Code Projet IN304.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importation des différentes librairies nécessaires au projet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!/usr/bin/env python\n",
    "# -*- coding: utf-8 -*-\n",
    "\n",
    "__author__ = \"Mathilde GAUTEUR, Joseph ARIAS\"\n",
    "__copyright__ = \"Copyright 2023, Projet InPoDa\"\n",
    "__credits__ = [\"Mathilde GAUTEUR\", \"Joseph ARIAS\"]\n",
    "__license__ = \"GPL\"\n",
    "__maintainer__ = \"Joseph ARIAS\"\n",
    "__email__ = \"joseph.arias@ens.uvsq.fr\"\n",
    "__status__ = \"Development\"\n",
    "\n",
    "import json as js\n",
    "import time\n",
    "import geopy.exc\n",
    "from textblob import TextBlob\n",
    "import random\n",
    "import folium\n",
    "from collections import Counter\n",
    "from geopy.geocoders import Nominatim\n",
    "import regex as re\n",
    "import plotly.express as px\n",
    "import pandas as pd\n",
    "import os\n",
    "import emoji\n",
    "import gradio as gr\n",
    "import operator\n",
    "import threading"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Différentes variable permettant le bon fonctionnement du projet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "avancement_map = 0 # devient 1 lorsque la géolocalisation des tweets se lance\n",
    "fin_map = 0 # devint 1 lorsque le thread s'occupant de la carte recensant les lieux de publications des tweets est fini\n",
    "analyse_finished = 0 # devient 1 lorsque l'ensemble des tweets du fichier json de départ ont été converti en objet puis analysé\n",
    "file_path = 'aitweets.json' # chemin du fichier à analyser"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Création d'une classe tweet afin de faciliter la manipulation et l'analyse des tweets fournit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Tweet:\n",
    "    used_hashtag = {}  # dictionnaire avec le nom du hashtag en clé et la liste des tweets contenant le hashtag en\n",
    "    # valeur\n",
    "    used_hashtag_sorted = []\n",
    "    user_mentioned = {}  # dictionnaire avec le nom de l'utilisateur en clé et la liste des tweets mentionnant\n",
    "    # l'utilisateur en valeur\n",
    "    user_mentioned_sorted = []\n",
    "    tweets_of_users = {}  # dictionnaire avec le nom de l'utilisateur en clé et la liste des tweets de l'utilisateur en\n",
    "    # valeur\n",
    "    tweets_of_users_sorted = []\n",
    "    tweets_users = []\n",
    "    tweets_polarity = [0, 0, 0]  # avec en indice 0 le nombre de tweets négatifs, en indice 1 le nombre de tweets\n",
    "    # neutres et en indice 2 le nombre de tweets positifs\n",
    "    tweets_objectivity = [0, 0]  # avec en indice 0 le nombre de tweets objectifs et en indice 1 le nombre de tweets\n",
    "    # subjectifs\n",
    "    topics = {}\n",
    "    topics_sorted = []\n",
    "    tweets_localization = []  # liste de toutes les localisations des tweets\n",
    "    tweets_time = {str(i).zfill(2): 0 for i in range(24)}  # création des clés dans le dictionnaire dans l'ordre\n",
    "    # croissant pour améliorer le graphique de la fonction visualize_time\n",
    "    compass = []\n",
    "    specific_user = {}\n",
    "    specific_hashtag = {}\n",
    "    mentioned_by_user = {}\n",
    "    hashtag_by_user = {}\n",
    "    retweets = {}\n",
    "    retweets_sorted = []\n",
    "    languages = {}\n",
    "    languages_sorted = []\n",
    "    nb_tweets = 0\n",
    "\n",
    "    def __init__(self, tweet: dict):\n",
    "\n",
    "        if not isinstance(tweet, dict):\n",
    "            print(f'Un élément n\\'est pas un dictionnaire et n\\'a donc pas pu être analysé: {tweet}')\n",
    "        else:\n",
    "            required_keys = ['id', 'AuthorLocation', 'CreatedAt', 'RetweetCount', 'TweetLanguage', 'TweetText']\n",
    "            missing_keys = [key for key in required_keys if key not in tweet]\n",
    "            if missing_keys:\n",
    "                print(f\"Attention : Le dictionnaire {tweet} ne contient pas toutes les clés requises ! \\n\"\n",
