitude": 42.5825094 },
    	{ "ID": "67", "Longitude": 7.5962225, "Latitude": 48.662515 },
    	{ "ID": "68", "Longitude": 7.2656284, "Latitude": 47.8586205 },
    	{ "ID": "69", "Longitude": 4.6859896, "Latitude": 45.8714754 },
    	{ "ID": "70", "Longitude": 6.1388571, "Latitude": 47.5904191 },
    	{ "ID": "71", "Longitude": 4.6394021, "Latitude": 46.5951234 },
    	{ "ID": "72", "Longitude": 0.1947322, "Latitude": 48.0041421 },
    	{ "ID": "73", "Longitude": 6.4662232, "Latitude": 45.4956055 },
    	{ "ID": "74", "Longitude": 6.3609606, "Latitude": 46.1045902 },
    	{ "ID": "75", "Longitude": 2.3416082, "Latitude": 48.8626759 },
    	{ "ID": "76", "Longitude": 1.025579, "Latitude": 49.6862911 },
    	{ "ID": "77", "Longitude": 2.8977309, "Latitude": 48.5957831 },
    	{ "ID": "78", "Longitude": 1.8080138, "Latitude": 48.7831982 },
    	{ "ID": "79", "Longitude": -0.3159014, "Latitude": 46.5490257 },
    	{ "ID": "80", "Longitude": 2.3380595, "Latitude": 49.9783317 },
    	{ "ID": "81", "Longitude": 2.2072751, "Latitude": 43.8524305 },
    	{ "ID": "82", "Longitude": 1.2649374, "Latitude": 44.1254902 },
    	{ "ID": "83", "Longitude": 6.1486127, "Latitude": 43.5007903 },
    	{ "ID": "84", "Longitude": 5.065418, "Latitude": 44.0001599 },
    	{ "ID": "85", "Longitude": -1.3956692, "Latitude": 46.5929102 },
    	{ "ID": "86", "Longitude": 0.4953679, "Latitude": 46.5719095 },
    	{ "ID": "87", "Longitude": 1.2500647, "Latitude": 45.9018644 },
    	{ "ID": "88", "Longitude": 6.349702, "Latitude": 48.1770451 },
    	{ "ID": "89", "Longitude": 3.5634078, "Latitude": 47.8474664 },
    	{ "ID": "90", "Longitude": 6.9498114, "Latitude": 47.6184394 },
    	{ "ID": "91", "Longitude": 2.2714555, "Latitude": 48.5203114 },
    	{ "ID": "92", "Longitude": 2.2407148, "Latitude": 48.835321 },
    	{ "ID": "93", "Longitude": 2.4811577, "Latitude": 48.9008719 },
    	{ "ID": "94", "Longitude": 2.4549766, "Latitude": 48.7832368 },
    	{ "ID": "95", "Longitude": 2.1802056, "Latitude": 49.076488 },
    	{ "ID": "974", "Longitude": 55.536384, "Latitude": -21.115141 },
    	{ "ID": "973", "Longitude": -53.125782, "Latitude": 3.933889 },
    	{ "ID": "972", "Longitude": -61.024174, "Latitude": 14.641528 },
    	{ "ID": "971", "Longitude": -61.551, "Latitude": 16.265 }
    ]
    
    return ListCentroids

def vectorDatabase_connexion():
    pc = Pinecone(api_key=os.environ['PINECONE_API_KEY'])
    index_name = "all-skills"
    index = pc.Index(index_name)
    return index

def searchByRome(codeRome):
    index = vectorDatabase_connexion()
    allRome = []
    if codeRome:
        all_docs = index.query(
            top_k=1500,
            vector= [0] * 768, # embedding dimension
            namespace='',
            filter={"categorie": {"$eq": "rome"},"rome": {"$eq": codeRome}},
            include_metadata=True
        )
    else:
        all_docs = index.query(
            top_k=1500,
            vector= [0] * 768, # embedding dimension
            namespace='',
            filter={"categorie": {"$eq": "rome"}},
            include_metadata=True
        )
    for refRome in all_docs['matches']:
        allRome.append({"value": refRome['metadata']['rome'], "label": refRome['metadata']['rome'] + " - " + refRome['metadata']['libelle_rome']})
    return sorted(allRome, key=lambda element:element["value"])

def connexion_France_Travail():
    client = Api(client_id=os.getenv('POLE_EMPLOI_CLIENT_ID'),
                client_secret=os.getenv('POLE_EMPLOI_CLIENT_SECRET'))
    return client

def API_France_Travail(romeListArray):
    client = connexion_France_Travail()
    todayDate = datetime.datetime.today()
    month, year = (todayDate.month-1, todayDate.year) if todayDate.month != 1 else (12, todayDate.year-1)
    start_dt = todayDate.replace(day=1, month=month, year=year)
    end_dt = datetime.datetime.today()
    results = []
    for k in romeListArray:
        if k[0:1] == ' ':
            k = k[1:]
        params = {"motsCles": k.replace('/', '').replace('-', '').replace(',', '').replace(' ', ','),'minCreationDate': dt_to_str_iso(start_dt),'maxCreationDate': dt_to_str_iso(end_dt),'range':'0-149'}
        try:
            search_on_big_data = client.search(params=params)
            results += search_on_big_data["resultats"]
        except:
            print("Il n'y a pas d'offres d'emploi.")
        
    results_df = pd.DataFrame(results)
    return results_df

theme_toggle = dmc.Tooltip(
    dmc.ActionIcon(
    [
        dmc.Paper(DashIconify(icon="radix-icons:sun", width=25), darkHidden=True),
        dmc.Paper(DashIconify(icon="radix-icons:moon", width=25), lightHidden=True),
    ],
    variant="transparent",
    color="yellow",
    id="color-scheme-toggle",
    size="lg",
    ms="auto",
    ),
    label="Changez de thème",
    position="left",
    withArrow=True,
    arrowSize=6,
)

styleRefresh = {
    "color": "lightgrey",
    "textDecoration" : "none"
}

styleTitle = {
    "textAlign": "center"
}
styleUSERIA = {
    "textAlign": "right",
    "marginBottom" : "5px"
}
styleSUBMITIA = {
    "marginLeft":"auto",
    "marginRight":"auto",
    "marginTop": "5px",
    "marginBottom" : "5px"
}
styleSYSIA = {
    "marginTop":"10px",
    "marginBottom":"120px",
}
styleTopvar = {
    "display": "none"
}

styleToggle = {
    "marginTop":"25px",
    "textAlign": "right",
}
styleIcon = {
    "marginTop":"10px",
}
styleSubmitBox = {
    "position":"fixed",
    "width": "100%",
    "top": "calc(100vh - 100px)",
    "right": "0"
}
#datadefault = [
#    {"value": "K2105", "label": "K2105"},
#    {"value": "L1101", "label": "L1101"},
#    {"value": "L1202", "label": "L1202"},
#    {"value": "L1507", "label": "L1507"},
#    {"value": "L1508", "label": "L1508"},
#    {"value": "L1509", "label": "L1509"},
#]

def custom_error_handler(err):
# This function defines what we want to happen when an exception occurs
# For now, we just print the exception to the terminal with additional text
    print(f"The app raised the following exception: {err}")
def textbox(text, box="AI", name="Philippe"):
    text = text.replace(f"{name}:", "").replace("You:", "")
    #text = textile.textile(text)
    style = {
        "max-width": "60%",
        "width": "max-content",
        "padding": "5px 10px",
        "border-radius": 25,
        "margin-bottom": 20,
    }

    if box == "user":
        style["margin-left"] = "auto"
        style["margin-right"] = 0

