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| 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"]) | |
| 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) | |
| 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 | |
| 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}, | |
| }, | |
| }, | |
| }, | |
| ) | |
| def switch_theme(_, theme): | |
| return "dark" if theme == "light" else "light" | |
| def drawer_demo(n_clicks): | |
| return True | |
| 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 | |
| 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) | |
| 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) | |
| 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) | |
| def update_tableau(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_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, | |
| ) | |
| 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, | |
| ) | |
| 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 ########### | |
| 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]) | |
| ] | |
| def clear_input(n_clicks, n_submit): | |
| return "" | |
| 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) |