    "                      f\"Clé{'s' if len(missing_keys) > 1 else ''} manquante{'s' if len(missing_keys) > 1 else ''} : \"\n",
    "                      f\"{', '.join(missing_keys)}\")\n",
    "            else:\n",
    "                tweet['Hashtags'] = []\n",
    "                tweet['Mentions'] = []\n",
    "                tweet['Topics'] = []\n",
    "                tweet['Polarity'] = ''\n",
    "                tweet['Subjectivity'] = ''\n",
    "                self.id = tweet['id']\n",
    "                self.localization = tweet['AuthorLocation']\n",
    "                self.date = tweet['CreatedAt']\n",
    "                self.rt = int(tweet['RetweetCount'])\n",
    "                self.langue = tweet['TweetLanguage']\n",
    "                self.texte = tweet['TweetText']\n",
    "                self.hashtag = tweet['Hashtags']\n",
    "                self.mention = tweet['Mentions']\n",
    "                self.polarity = tweet['Polarity']\n",
    "                self.subjectivity = tweet['Subjectivity']\n",
    "                self.topics = tweet['Topics']\n",
    "                self.author = tweet['Author']\n",
    "                Tweet.retweets[self.texte] = self.rt\n",
    "                self.extract_car('#')  # extraction des hashtags utilisés dans le tweet\n",
    "                self.extract_car('@')  # extraction des utilisateurs mentionnés dans le tweet\n",
    "                self.analyze_sentiment()  # analyse du sentiment du tweet (négatif, neutre ou positif) et la\n",
    "                # subjectivité\n",
    "                self.extract_topics()  # extraction des topics du tweet\n",
    "                self.list_tweet_by_author()  # ajout du tweet au dictionnaire tweets_of_users avec en clé l'auteur du\n",
    "                # tweet\n",
    "                self.analyse_language()\n",
    "                tweet['Polarity'] = self.polarity\n",
    "                tweet['Subjectivity'] = self.subjectivity\n",
    "                tweet['Topics'] = self.topics\n",
    "                tweet['Mentions'] = self.mention\n",
    "                tweet['Hashtags'] = self.hashtag\n",
    "                if self.localization != \"\":\n",
    "                    Tweet.tweets_localization.append(self.localization)\n",
    "                Tweet.tweets_time[self.date[11:13]] += 1\n",
    "                Tweet.nb_tweets += 1\n",
    "\n",
    "    @staticmethod\n",
    "    def instantiate_from_file(filepath=\"aitweets.json\"):\n",
    "        \"\"\" Fonction qui instancie les tweets présents dans un fichier json\n",
    "        \"\"\"\n",
    "        global analyse_finished\n",
    "        if filepath == '':\n",
    "            filepath = 'aitweets.json'\n",
    "        data = open(filepath, 'r', encoding='UTF-8')\n",
    "        list_tweets = [js.loads(line) for line in data]\n",
    "\n",
    "        # Noms d'utilisateurs qui seront ajoutés aux tweets afin de mieux répondre aux questions du projet étant donné\n",
    "        # qu'aucun nom d'utilisateur n'est fourni (certains noms sont plus susceptibles d'apparaître souvent pour\n",
    "        # rendre l'analyse des données plus intéressante)\n",
    "        users_names = [*['@The_Fiend'] * 18, *['@Bray_Wyatt'] * 66, *['@Shinsuke_Nakamura'] * 11, *['@Cory'] * 20,\n",
    "                       '@Chumlee', '@Liklenb', '@Jean_Valjean', '@Martin', '@Dupont', *['@IainLJBrown'] * 100,\n",
    "                       *['@Paula_Piccard'] * 50, *['@nigewillson'] * 25, *['@machinelearnTec'] * 15, '@Karl_Marx',\n",
    "                       *['@akbarth3great'] * 15, '@JoshuaBarbeau', '@sankrant', '@machinelearnflx', '@SpirosMargaris',\n",
    "                       *['@Datascience__'] * 30, *['@Charles_Henry'] * 38, *['@UEYvelines'] * 78,\n",
    "                       *['@unionetudiante_'] * 99, *['@la_classe_ouvriere'] * 16, *['@ogcnice'] * 6, '@Utah',\n",
    "                       '@chachat']\n",
    "\n",
    "        for i in range(len(list_tweets)):\n",
    "            list_tweets[i]['Author'] = random.choice(users_names)\n",