        #return dbc.Card(text, style=style, body=True, color="primary", inverse=True)
        return html.Div(dmc.Button(text, variant="gradient", gradient={"from": "grape", "to": "pink", "deg": 35}), style=styleUSERIA)

    elif box == "AI":
        style["margin-left"] = 0
        style["margin-right"] = "auto"

        thumbnail = html.Img(
            src=app.get_asset_url("sparkles.gif"),
            style={
                "border-radius": 50,
                "height": 36,
                "margin-right": 5,
                "float": "left",
            },
        )
        #textbox = dbc.Card(text, style=style, body=True, color="light", inverse=False)
        #textbox = dmc.Blockquote(text, style=styleSYSIA)
        textbox = dmc.Card(children=[dmc.Text(text,size="sm",c="dimmed")],withBorder=False,w="100%", style=styleSYSIA)
        return html.Div([thumbnail, textbox])

    else:
        raise ValueError("Incorrect option for `box`.")


#description = """
#Philippe is the principal architect at a condo-development firm in Paris. He lives with his girlfriend of five years in a 2-bedroom condo, with a small dog named Coco. Since the pandemic, his firm has seen a  significant drop in condo requests. As such, he’s been spending less time designing and more time on cooking,  his favorite hobby. He loves to cook international foods, venturing beyond French cuisine. But, he is eager  to get back to architecture and combine his hobby with his occupation. That’s why he’s looking to create a  new design for the kitchens in the company’s current inventory. Can you give him advice on how to do that?
#"""

# Authentication
#openai.api_key = os.getenv("OPENAI_KEY")


# Define Layout
conversation = html.Div(
    html.Div(id="display-conversation"),
    style={
        "overflow-y": "auto",
        "display": "flex",
        "height": "calc(100vh - 100px)",
        "flex-direction": "column-reverse",
    },
)

controls = dbc.InputGroup(
    children=[
        dmc.TextInput(id="user-input", placeholder="Ecrire votre requête...", w="400", style=styleSUBMITIA),
        dbc.InputGroupAddon(dmc.Button(leftSection=DashIconify("Envoyer", icon="tabler:send", width=20), id="submit"), addon_type="append", style=styleTitle),
        
        #dbc.Input(id="user-input", placeholder="Ecrire votre requête...", type="text"),
        #dbc.InputGroupAddon(dbc.Button("Submit", id="submit"), addon_type="append"),
    ],style=styleSubmitBox
)
class CustomDash(Dash):
    def interpolate_index(self, **kwargs):
        # Inspect the arguments by printing them
        return '''
        <!DOCTYPE html>
        <html>
            <head>
                <title>Dashboard des compétences</title>
            </head>
            <body>

                <div id="custom-topbar"></div>
                {app_entry}
                {config}
                {scripts}
                {renderer}
                <div id="custom-footer"></div>
            </body>
        </html>
        '''.format(
            app_entry=kwargs['app_entry'],
            config=kwargs['config'],
            scripts=kwargs['scripts'],
            renderer=kwargs['renderer'])
        
#app = Dash(__name__, external_scripts=external_scripts, external_stylesheets=dmc.styles.ALL, on_error=custom_error_handler)
app = CustomDash(__name__, server=server, external_scripts=external_scripts, external_stylesheets=dmc.styles.ALL, on_error=custom_error_handler)