    "        try:\n",
    "            for tweet in list_tweets:\n",
    "                tweet['TweetText'] = emoji.demojize(tweet['TweetText'])\n",
    "                tweet['TweetText'] = ''.join(re.split(':[^:]+:', tweet['TweetText']))\n",
    "        except KeyError:\n",
    "            print('Pas de texte associé au tweet. Les données fournit ne possèdentprobablement pas la bonne structure')\n",
    "        for tweet in list_tweets:\n",
    "            Tweet(tweet)\n",
    "        Tweet.fill_zone_atterrissage(filepath, list_tweets)\n",
    "\n",
    "        Tweet.used_hashtag_sorted = sorted(Tweet.used_hashtag.items(),\n",
    "                                           key=operator.itemgetter(1), reverse=True)  # création d'une liste\n",
    "        # des hashtags présents dans les tweets analysés triée par ordre décroissant du nombre d'apparitions du\n",
    "        # hashtag\n",
    "        Tweet.user_mentioned_sorted = sorted(Tweet.user_mentioned.items(),\n",
    "                                             key=operator.itemgetter(1),\n",
    "                                             reverse=True)  # création d'une liste\n",
    "        # des utilisateurs mentionnés dans les tweets analysés triée par ordre décroissant du nombre de mentions\n",
    "        # de l'utilisateur\n",
    "        Tweet.tweets_of_users_sorted = dict(sorted(Tweet.tweets_of_users.items(), key=lambda x: len(x[1]),\n",
    "                                                   reverse=True))  # création d'une liste des utilisateurs triée\n",
    "        # par ordre décroissant du nombre de tweets de l'utilisateur\n",
    "        Tweet.tweets_users = [(key, len(tweets)) for key, tweets in Tweet.tweets_of_users_sorted.items()]\n",
    "        Tweet.topics_sorted = sorted(Tweet.topics.items(), key=operator.itemgetter(1), reverse=True)\n",
    "        Tweet.retweets_sorted = sorted(Tweet.retweets.items(), key=operator.itemgetter(1), reverse=True)\n",
    "        Tweet.languages_sorted = sorted(Tweet.languages.items(), key=operator.itemgetter(1), reverse=True)\n",
    "        for user in Tweet.hashtag_by_user:\n",
    "            Tweet.hashtag_by_user[user] = list(set(Tweet.hashtag_by_user[user]))\n",
    "        for user in Tweet.mentioned_by_user:\n",
    "            Tweet.mentioned_by_user[user] = list(set(Tweet.mentioned_by_user[user]))\n",
    "        analyse_finished = 1\n",
    "\n",
    "    @staticmethod\n",
    "    def fill_zone_atterrissage(filepath, list_tweets):\n",
    "        \"\"\"Remplit la zone d'atterrissage avec tous les tweets de la liste de tweets.\"\"\"\n",
    "        new_name = os.path.basename(filepath)\n",
    "        new_name = os.path.splitext(new_name)[0]\n",
    "\n",
    "        Tweet.reset_zone_atterrissage(new_name)\n",
    "        file = open(f'zone_atterrissage_{new_name}.json', 'a')\n",
    "        for tweet in list_tweets:\n",
    "            js.dump(tweet, file)\n",
    "            file.write('\\n')\n",
    "        file.close()\n",
    "        return f'zone_atterrissage_{new_name}.json'\n",
    "\n",
    "    @staticmethod\n",
    "    def reset_zone_atterrissage(name):\n",
    "        \"\"\"Supprime tout le text du fichier zone_atterrissage.txt.\"\"\"\n",
    "        open(f'zone_atterrissage_{name}.json', \"w\").close()\n",
    "\n",
    "    def analyse_language(self):\n",
    "        langue_correspondance = {\n",
    "            'en': 'anglais',\n",
    "            'fr': 'français',\n",
    "            'und': 'non-défini',\n",
    "            'ja': 'japonais',\n",
    "            'es': 'espagnol',\n",
    "            'da': 'danois',\n",
    "            'ro': 'roumain',\n",
    "            'pt': 'portugais',\n",
    "            'ko': 'coréen',\n",
    "            'de': 'allemand',\n",
    "            'in': 'maori',\n",
    "            'it': 'italien',\n",
    "            'ar': 'arabe',\n",
    "            'fa': 'persan',\n",
    "            'ca': 'catalan',\n",
    "            'fi': 'finnois'\n",
    "        }\n",
    "        self.langue = langue_correspondance[self.langue]\n",