app.layout = dmc.MantineProvider(
    [
        html.Div(
            children=[
                dmc.Container(
                    children=[
                        dmc.Grid(
                            children=[
                                dmc.GridCol(html.Div(
                                    children=[
                                    dmc.MultiSelect(
                                        placeholder="Selectionnez vos Codes ROME",
                                        id="framework-multi-select",
                                        value=['K2105', 'L1101', 'L1202', 'L1507', 'L1508', 'L1509'],
                                        data=searchByRome(''),
                                        w=600,
                                        mt=10,
                                        styles={
                                            "input": {"borderColor": "grey"},
                                            "label": {"color": dmc.DEFAULT_THEME["colors"]["orange"][4]},
                                        },
                                    ),
                                    dmc.Drawer(
                                            title="Mistral répond à vos questions sur les datas de l'emploi et des compétences.",
                                            children=[dbc.Container(
                                                            fluid=False,
                                                            children=[
                                                                dcc.Store(id="store-conversation", data=""),
                                                                html.Div(dmc.Button("Bonjour, Mistral est à votre écoute!", variant="gradient", gradient={"from": "grape", "to": "pink", "deg": 35}), style=styleUSERIA),
                                                                conversation,
                                                                dcc.Loading(html.Div(id="loading-component"),type="default"),
                                                                controls,
                                                                #dbc.Spinner(html.Div(id="loading-component")),
                                                            ],
                                                        )
                                            ],
                                            id="drawer-simple",
                                            padding="md",
                                            size="50%",
                                            position="right"
                                    ),]
                                ), span=5),
                                dmc.GridCol(html.Div(dmc.Title(f"Le marché et les statistiques de l'emploi", order=1, size="30", my="20", id="chainlit-call-fn", style=styleTitle)), span=5),
                                dmc.GridCol(html.Div(theme_toggle, style=styleToggle), span=1),
                                dmc.GridCol(html.Div(dmc.Tooltip(dmc.Button(leftSection=DashIconify(icon="tabler:sparkles", width=30), id="drawer-demo-button"), label="IA générative sur les données",position="left",withArrow=True,arrowSize=6,), style=styleToggle), span=1),
                                dmc.GridCol(html.A(DashIconify(icon="tabler:restore", width=20), href='/', style=styleRefresh), p=0,style=styleUSERIA, span=12),
                                dmc.GridCol(dmc.Tabs(
                                    [
                                        dmc.TabsList(mx="auto",grow=True,
                                            children=[
                                                dmc.TabsTab("Marché de l'emploi", leftSection=DashIconify(icon="tabler:graph"), value="1"),
                                                dmc.TabsTab("Statistiques de l'emploi", leftSection=DashIconify(icon="tabler:chart-pie"), value="2"),
                                                dmc.TabsTab("Savoir-faire, Savoirs et Contexte des métiers", leftSection=DashIconify(icon="tabler:ikosaedr"), value="3"),
                                            ]
                                        ),
                                        dmc.TabsPanel(
                                            dmc.Grid(
                                                children=[
                                                    dmc.GridCol(html.Div(
                                                        dcc.Loading(
                                                            id="loadingRepartition",
                                                            children=(dcc.Graph(id="figRepartition",selectedData={'points': [{'hovertext': ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16','17','18','19','2A','2B','21','22','23','24','25','26','27','28','29','30','31','32','33','34','35','36','37','38','39','40','41','42','43','44','45','46','47','48','49','50','51','52','53','54','55','56','57','58','59','60','61','62','63','64','65','66','67','68','69','70','71','72','73','74','75','76','77','78','79','80','81','82','83','84','85','86','87','88','89','90','91','92','93','94','95','971','972','973','974']}]})),
                                                            type="default",
                                                        )
                                                    ), span=6),
                                                    dmc.GridCol(html.Div(
                                                        dcc.Loading(
                                                            id="loadingEmplois",
                                                            children=(dcc.Graph(id="figEmplois")),
                                                            type="default",
                                                        )
                                                    ), span=6),
                                                    dmc.GridCol(html.Div(
                                                        dcc.Loading(
                                                            id="loadingContrats",
                                                            children=(dcc.Graph(id="figContrats")),
                                                            type="default",
                                                        )
                                                    ), span=6),
                                                    dmc.GridCol(html.Div(
                                                        dcc.Loading(
                                                            id="loadingExperiences",
                                                            children=(dcc.Graph(id="figExperiences")),
                                                            type="default",
                                                        )
                                                    ), span=6),
                                                    dmc.GridCol(html.Div(
                                                        dcc.Loading(
                                                            id="loadingCompetences",
                                                            children=(dcc.Graph(id="figCompetences")),
                                                            type="default",
                                                        )
                                                    ), span=6),
                                                    dmc.GridCol(html.Div(
                                                        dcc.Loading(
                                                            id="loadingTransversales",
                                                            children=(dcc.Graph(id="figTransversales")),
                                                            type="default",
                                                        )
                                                    ), span=6),
                                                    dmc.GridCol(html.Div(
                                                        dcc.Loading(
                                                            id="loadingNiveau",
                                                            children=(dcc.Graph(id="figNiveau")),
                                                            type="default",
                                                        )
                                                    ), span=6),
                                                    dmc.GridCol(html.Div(
                                                        dcc.Loading(
                                                            id="loadingSecteur",
                                                            children=(dcc.Graph(id="figSecteur")),
                                                            type="default",
                                                        )
                                                    ), span=6),
                                                    dmc.GridCol(html.Div(
                                                        dcc.Loading(
                                                            id="loadingTableau",
                                                            children=(dbc.Container(id="tableauEmplois")),
                                                            type="default",
                                                        )
                                                    ), span=12),
                                                ]
                                            )
                                        , value="1"),
                                        dmc.TabsPanel(
                                            children=[
                                                dmc.Button("Afficher les statistiques des métiers", mt=10, ml="auto", id="loading-button", leftSection=DashIconify(icon="tabler:chart-pie")),
                                                html.Div(id="clicked-output"),
                                                html.Div(id="clicked-output-tabs"),
                                            ], value="2"),
                                        dmc.TabsPanel(
                                            children=[
                                                dmc.Button("Afficher les savoirs des métiers", mt=10, ml="auto", id="loading-skills", leftSection=DashIconify(icon="tabler:ikosaedr")),
                                                html.Div(id="clicked-output-skills"),
                                                html.Div(id="clicked-output-skills-tabs"),
                                            ], value="3"),
                                    ],
                                    value="1",
                                ), span=12),
                            ],
                            gutter="xs",
                        )
                    ],size="xxl",fluid=True
                ),
            ]
        )
    ],
    id="mantine-provider",
    forceColorScheme="light",
    theme={
        "primaryColor": "indigo",
         "fontFamily": "'Inter', sans-serif",
         "components": {
             "Button": {"defaultProps": {"fw": 400}},
             "Alert": {"styles": {"title": {"fontWeight": 500}}},
             "AvatarGroup": {"styles": {"truncated": {"fontWeight": 500}}},
             "Badge": {"styles": {"root": {"fontWeight": 500}}},
             "Progress": {"styles": {"label": {"fontWeight": 500}}},
             "RingProgress": {"styles": {"label": {"fontWeight": 500}}},
             "CodeHighlightTabs": {"styles": {"file": {"padding": 12}}},
             "Table": {
                 "defaultProps": {
                     "highlightOnHover": True,
                     "withTableBorder": True,
                     "verticalSpacing": "sm",
                     "horizontalSpacing": "md",
                 }
             },
         },
         # add your colors
        "colors": {
            "deepBlue": ["#E9EDFC", "#C1CCF6", "#99ABF0"], # 10 color elements
        },
        "shadows": {
            # other shadows (xs, sm, lg) will be merged from default theme
            "md": "1px 1px 3px rgba(0,0,0,.25)",
            "xl": "5px 5px 3px rgba(0,0,0,.25)",
        },
        "headings": {
            "fontFamily": "Roboto, sans-serif",
            "sizes": {
                "h1": {"fontSize": 30},
            },
        },
    },
)
@callback(
    Output("mantine-provider", "forceColorScheme"),
    Input("color-scheme-toggle", "n_clicks"),
    State("mantine-provider", "forceColorScheme"),
    prevent_initial_call=True,
)
def switch_theme(_, theme):
    return "dark" if theme == "light" else "light"

@callback(
    Output("drawer-simple", "opened"),
    Input("drawer-demo-button", "n_clicks"),
    prevent_initial_call=True,
)
def drawer_demo(n_clicks):
    return True
    
@callback(
    Output(component_id='figRepartition', component_property='figure'),
    Output(component_id='figCompetences', component_property='figure'),
    Output(component_id='figTransversales', component_property='figure'),
    Output(component_id='figNiveau', component_property='figure'),
    Output(component_id='figSecteur', component_property='figure'),
    Input(component_id='framework-multi-select', component_property='value'),
    Input('figEmplois', 'selectedData'),
    Input("mantine-provider", "forceColorScheme"),
)
def create_repartition(array_value, selectedData, theme):
    if theme == "dark":
        template = "plotly_dark"
        paper_bgcolor = 'rgba(36, 36, 36, 1)'
        plot_bgcolor = 'rgba(36, 36, 36, 1)'
    else:
        template = "ggplot2"
        paper_bgcolor = 'rgba(255, 255, 255, 1)'
        plot_bgcolor = 'rgba(255, 255, 255, 1)'
    
    df_FT = API_France_Travail(array_value)
    
    ######## localisation ########
    df = df_FT[['intitule','typeContratLibelle','experienceLibelle','lieuTravail','secteurActiviteLibelle']].copy()
    df["lieuTravail"] = df["lieuTravail"].apply(lambda x: x['libelle']).apply(lambda x: x[0:3]).apply(lambda x: x.strip())
    df.drop(df[df['lieuTravail'] == 'Fra'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'FRA'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'Ile'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'Mar'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'Bou'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == '976'].index, inplace = True)
    
    ######## Filtre Emplois ########
    options = []
    if selectedData != None:
        if type(selectedData['points'][0]['y']) == str:
            options.append(selectedData['points'][0]['y'][:-3])
        else:
            options = selectedData['points'][0]['y'][:-3]
    else:
        options = df['intitule'].values.tolist()
    df = df[df['intitule'].isin(options)]
    
    ######## localisation ########
    ListCentroids = localisation()
    df_localisation = df.groupby('lieuTravail').size().reset_index(name='obs')
    df_localisation = df_localisation.sort_values(by=['lieuTravail'])
    df_localisation['longitude'] = df_localisation['lieuTravail']
    df_localisation['latitude'] = df_localisation['lieuTravail']
    df_localisation["longitude"] = df_localisation['longitude'].apply(lambda x:[loc['Longitude'] for loc in ListCentroids if loc['ID'] == x]).apply(lambda x:''.join(map(str, x)))
    df_localisation["longitude"] = pd.to_numeric(df_localisation["longitude"], downcast="float")
    df_localisation["latitude"] = df_localisation['latitude'].apply(lambda x:[loc['Latitude'] for loc in ListCentroids if loc['ID'] == x]).apply(lambda x:''.join(map(str, x)))
    df_localisation["latitude"] = pd.to_numeric(df_localisation["latitude"], downcast="float")
    
    res = requests.get(
            "https://raw.githubusercontent.com/codeforgermany/click_that_hood/main/public/data/france-regions.geojson"
        )
    fig_localisation = px.scatter_mapbox(df_localisation, lat="latitude", lon="longitude", height=600, template=template, title="La répartition géographique des emplois", hover_name="lieuTravail", size="obs").update_layout(
        mapbox={
            "style": "carto-positron",
            "center": {"lon": 2, "lat" : 47},
            "zoom": 4.5,
            "layers": [
                {
                    "source": res.json(),
                    "type": "line",
                    "color": "green",
                    "line": {"width": 0},
                }
            ],
        },font=dict(size=10),paper_bgcolor=paper_bgcolor,autosize=True,clickmode='event+select'
    )
        
    ######## Compétences professionnelles ########
    df_FT.dropna(subset=['qualitesProfessionnelles','formations','competences'], inplace=True)
    df_FT["competences"] = df_FT["competences"].apply(lambda x:[str(e['libelle']) for e in x]).apply(lambda x:'; '.join(map(str, x)))
    df_FT["qualitesProfessionnelles"] = df_FT["qualitesProfessionnelles"].apply(lambda x:[str(e['libelle']) + ": " + str(e['description']) for e in x]).apply(lambda x:'; '.join(map(str, x)))
    
    df_comp = df_FT
    df_comp['competences'] = df_FT['competences'].str.split(';')
    df_comp = df_comp.explode('competences')
    df_comp = df_comp.groupby('competences').size().reset_index(name='obs')
    df_comp = df_comp.sort_values(by=['obs'])
    df_comp = df_comp.iloc[-25:]
    fig_competences = px.bar(df_comp, x='obs', y='competences', orientation='h', color='obs', height=600, template=template, title="Les principales compétences professionnelles", labels={'obs':'nombre'}, color_continuous_scale="Teal", text_auto=True).update_layout(font=dict(size=10),paper_bgcolor=paper_bgcolor,plot_bgcolor=plot_bgcolor,clickmode='event+select',autosize=True).update_traces(hovertemplate=df_comp["competences"] + ' <br>Nombre : %{x}', y=[y[:100] + "..." for y in df_comp['competences']], showlegend=False)
        
    ######## Compétences transversales ########
    df_transversales = df_FT
    df_transversales['qualitesProfessionnelles'] = df_FT['qualitesProfessionnelles'].str.split(';')
    df_comptransversales = df_transversales.explode('qualitesProfessionnelles')
    df_comptransversales = df_comptransversales.groupby('qualitesProfessionnelles').size().reset_index(name='obs')
    df_comptransversales = df_comptransversales.sort_values(by=['obs'])
    df_comptransversales = df_comptransversales.iloc[-25:]
    fig_transversales = px.bar(df_comptransversales, x='obs', y='qualitesProfessionnelles', orientation='h', color='obs', height=600, template=template, title="Les principales compétences transversales", labels={'obs':'nombre'}, color_continuous_scale="Teal", text_auto=True).update_layout(font=dict(size=10),paper_bgcolor=paper_bgcolor,plot_bgcolor=plot_bgcolor,autosize=True).update_traces(hovertemplate=df_comptransversales["qualitesProfessionnelles"] + ' <br>Nombre : %{x}', y=[y[:80] + "..." for y in df_comptransversales["qualitesProfessionnelles"]], showlegend=False)
        
    ######## Niveaux de qualification ########
    df_niveau = df_FT
    df_niveau["formations"] = df_niveau["formations"].apply(lambda x:[str(e['niveauLibelle']) for e in x]).apply(lambda x:'; '.join(map(str, x)))
    df_niveau = df_niveau.groupby('formations').size().reset_index(name='obs')
    fig_niveau = px.pie(df_niveau, names='formations', height=600, values='obs', color='obs', template=template, title="Les niveaux de qualification", labels={'obs':'nombre'}, color_discrete_sequence=px.colors.qualitative.Safe).update_traces(textposition='inside', textinfo='percent+label').update_layout(font=dict(size=10),paper_bgcolor=paper_bgcolor)
    
    ######## Secteurs ########
    df_secteur = df.groupby('secteurActiviteLibelle').size().reset_index(name='obs')
    df_secteur = df_secteur.sort_values(by=['obs'])
    df_secteur = df_secteur.iloc[-25:]
    fig_secteur = px.bar(df_secteur, x='obs', y='secteurActiviteLibelle', height=600, orientation='h', color='obs', template=template, title="Les principaux secteurs d'activités", labels={'obs':'nombre'}, color_continuous_scale="Teal", text_auto=True).update_layout(font=dict(size=10),paper_bgcolor=paper_bgcolor,plot_bgcolor=plot_bgcolor,autosize=True).update_traces(hovertemplate=df_secteur["secteurActiviteLibelle"] + ' <br>Nombre : %{x}', y=[y[:80] + "..." for y in df_secteur["secteurActiviteLibelle"]], showlegend=False)

        
    return fig_localisation, fig_competences, fig_transversales, fig_niveau, fig_secteur

def create_emploi(df, theme):
    if theme == "dark":
        template = "plotly_dark"
        paper_bgcolor = 'rgba(36, 36, 36, 1)'
        plot_bgcolor = 'rgba(36, 36, 36, 1)'
    else:
        template = "ggplot2"
        paper_bgcolor = 'rgba(255, 255, 255, 1)'
        plot_bgcolor = 'rgba(255, 255, 255, 1)'
    ######## Emplois ########
    df_intitule = df.groupby('intitule').size().reset_index(name='obs')
    df_intitule = df_intitule.sort_values(by=['obs'])
    df_intitule = df_intitule.iloc[-25:]
    fig_intitule = px.bar(df_intitule, x='obs', y='intitule', height=600, orientation='h', color='obs', template=template, title="Les principaux emplois", labels={'obs':'nombre'}, color_continuous_scale="Teal", text_auto=True).update_layout(font=dict(size=10),paper_bgcolor=paper_bgcolor,plot_bgcolor=plot_bgcolor,clickmode='event+select',autosize=True).update_traces(hovertemplate=df_intitule["intitule"] + ' <br>Nombre : %{x}', y=[y[:100] + "..." for y in df_intitule["intitule"]], showlegend=False)
    
    return fig_intitule

def create_contrat(df, theme):
    if theme == "dark":
        template = "plotly_dark"
        paper_bgcolor = 'rgba(36, 36, 36, 1)'
    else:
        template = "ggplot2"
        paper_bgcolor = 'rgba(255, 255, 255, 1)'
    