    "        try:\n",
    "            Tweet.languages[self.langue] += 1\n",
    "        except KeyError:\n",
    "            Tweet.languages[self.langue] = 1\n",
    "\n",
    "    def extract_car(self, car: str):\n",
    "        \"\"\"Fonction qui extrait les hashtags utilisés ou les utilisateurs mentionnés dans le tweet à partir d'une base\n",
    "        de données et les ajoute à la base de données\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        car : str\n",
    "            Le caractère que l'on recherche (# si on cherche les hashtags et @ si on cherche les utilisateurs\"\n",
    "        \"\"\"\n",
    "\n",
    "        txt = self.texte\n",
    "        if car == \"@\":  # si on veut extraire les utilisateurs mentionnés\n",
    "            list_car = self.mention\n",
    "            used_car = Tweet.user_mentioned\n",
    "            name_car = re.findall(r'@\\w+', txt)\n",
    "            specific = Tweet.specific_user\n",
    "            for user in name_car:\n",
    "                try:\n",
    "                    Tweet.mentioned_by_user[self.author].append(user)\n",
    "                except KeyError:\n",
    "                    Tweet.mentioned_by_user[self.author] = [user]\n",
    "        elif car == \"#\":  # si on veut extraire les hashtags utilisés\n",
    "            list_car = self.hashtag\n",
    "            used_car = Tweet.used_hashtag\n",
    "            name_car = re.findall(r'#\\w+', txt)\n",
    "            specific = Tweet.specific_hashtag\n",
    "            for hashtag in name_car:\n",
    "                try:\n",
    "                    Tweet.hashtag_by_user[hashtag].append(self.author)\n",
    "                except KeyError:\n",
    "                    Tweet.hashtag_by_user[hashtag] = [self.author]\n",
    "        else:\n",
    "            return f'Le caractère {car} ne correspond ni aux hashtags ni aux mentions'\n",
    "        for element in name_car:\n",
    "            list_car.append(element)\n",
    "            if element in used_car:\n",
    "                used_car[element] += 1\n",
    "            else:\n",
    "                used_car[element] = 1\n",
    "            if element in specific:\n",
    "                specific[element].append(txt)\n",
    "            else:\n",
    "                specific[element] = [txt]\n",
    "\n",
    "    def analyze_sentiment(self):\n",
    "        \"\"\"Fonction qui analyse le sentiment d'un tweet (négatif, neutre ou positif)\n",
    "        \"\"\"\n",
    "        sentiment = TextBlob(self.texte).sentiment\n",
    "        if sentiment[0] < 0:\n",
    "            self.polarity = 'Negative'\n",
    "            Tweet.tweets_polarity[0] += 1\n",
    "        elif sentiment[0] == 0:\n",
    "            self.polarity = 'Neutral'\n",
    "            Tweet.tweets_polarity[1] += 1\n",
    "        elif sentiment[0] > 0:\n",
    "            self.polarity = 'Positive'\n",
    "            Tweet.tweets_polarity[2] += 1\n",
    "\n",
    "        if sentiment[1] <= 0.5:\n",
    "            self.subjectivity = 'Objective'\n",
    "            Tweet.tweets_objectivity[0] += 1\n",
    "        elif sentiment[1] >= 0.5:\n",
    "            self.subjectivity = 'Subjectif'\n",
    "            Tweet.tweets_objectivity[1] += 1\n",
    "        Tweet.compass.append([sentiment[0], sentiment[1]])\n",
    "\n",
    "    def extract_topics(self):\n",
    "        equivalence = {'artificial intelligence': ['ai', 'artificialintelligence', 'artificial', 'artif', 'arti', 'art',\n",
    "                                                   'intoainews', 'intelligence', 'artificial_intelligence',\n",
    "                                                   'artificia', 'artificialin', 'artificialintellig',\n",
    "                                                   'artificialintellige', 'artificialintell', 'ainews', 'artificialint',\n",
    "                                                   'artificialintelli', 'artific', 'ia', 'inteligenciaartificial',\n",