    ######## Types de contrat ########
    df_contrat = df.groupby('typeContratLibelle').size().reset_index(name='obs')
    fig_contrat = px.pie(df_contrat, names='typeContratLibelle', values='obs', color='obs', height=600, template=template, title="Les types de contrat", labels={'obs':'nombre'}, color_discrete_sequence=px.colors.qualitative.Safe).update_traces(textposition='inside', textinfo='percent+label').update_layout(font=dict(size=10),paper_bgcolor=paper_bgcolor)
    
    return fig_contrat

def create_experience(df, theme):
    if theme == "dark":
        template = "plotly_dark"
        paper_bgcolor = 'rgba(36, 36, 36, 1)'
    else:
        template = "ggplot2"
        paper_bgcolor = 'rgba(255, 255, 255, 1)'
    ######## Expériences professionnelles ########
    df_experience = df.groupby('experienceLibelle').size().reset_index(name='obs')
    fig_experience = px.pie(df_experience, names='experienceLibelle', values='obs', color='obs', height=600, template=template, title="Les expériences professionnelles", labels={'obs':'nombre'}, color_discrete_sequence=px.colors.qualitative.Safe).update_traces(textposition='inside', textinfo='percent+label').update_layout(font=dict(size=10),paper_bgcolor=paper_bgcolor)
    
    return fig_experience

def create_tableau(df, theme):
    if theme == "dark":
        style_header = {
            'fontFamily': "'Inter', sans-serif",
            'fontSize': '10px',
            'backgroundColor': 'rgb(30, 30, 30)',
            'color': 'white'
        }
        style_data={
            'fontFamily': "'Inter', sans-serif",
            'fontSize': '10px',
            'backgroundColor': 'rgb(50, 50, 50)',
            'color': 'white'
        }
        style_tooltip='background-color: lightgrey; font-family: "Inter", sans-serif; font-size:10px; color: white'
    else:
        style_header = {
            'fontFamily': "'Inter', sans-serif",
            'fontSize': '10px',
            'backgroundColor': 'transparent',
            'color': 'black'
        }
        style_data={
            'fontFamily': "'Inter', sans-serif",
            'fontSize': '10px',
            'backgroundColor': 'transparent',
            'color': 'black'
        }
        style_tooltip='background-color: lightgrey; font-family: "Inter", sans-serif; font-size:10px; color: black'

    ######## Tableau des emplois ########
    #df = df.fillna('N/A').replace('', 'N/A')
    df_tableau = df[['origineOffre','intitule','typeContratLibelle','experienceLibelle','description','lieuTravail']].copy()
    dictHeader = {'origineOffre': 'Lien','intitule': 'Offre','typeContratLibelle': 'Type de contrat','experienceLibelle':'Expérience','description':'Détail','lieuTravail':'Département'}
    df_tableau.rename(columns=dictHeader,inplace=True)
    tableau_Emplois = dash_table.DataTable(
        data=df_tableau.to_dict('records'),
        sort_action='native',
        columns=[{'id': c, 'name': c, 'presentation': 'markdown'} if c == 'Lien' else {'id': c, 'name': c} for c in df_tableau.columns],
        filter_action="native",
        filter_options={"placeholder_text": "Filtrer les valeurs de la colonne..."},
        page_action='native',
        page_current= 0,
        page_size= 10,
        style_header=style_header,
        style_data=style_data,
        style_table={'overflowX': 'auto'},
        style_cell={
            'overflow': 'hidden',
            'textOverflow': 'ellipsis',
            'maxWidth': 0,
        },
        tooltip_data=[
            {
                column: {'value': str(value), 'type': 'markdown'}
                for column, value in row.items()
            } for row in df_tableau.to_dict('records')
        ],
        css=[{
            'selector': '.dash-table-tooltip',
            'rule': style_tooltip
        },{
            'selector': '.dash-table-tooltip > p',
            'rule': style_tooltip
        }],
        tooltip_delay=0,
        tooltip_duration=None
    )
    return tableau_Emplois

@callback(
    Output(component_id='figEmplois', component_property='figure'),
    Input('figRepartition', 'selectedData'),
    Input(component_id='framework-multi-select', component_property='value'),
    Input("mantine-provider", "forceColorScheme"),
)

def update_emploi(selectedData, array_value, theme):
    options = []
    if selectedData != None:
        if type(selectedData['points'][0]['hovertext']) == str:
            options.append(selectedData['points'][0]['hovertext'])
        else:
            options = selectedData['points'][0]['hovertext']
    else:
        options = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16','17','18','19','2A','2B','21','22','23','24','25','26','27','28','29','30','31','32','33','34','35','36','37','38','39','40','41','42','43','44','45','46','47','48','49','50','51','52','53','54','55','56','57','58','59','60','61','62','63','64','65','66','67','68','69','70','71','72','73','74','75','76','77','78','79','80','81','82','83','84','85','86','87','88','89','90','91','92','93','94','95','971','972','973','974']
    
    df_FT = API_France_Travail(array_value)
    df = df_FT[['intitule','typeContratLibelle','experienceLibelle','lieuTravail']].copy()
    df["lieuTravail"] = df["lieuTravail"].apply(lambda x: x['libelle']).apply(lambda x: x[0:3]).apply(lambda x: x.strip())
    df.drop(df[df['lieuTravail'] == 'Fra'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'FRA'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'Ile'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'Mar'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'Bou'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == '976'].index, inplace = True)
    df = df[df['lieuTravail'].isin(options)]
    return create_emploi(df, theme)

@callback(
    Output(component_id='figContrats', component_property='figure'),
    Input('figRepartition', 'selectedData'),
    Input(component_id='framework-multi-select', component_property='value'),
    Input("mantine-provider", "forceColorScheme"),
)

def update_contrat(selectedData, array_value, theme):
    options = []
    if selectedData != None:
        if type(selectedData['points'][0]['hovertext']) == str:
            options.append(selectedData['points'][0]['hovertext'])
        else:
            options = selectedData['points'][0]['hovertext']
    else:
        options = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16','17','18','19','2A','2B','21','22','23','24','25','26','27','28','29','30','31','32','33','34','35','36','37','38','39','40','41','42','43','44','45','46','47','48','49','50','51','52','53','54','55','56','57','58','59','60','61','62','63','64','65','66','67','68','69','70','71','72','73','74','75','76','77','78','79','80','81','82','83','84','85','86','87','88','89','90','91','92','93','94','95','971','972','973','974']
    
    df_FT = API_France_Travail(array_value)
    df = df_FT[['intitule','typeContratLibelle','experienceLibelle','lieuTravail']].copy()
    df["lieuTravail"] = df["lieuTravail"].apply(lambda x: x['libelle']).apply(lambda x: x[0:3]).apply(lambda x: x.strip())
    df.drop(df[df['lieuTravail'] == 'Fra'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'FRA'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'Ile'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'Mar'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'Bou'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == '976'].index, inplace = True)
    df = df[df['lieuTravail'].isin(options)]
        
    return create_contrat(df, theme)