    "                                                   'artificialintelligen', 'artificialintel', 'artificialinte',\n",
    "                                                   'arificialinterlligence', 'artificialintelligenc', 'intela',\n",
    "                                                   'artificialintelligenc', 'ronald_teaches_artificial_intelligence',\n",
    "                                                   'artificial intelligence', 'ia', 'artifici',\n",
    "                                                   'artificialintelligencetechnology'],\n",
    "                       'machine learning': ['machinelearn', 'machinelearning', 'ml', 'deeplearning', 'learning',\n",
    "                                            'machine', 'dl', 'nlp', 'machinelea', 'datascien', 'machi', 'machinelearni'\n",
    "                                                                                                        'deeple',\n",
    "                                            'mach', 'machinelearnin', 'tensorflow', 'deepneuralnetworks',\n",
    "                                            'machinele', 'neuralnetworks', 'machin', 'machinelear', 'sciketlearn',\n",
    "                                            'deeplearningframework', '100daysofmlcode', 'machinelearni',\n",
    "                                            'machine learning', 'deeple'],\n",
    "                       'data': ['datascience', 'bigdata', 'analytics', 'data', 'datatype', 'datax', 'datas', 'datasc',\n",
    "                                'bigdat', 'datasci', 'dat', 'hdatasystems'],\n",
    "                       'programmation': ['python', 'python3', 'programming', '100daysofcode', 'coding', 'javascript',\n",
    "                                         'java', 'sql', 'code', 'cod', '100da', '100daysof', 'pytho', '100days',\n",
    "                                         'fullstack', '100day', 'coder', '100daysofcod', 'cloudcomputing', 'prog',\n",
    "                                         'flutte', 'algorithm', 'programmerlife', 'iot', 'programmation', 'numpy',\n",
    "                                         'devcommunity']\n",
    "                       }\n",
    "        for hashtag in self.hashtag:\n",
    "            hashtag = hashtag[1::].lower()\n",
    "            for topic in equivalence:\n",
    "                if hashtag in equivalence[topic]:\n",
    "                    hashtag = topic\n",
    "                self.topics.append(hashtag)\n",
    "\n",
    "        for word in self.texte.split():\n",
    "            word = word.lower()\n",
    "            for topic in equivalence:\n",
    "                if word in equivalence[topic]:\n",
    "                    word = topic\n",
    "                    self.topics.append(word)\n",
    "\n",
    "        self.topics = list(set(self.topics))\n",
    "\n",
    "        for topic in self.topics:\n",
    "            if topic in Tweet.topics:\n",
    "                Tweet.topics[topic] += 1\n",
    "            else:\n",
    "                Tweet.topics[topic] = 1\n",
    "\n",
    "    def list_tweet_by_author(self):\n",
    "        \"\"\"Fonction qui ajoute le tweet à la liste des tweets d'un utilisateur\n",
    "        \"\"\"\n",
    "        try:\n",
    "            Tweet.tweets_of_users[self.author].append(self.texte)\n",
    "        except KeyError:\n",
    "            Tweet.tweets_of_users[self.author] = [self.texte]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "Tweet.instantiate_from_file()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fonction qui retourne tout les utilisateurs utilisant un hashtag particulier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Utilisateur n°1 utilisant #AI: @ogcnice\n",
      "Utilisateur n°2 utilisant #AI: @la_classe_ouvriere\n",
      "Utilisateur n°3 utilisant #AI: @Dupont\n",
      "Utilisateur n°4 utilisant #AI: @Charles_Henry\n",
      "Utilisateur n°5 utilisant #AI: @UEYvelines\n",
      "Utilisateur n°6 utilisant #AI: @Bray_Wyatt\n",
      "Utilisateur n°7 utilisant #AI: @unionetudiante_\n",
      "Utilisateur n°8 utilisant #AI: @IainLJBrown\n",
      "Utilisateur n°9 utilisant #AI: @nigewillson\n",
      "Utilisateur n°10 utilisant #AI: @machinelearnTec\n",
      "Utilisateur n°11 utilisant #AI: @Shinsuke_Nakamura\n",
      "Utilisateur n°12 utilisant #AI: @Paula_Piccard\n",
      "Utilisateur n°13 utilisant #AI: @The_Fiend\n",
      "Utilisateur n°14 utilisant #AI: @akbarth3great\n",
      "Utilisateur n°15 utilisant #AI: @Datascience__\n",
      "Utilisateur n°16 utilisant #AI: @Cory\n",
      "Utilisateur n°1 utilisant #machine: @Charles_Henry\n",
      "Utilisateur n°2 utilisant #machine: @UEYvelines\n",
      "Utilisateur n°3 utilisant #machine: @Bray_Wyatt\n",
      "Utilisateur n°4 utilisant #machine: @unionetudiante_\n",
      "Utilisateur n°5 utilisant #machine: @IainLJBrown\n",
      "Utilisateur n°6 utilisant #machine: @machinelearnTec\n",
      "Utilisateur n°7 utilisant #machine: @Paula_Piccard\n",
      "#chocolat n'a jamais été utilisé\n"
     ]
    }
   ],
   "source": [
    "def user_mention_specific_hashtag(hashtag: str):\n",
    "    if hashtag[0] != '#':\n",
    "        hashtag = '#' + hashtag\n",
    "    try:\n",
    "        i = 1\n",
    "        for utilisateur in Tweet.hashtag_by_user[hashtag]:\n",
    "            print(f'Utilisateur n°{i} utilisant {hashtag}: {utilisateur}')\n",
    "            i += 1\n",
    "    except KeyError:\n",
    "        print(f'{hashtag} n\\'a jamais été utilisé')\n",
    "\n",
    "user_mention_specific_hashtag('#AI')\n",
    "user_mention_specific_hashtag('#machine')\n",
    "user_mention_specific_hashtag('#chocolat')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fonction qui retourne tous les utilisateurs mentionnés par un utilisateur spécifique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "@UEY n'a jamais mentionné ou tweeté\n",
      "Utilisateur n°1 mentionné par @Chumlee: @KingSazeeb\n",
      "@Charlou n'a jamais mentionné ou tweeté\n"
     ]
    }
   ],
   "source": [
    "def users_mention_by_user(user: str):\n",
    "    if user[0] != '@':\n",
    "        user = '@' + user\n",
    "    try:\n",
    "        i = 1\n",
    "        for utilisateur in Tweet.mentioned_by_user[user]:\n",
    "            print(f'Utilisateur n°{i} mentionné par {user}: {utilisateur}')\n",
    "            i += 1\n",
    "    except KeyError:\n",
    "        print(f'{user} n\\'a jamais mentionné ou tweeté')\n",
    "\n",
    "users_mention_by_user('@UEY')\n",
    "users_mention_by_user('@Chumlee')\n",
    "users_mention_by_user('@Charlou')"
   ]
  },
  {
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   "metadata": {},
   "source": [
    "Fonction qui fait apparaître un graphique montrant les top k (marche pour utilisateurs, hashtags, utilisateurs mentionnés, topics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
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      "Top 1 utilisateur : @IainLJBrown avec 288 tweets\n",
      "Top 2 utilisateur : @unionetudiante_ avec 273 tweets\n",
      "Top 3 utilisateur : @UEYvelines avec 226 tweets\n",
      "Top 4 utilisateur : @Bray_Wyatt avec 176 tweets\n",
      "Top 5 utilisateur : @Paula_Piccard avec 156 tweets\n",
      "Top 6 utilisateur : @Charles_Henry avec 107 tweets\n",
      "Top 7 utilisateur : @Datascience__ avec 81 tweets\n",
      "Top 8 utilisateur : @nigewillson avec 74 tweets\n",
      "Top 9 utilisateur : @Cory avec 56 tweets\n",
      "Top 10 utilisateur : @machinelearnTec avec 52 tweets\n",
      "Top 11 utilisateur : @The_Fiend avec 51 tweets\n"
     ]
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        },
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        },
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         "anchor": "y",
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          "text": "Nombre de mentions"
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     },
     "metadata": {},
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   ],
   "source": [
    "def get_top(list_used=Tweet.used_hashtag_sorted, k=10):\n",
    "    \"\"\"Top k hashtags ou Top k utilisateurs mentionnés\n",