@callback(
    Output(component_id='figExperiences', component_property='figure'),
    Input('figRepartition', 'selectedData'),
    Input(component_id='framework-multi-select', component_property='value'),
    Input("mantine-provider", "forceColorScheme"),
)

def update_experience(selectedData, array_value, theme):
    options = []
    if selectedData != None:
        if type(selectedData['points'][0]['hovertext']) == str:
            options.append(selectedData['points'][0]['hovertext'])
        else:
            options = selectedData['points'][0]['hovertext']
    else:
        options = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16','17','18','19','2A','2B','21','22','23','24','25','26','27','28','29','30','31','32','33','34','35','36','37','38','39','40','41','42','43','44','45','46','47','48','49','50','51','52','53','54','55','56','57','58','59','60','61','62','63','64','65','66','67','68','69','70','71','72','73','74','75','76','77','78','79','80','81','82','83','84','85','86','87','88','89','90','91','92','93','94','95','971','972','973','974']
    
    df_FT = API_France_Travail(array_value)
    df = df_FT[['intitule','typeContratLibelle','experienceLibelle','lieuTravail']].copy()
    df["lieuTravail"] = df["lieuTravail"].apply(lambda x: x['libelle']).apply(lambda x: x[0:3]).apply(lambda x: x.strip())
    df.drop(df[df['lieuTravail'] == 'Fra'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'FRA'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'Ile'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'Mar'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == 'Bou'].index, inplace = True)
    df.drop(df[df['lieuTravail'] == '976'].index, inplace = True)
    df = df[df['lieuTravail'].isin(options)]
    
    return create_experience(df, theme)

@callback(
    Output(component_id='tableauEmplois', component_property='children'),
    Input('figRepartition', 'selectedData'),
    Input(component_id='framework-multi-select', component_property='value'),
    Input("mantine-provider", "forceColorScheme"),
    Input('store-emplois', 'data')
)

def update_tableau(selectedData, array_value, theme, data):
    options = []
    if selectedData != None:
        if type(selectedData['points'][0]['hovertext']) == str:
            options.append(selectedData['points'][0]['hovertext'])
        else:
            options = selectedData['points'][0]['hovertext']
    else:
        options = ['01','02','03','04','05','06','07','08','09','10','11','12','13','14','15','16','17','18','19','2A','2B','21','22','23','24','25','26','27','28','29','30','31','32','33','34','35','36','37','38','39','40','41','42','43','44','45','46','47','48','49','50','51','52','53','54','55','56','57','58','59','60','61','62','63','64','65','66','67','68','69','70','71','72','73','74','75','76','77','78','79','80','81','82','83','84','85','86','87','88','89','90','91','92','93','94','95','971','972','973','974']
    
    #df_FT = API_France_Travail(array_value)
    df_FT = pd.DataFrame(data)
    df_FT["origineOffre"] = df_FT["origineOffre"].apply(lambda x: "[Voir l'offre sur le site web de France Travail](" + x['urlOrigine'] + ")")
    df_FT["lieuTravail"] = df_FT["lieuTravail"].apply(lambda x: x['libelle']).apply(lambda x: x[0:3]).apply(lambda x: x.strip())
    df_FT.drop(df_FT[df_FT['lieuTravail'] == 'Fra'].index, inplace = True)
    df_FT.drop(df_FT[df_FT['lieuTravail'] == 'FRA'].index, inplace = True)
    df_FT.drop(df_FT[df_FT['lieuTravail'] == 'Ile'].index, inplace = True)
    df_FT.drop(df_FT[df_FT['lieuTravail'] == 'Mar'].index, inplace = True)
    df_FT.drop(df_FT[df_FT['lieuTravail'] == 'Bou'].index, inplace = True)
    df_FT.drop(df_FT[df_FT['lieuTravail'] == '976'].index, inplace = True)
    df_FT = df_FT[df_FT['lieuTravail'].isin(options)]
    
    return create_tableau(df_FT, theme)

clientside_callback(
    """
    function updateLoadingState(n_clicks) {
        return true
    }
    """,
    Output("loading-button", "loading", allow_duplicate=True),
    Input("loading-button", "n_clicks"),
    prevent_initial_call=True,
)