    "\n",
    "    Fonction qui affiche les top k hashtags ou les tops k utilisateurs mentionnés\n",
    "\n",
    "    Paramètres\n",
    "    ----------\n",
    "    list_used : list\n",
    "        liste des hashtags et leurs occurrences ou liste des mentions d'utilisateurs et leurs occurrences\n",
    "    k : int\n",
    "        les k hashtags ou utilisateurs qui reviennent le plus\n",
    "\n",
    "    \"\"\"\n",
    "    name = []\n",
    "    occurrence = []\n",
    "\n",
    "    for i in range(0, k):\n",
    "        try:\n",
    "            if list_used == Tweet.tweets_users:\n",
    "                top = 'utilisateur'\n",
    "                mention = 'tweet'\n",
    "                x_label = 'Utilisateurs'\n",
    "                y_label = 'Nombre de tweets'\n",
    "            elif list_used == Tweet.used_hashtag_sorted:\n",
    "                top = 'hashtag'\n",
    "                mention = 'occurrence'\n",
    "                x_label = 'Hashtags'\n",
    "                y_label = 'Nombre d\\'utilisations'\n",
    "            elif list_used == Tweet.user_mentioned_sorted:\n",
    "                top = 'utilisateur mentionné'\n",
    "                mention = 'mention'\n",
    "                x_label = 'Utilisateurs mentionnés'\n",
    "                y_label = 'Nombre de mentions'\n",
    "            elif list_used == Tweet.topics_sorted:\n",
    "                top = 'sujets de discussions'\n",
    "                mention = 'mention'\n",
    "                x_label = 'topics mentionnés'\n",
    "                y_label = 'Nombre de mentions'\n",
    "            else:\n",
    "                return f'La liste {list_used} n\\'est pas compatible avec la fonction top'\n",
    "\n",
    "            print(\n",
    "                f\"Top {i + 1} {top} : {list_used[i][0]} avec {list_used[i][1]} {mention}\"\n",
    "                f\"{'s' if list_used[i][1] > 1 else ''}\")\n",
    "            name.append(list_used[i][0])\n",
    "            occurrence.append(list_used[i][1])\n",
    "        except IndexError:\n",
    "            pass\n",
    "    df = pd.DataFrame({x_label: name, y_label: occurrence})\n",
    "    fig = px.bar(df, x=x_label, y=y_label, text=y_label, title=f'Top {k} des {top}s',\n",
    "                 labels={x_label: x_label, y_label: y_label})\n",
    "    fig.show()\n",
    "    return fig\n",
    "\n",
    "get_top(Tweet.tweets_users, 11)\n",
    "get_top(Tweet.used_hashtag_sorted, 11)\n",
    "get_top(Tweet.user_mentioned_sorted, 11)\n",
    "get_top(Tweet.topics_sorted, 11)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fonction qui retourne les k tweets les plus retweetés"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 1 des tweets les plus retweetés :\n",
      "\"RT @ThomasSowell: Ours may become the first civilization destroyed, not by the power of our enemies, but by the ignorance of our teachers a…\"\n",
      " avec 10327 retweets\n",
      "\n",
      "Top 2 des tweets les plus retweetés :\n",
      "\"RT @TheTweetOfGod: Artificial intelligence will never be a bigger threat to humanity than natural stupidity.\"\n",
      " avec 6481 retweets\n",
      "\n",
      "Top 3 des tweets les plus retweetés :\n",
      "\"RT @elleprovocateur What does #Imperial College, the World Economic Forum (#WEF), #Salesforce, #Sinovatio…\"\n",
      " avec 1660 retweets\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def top_retweets(k):\n",
    "    for i in range(0, k):\n",
    "        print(\n",
    "            f\"Top {i + 1} des tweets les plus retweetés :\\n\\\"{Tweet.retweets_sorted[i][0]}\\\"\\n avec \"\n",
    "            f\"{Tweet.retweets_sorted[i][1]} retweet{'s' if Tweet.retweets_sorted[i][1] > 1 else ''}\\n\")\n",
    "\n",
    "top_retweets(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fonction qui retourne l'ensemble des tweets mentionnant un utilisateur spécifique ou l'ensemble des tweets utilisant un hashtag spécifique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweet n°1 utilisant #DataS:\n",
      "\"RT @Paula_Piccard//t.co/xg5LdUCxwb\n",