@callback(
    Output("clicked-output", "children"),
    Output("clicked-output-tabs", "children"),
    Output("loading-button", "loading"),
    Input("loading-button", "n_clicks"),
    Input(component_id='framework-multi-select', component_property='value'),
    Input("mantine-provider", "forceColorScheme"),
    prevent_initial_call=True,
)
def load_from_stats(n_clicks, array_value, theme):
    if theme == "dark":
        template = "plotly_dark"
        paper_bgcolor = 'rgba(36, 36, 36, 1)'
        plot_bgcolor = 'rgba(36, 36, 36, 1)'
        style_header = {
            'fontFamily': "'Inter', sans-serif",
            'fontSize': '10px',
            'backgroundColor': 'rgb(30, 30, 30)',
            'color': 'white'
        }
        style_data={
            'fontFamily': "'Inter', sans-serif",
            'fontSize': '10px',
            'backgroundColor': 'rgb(50, 50, 50)',
            'color': 'white'
        }
    else:
        template = "ggplot2"
        paper_bgcolor = 'rgba(255, 255, 255, 1)'
        plot_bgcolor = 'rgba(255, 255, 255, 1)'
        style_header = {
            'fontFamily': "'Inter', sans-serif",
            'fontSize': '10px',
            'backgroundColor': 'transparent',
            'color': 'black'
        }
        style_data={
            'fontFamily': "'Inter', sans-serif",
            'fontSize': '10px',
            'backgroundColor': 'transparent',
            'color': 'black'
        }
    children = []
    children_tabs = []
    for j in range(0, len(array_value)):
        table = datavisualisation_chiffres_cles_emplois("https://dataemploi.pole-emploi.fr/metier/chiffres-cles/NAT/FR/" + array_value[j])
        array_label_rome = searchByRome(array_value[j])
        df_demandeur = htmlToDataframe(table[0])
        df_demandeur = df_demandeur.sort_values(by=['Indicateur'])
        fig_demandeur = px.histogram(df_demandeur, x='Indicateur', y='Valeur', height=800, template=template, title="Demandeurs d'emploi et offres d'emploi du code ROME : " +  array_label_rome[0]['label'], color='Indicateur', labels={'Valeur':'Nombre'}, text_auto=True).update_layout(font=dict(size=9),paper_bgcolor=paper_bgcolor,plot_bgcolor=plot_bgcolor,autosize=True)
        children.append(dmc.GridCol(html.Div(dcc.Loading(id="loadingPlot",children=(dcc.Graph(figure=fig_demandeur)),type="default")), span=6),)
        children_tabs.append(dmc.GridCol(html.Div(dcc.Loading(id="loadingPlot",children=[dbc.Label("Demandeurs d'emploi et offres d'emploi du code ROME : " + array_label_rome[0]['label']),dash_table.DataTable(data=df_demandeur.to_dict('records'),sort_action='native', columns=[{'id': c, 'name': c} for c in df_demandeur.columns],page_action='native', page_current= 0,page_size= 10,style_header=style_header,style_data=style_data,style_table={'overflowX': 'auto'},style_cell={'overflow': 'hidden','textOverflow': 'ellipsis','maxWidth': 0,})],type="default")), span=12),)
        if len(table[1]) > 0:
            df_salaire = htmlToDataframe(table[1])
            df_salaire = df_salaire.sort_values(by=['salaire'])
            fig_salaire = px.histogram(df_salaire, x='emploi', y='salaire', height=600, template=template, barmode='group', title="Salaires médians du code ROME : " + array_label_rome[0]['label'], color='categorie', text_auto=True).update_layout(font=dict(size=9),paper_bgcolor=paper_bgcolor,plot_bgcolor=plot_bgcolor,autosize=True)
            children.append(dmc.GridCol(html.Div(dcc.Loading(id="loadingPlot",children=(dcc.Graph(figure=fig_salaire)),type="default")), span=6),)
            children_tabs.append(dmc.GridCol(html.Div(dcc.Loading(id="loadingPlot",children=[dbc.Label("Salaires médians du code ROME : " + array_label_rome[0]['label']),dash_table.DataTable(data=df_salaire.to_dict('records'),sort_action='native', columns=[{'id': c, 'name': c} for c in df_salaire.columns],page_action='native', page_current= 0,page_size= 10,style_header=style_header,style_data=style_data,style_table={'overflowX': 'auto'},style_cell={'overflow': 'hidden','textOverflow': 'ellipsis','maxWidth': 0,})],type="default")), span=12),)
        df_difficulte = htmlToDataframe(table[2])
        if len(df_difficulte) == 0:
            title = "Aucune donnée difficulté de recrutement renseignée!"
        else:
            title = "Difficulté de recrutement du code ROME : " + array_label_rome[0]['label']
        df_difficulte = df_difficulte.sort_values(by=['Valeur'])
        fig_difficulte = px.histogram(df_difficulte, x='Indicateur', y='Valeur', height=600, template=template, title=title, color='Indicateur', labels={'Valeur':'Pourcentage'}, text_auto=True).update_layout(font=dict(size=9),paper_bgcolor=paper_bgcolor,plot_bgcolor=plot_bgcolor,autosize=True)
        children.append(dmc.GridCol(html.Div(dcc.Loading(id="loadingPlot",children=(dcc.Graph(figure=fig_difficulte)),type="default")), span=6))
        children_tabs.append(dmc.GridCol(html.Div(dcc.Loading(id="loadingPlot",children=[dbc.Label(title),dash_table.DataTable(data=df_difficulte.to_dict('records'),sort_action='native', columns=[{'id': c, 'name': c} for c in df_difficulte.columns],page_action='native', page_current= 0,page_size= 10,style_header=style_header,style_data=style_data,style_table={'overflowX': 'auto'},style_cell={'overflow': 'hidden','textOverflow': 'ellipsis','maxWidth': 0,})],type="default")), span=12),)
        df_repartitionContrat = htmlToDataframe(table[3])
        df_repartitionContrat = df_repartitionContrat.sort_values(by=['Valeur'])
        fig_repartitionContrat = px.pie(df_repartitionContrat, names='Indicateur', values='Valeur', color='Indicateur', template=template, title="Répartition des embauches du métier : type de contrat du code ROME : " + array_label_rome[0]['label'], labels={'Valeur':'pourcentage'}, color_discrete_sequence=px.colors.qualitative.Safe).update_traces(textposition='inside', textinfo='percent+label').update_layout(font=dict(size=10),paper_bgcolor=paper_bgcolor)
        children.append(dmc.GridCol(html.Div(dcc.Loading(id="loadingPlot",children=(dcc.Graph(figure=fig_repartitionContrat)),type="default")), span=6))
        children_tabs.append(dmc.GridCol(html.Div(dcc.Loading(id="loadingPlot",children=[dbc.Label("Répartition des embauches du métier : type de contrat du code ROME : " + array_label_rome[0]['label']),dash_table.DataTable(data=df_repartitionContrat.to_dict('records'),sort_action='native', columns=[{'id': c, 'name': c} for c in df_repartitionContrat.columns],page_action='native', page_current= 0,page_size= 10,style_header=style_header,style_data=style_data,style_table={'overflowX': 'auto'},style_cell={'overflow': 'hidden','textOverflow': 'ellipsis','maxWidth': 0,})],type="default")), span=12),)
        df_repartitionEntreprise = htmlToDataframe(table[4])
        df_repartitionEntreprise = df_repartitionEntreprise.sort_values(by=['Valeur'])
        fig_repartitionEntreprise = px.pie(df_repartitionEntreprise, names='Indicateur', values='Valeur', color='Indicateur', template=template, title="Répartition des embauches du métier : type entreprise du code ROME : " + array_label_rome[0]['label'], labels={'Valeur':'pourcentage'}, color_discrete_sequence=px.colors.qualitative.Safe).update_traces(textposition='inside', textinfo='percent+label').update_layout(font=dict(size=10),paper_bgcolor=paper_bgcolor)
        children.append(dmc.GridCol(html.Div(dcc.Loading(id="loadingPlot",children=(dcc.Graph(figure=fig_repartitionEntreprise)),type="default")), span=6))
        children_tabs.append(dmc.GridCol(html.Div(dcc.Loading(id="loadingPlot",children=[dbc.Label("Répartition des embauches du métier : type entreprise du code ROME : " + array_label_rome[0]['label']),dash_table.DataTable(data=df_repartitionEntreprise.to_dict('records'),sort_action='native', columns=[{'id': c, 'name': c} for c in df_repartitionEntreprise.columns],page_action='native', page_current= 0,page_size= 10,style_header=style_header,style_data=style_data,style_table={'overflowX': 'auto'},style_cell={'overflow': 'hidden','textOverflow': 'ellipsis','maxWidth': 0,})],type="default")), span=12),)
    return dmc.Grid(children=children), dmc.Grid(children=children_tabs), False

clientside_callback(
    """
    function updateLoadingState(n_clicks) {
        return true
    }
    """,
    Output("loading-skills", "loading", allow_duplicate=True),
    Input("loading-skills", "n_clicks"),
    prevent_initial_call=True,
)

@callback(
    Output("clicked-output-skills", "children"),
    Output("loading-skills", "loading"),
    Input("loading-skills", "n_clicks"),
    Input(component_id='framework-multi-select', component_property='value'),
    Input("mantine-provider", "forceColorScheme"),
    prevent_initial_call=True,
)

def load_from_skills(n_clicks, array_value, theme):
    if theme == "dark":
        template = "plotly_dark"
        paper_bgcolor = 'rgba(36, 36, 36, 1)'
        plot_bgcolor = 'rgba(36, 36, 36, 1)'
    else:
        template = "ggplot2"
        paper_bgcolor = 'rgba(255, 255, 255, 1)'
        plot_bgcolor = 'rgba(255, 255, 255, 1)'
    children = []
    for j in range(0, len(array_value)):
        ficheSF = getSavoirFaireFromHTMLMetier("https://candidat.francetravail.fr/metierscope/fiche-metier/" + array_value[j])
        fig_SF = datavisualisation_skills_context(htmlToDataframe(ficheSF), template, paper_bgcolor, plot_bgcolor, "Savoir-faire", array_value[j])
        ficheSavoir = getSavoirFromHTMLMetier("https://candidat.francetravail.fr/metierscope/fiche-metier/" + array_value[j])
        fig_Savoir = datavisualisation_skills_context(htmlToDataframe(ficheSavoir), template, paper_bgcolor, plot_bgcolor, "Savoirs", array_value[j])
        ficheContext = getContextFromHTMLMetier("https://candidat.francetravail.fr/metierscope/fiche-metier/" + array_value[j])
        fig_Context = datavisualisation_skills_context(htmlToDataframe(ficheContext), template, paper_bgcolor, plot_bgcolor, "Contexte", array_value[j])
        
        children.append(dmc.GridCol(html.Div(dcc.Loading(id="loadingPlot",children=(dcc.Graph(figure=fig_SF)), type="default"), style=styleTitle), span=12),)
        children.append(dmc.GridCol(html.Div(dcc.Loading(id="loadingPlot",children=(dcc.Graph(figure=fig_Savoir)), type="default"), style=styleTitle), span=12),)
        children.append(dmc.GridCol(html.Div(dcc.Loading(id="loadingPlot",children=(dcc.Graph(figure=fig_Context)), type="default"), style=styleTitle), span=12),)
    
    return dmc.Grid(children=children), False
        

########### IA Chatbot ###########
@app.callback(
    Output("display-conversation", "children"), [Input("store-conversation", "data")]
)
def update_display(chat_history):
    return [
        textbox(x, box="user") if i % 2 == 0 else textbox(x, box="AI")
        for i, x in enumerate(chat_history.split("<split>")[:-1])
    ]


@app.callback(
    Output("user-input", "value"),
    [Input("submit", "n_clicks"), Input("user-input", "n_submit")],
)
def clear_input(n_clicks, n_submit):
    return ""

@app.callback(
    [Output("store-conversation", "data"), Output("loading-component", "children")],
    [Input("submit", "n_clicks"), Input("user-input", "n_submit")],
    [State("user-input", "value"), State("store-conversation", "data")],
    Input(component_id='framework-multi-select', component_property='value'),
)
def run_chatbot(n_clicks, n_submit, user_input, chat_history, array_value):
    if n_clicks == 0 and n_submit is None:
        return "", None

    if user_input is None or user_input == "":
        return chat_history, None

    df_FT = API_France_Travail(array_value)
    
    df_FT_Select = df_FT[['intitule','typeContratLibelle','experienceLibelle','competences','description','qualitesProfessionnelles','salaire','lieuTravail','formations']].copy()
    list_FT = df_FT_Select.values.tolist()
    context = ''
    for i in range(0,len(list_FT)):
        context += "\n✔️ Emploi : " + str(list_FT[i][0]) + ";\n◉ Contrat : " + str(list_FT[i][1]) + ";\n◉ Compétences professionnelles : " + str(list_FT[i][3]).replace("{","").replace("}","").replace("[","").replace("]","").replace("code","").replace("libelle","") + ";\n" + "◉ Salaire : " + str(list_FT[i][6]).replace("{","").replace("}","").replace("[","").replace("]","") + ";\n◉ Qualification : " + str(list_FT[i][5]).replace("'libelle'","\n• 'libelle").replace("{","").replace("}","").replace("[","").replace("]","").replace("code","") + ";\n◉ Localisation : " + str(list_FT[i][7]).replace("{","").replace("}","").replace("[","").replace("]","") + ";\n◉ Expérience : " + str(list_FT[i][2]) + ";\n◉ Niveau de qualification : " + str(list_FT[i][8]).replace("{","").replace("}","").replace("[","").replace("]","") + ";\n◉ Description de l'emploi : " + str(list_FT[i][4]) + "\n"
        #context = df_FT.to_string(index=False)
    template = """<s>[INST] Vous êtes un ingénieur pédagogique de l'enseignement supérieur et vous êtes doué pour faire des analyses des formations de l'enseignement supérieur et de faire le rapprochement entre les compétences académiques et les compétences professionnelles attendues par le marché de l'emploi et les les recruteurs, en fonction des critères définis ci-avant. En fonction des informations suivantes et du contexte suivant seulement et strictement, répondez en langue française strictement à la question ci-dessous, en 5000 mots au moins. Lorsque cela est possible, cite les sources du contexte. Si vous ne pouvez pas répondre à la question sur la base des informations, dites que vous ne trouvez pas de réponse ou que vous ne parvenez pas à trouver de réponse. Essayez donc de comprendre en profondeur le contexte et répondez uniquement en vous basant sur les informations fournies. Ne générez pas de réponses non pertinentes.
    Répondez à la question ci-dessous à partir du contexte ci-dessous :
    {context}
    {question} [/INST] </s>
    """
    context_p = context[:28500]
    name = "Mistral"
    chat_history += f"Vous: {user_input}<split>{name}:"

    model_input = template + chat_history.replace("<split>", "\n")
    #model_input = template
    
    prompt = PromptTemplate(template=model_input, input_variables=["question","context"])
    
    #prompt = dedent(
    #    f"""
    #{description}

    #Vous: Bonjour {name}!
    #{name}: Bonjour! Ravi de parler avec vous aujourd'hui.
    #"""
    #)

    # First add the user input to the chat history
    
    os.environ['HUGGINGFACEHUB_API_TOKEN'] = os.environ['HUGGINGFACEHUB_API_TOKEN']
    #repo_id = "mistralai/Mistral-7B-Instruct-v0.3"
    repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
    #repo_id = "microsoft/Phi-3.5-mini-instruct"
    #mistral_url = "https://api-inference.huggingface.co/models/mistralai/Mixtral-8x22B-Instruct-v0.1"
    llm = HuggingFaceEndpoint(
        repo_id=repo_id, task="text2text-generation", max_new_tokens=8000, temperature=0.3, streaming=True
    )
    model_output = ""
    chain = prompt | llm
    for s in chain.stream({"question":"D'après le contexte, " + user_input,"context":context_p}):
        model_output = model_output + s
        print(s, end="", flush=True)
    
  
  #response = openai.Completion.create(
    #    engine="davinci",
    #    prompt=model_input,
    #    max_tokens=250,
    #    stop=["You:"],
    #    temperature=0.9,
    #)
    #model_output = response.choices[0].text.strip()

    chat_history += f"{model_output}<split>"
        
    return chat_history, None
    
if __name__ == '__main__':
    app.run_server(debug=True)