      "\n",
      "#MachineLearning  #DataS…\"\n",
      "\n",
      "Tweet n°2 utilisant #DataS:\n",
      "\"RT @Paula_Piccard//t.co/xg5LdUCxwb\n",
      "\n",
      "#MachineLearning  #DataS…\"\n",
      "\n",
      "Tweet n°3 utilisant #DataS:\n",
      "\"RT @kunaldchowdhurybackhand_index_pointing_down//t.co/Imf1GbAZev\n",
      "\n",
      "#MachineLearning #5G #DataS…\"\n",
      "\n",
      "Tweet n°1 mentionnant @Datascience__:\n",
      "\"RT @Datascience__ A Perfect Coupling? https://t.co/5SoU9EbhHG  #ArtificialIntelligence\"\n",
      "\n",
      "Tweet n°2 mentionnant @Datascience__:\n",
      "\"RT @Datascience__//t.co/ysxBNkYney  #ArtificialIntelligence\"\n",
      "\n",
      "Tweet n°3 mentionnant @Datascience__:\n",
      "\"RT @Datascience__//t.co/aaixAipN17  #ArtificialInt…\"\n",
      "\n",
      "Tweet n°4 mentionnant @Datascience__:\n",
      "\"RT @Datascience__//t.co/vszxiTneH4  #ArtificialIntelligence\"\n",
      "\n",
      "Tweet n°5 mentionnant @Datascience__:\n",
      "\"RT @Datascience__//t.co/h55YxdrYsR  #Artif…\"\n",
      "\n",
      "Tweet n°6 mentionnant @Datascience__:\n",
      "\"RT @Datascience__//t.co/OqdP5iNRrR  #ArtificialIntellige…\"\n",
      "\n",
      "Tweet n°7 mentionnant @Datascience__:\n",
      "\"RT @Datascience__//t.co/I4MfJb4rTt  #ArtificialIntelligence\"\n",
      "\n",
      "Tweet n°8 mentionnant @Datascience__:\n",
      "\"RT @Datascience__//t.co/syrNn5oAu7  #ArtificialIntelligence\"\n",
      "\n",
      "Tweet n°9 mentionnant @Datascience__:\n",
      "\"RT @Datascience__//t.co/ML477VJimG  #ArtificialIntelligence\"\n",
      "\n",
      "Tweet n°10 mentionnant @Datascience__:\n",
      "\"RT @Datascience__//t.co/PkOOcPZzEw  #ArtificialIntelligence\"\n",
      "\n",
      "Tweet n°11 mentionnant @Datascience__:\n",
      "\"RT @Datascience__//t.co/1dOkSGIh6F  #Artif…\"\n",
      "\n",
      "Tweet n°12 mentionnant @Datascience__:\n",
      "\"RT @Datascience__//t.co/hoPR2tBs3K  #ArtificialIntelligence\"\n",
      "\n",
      "Tweet n°13 mentionnant @Datascience__:\n",
      "\"RT @Datascience__ 2 Books in 1: The Ultimate Beginner's Guide on Artificial Intelligence, Data Sci…\"\n",
      "\n",
      "Tweet n°14 mentionnant @Datascience__:\n",
      "\"RT @Datascience__ The Mastery Bible: The Definitive Guide to Machine Learning, Data Science, Artificial Intelligence, Ne…\"\n",
      "\n",
      "Tweet n°15 mentionnant @Datascience__:\n",
      "\"RT @Datascience__ The Ultimate Guide to Artificial Intelligence, Neural Networks, Predictive Modellin…\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def mention_specific(mention: str):\n",
    "    if mention[0] == '@':\n",
    "        list_used = Tweet.specific_user\n",
    "        i = 1\n",
    "        try:\n",
    "            for tweet in list_used[mention]:\n",
    "                print(f'Tweet n°{i} mentionnant {mention}:\\n\"{tweet}\"\\n')\n",
    "                i += 1\n",
    "        except KeyError:\n",
    "            print('Cet utilisateur n\\'a jamais été mentionné\\n')\n",
    "    elif mention[0] == '#':\n",
    "        list_used = Tweet.specific_hashtag\n",
    "        i = 1\n",
    "        try:\n",
    "            for tweet in list_used[mention]:\n",
    "                print(f'Tweet n°{i} utilisant {mention}:\\n\"{tweet}\"\\n')\n",
    "                i += 1\n",
    "        except KeyError:\n",
    "            print('Ce hashtag n\\'a jamais été utilisé\\n')\n",
    "\n",
    "mention_specific('#DataS')\n",
    "mention_specific('@Datascience__')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fonction qui retourne le nombe de publication dans où un hashtag spécifique apparait"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Le hashtag #python apparaît dans 3 publications\n"
     ]
    }
   ],
   "source": [
    "def number_hashtag(hashtag: str):\n",
    "    \"\"\"Nombre de publications par hashtag\n",
    "\n",
    "    Fonction qui affiche le nombre de publications pour un hashtag donné\n",
    "\n",
    "    Paramètres\n",
    "    ----------\n",
    "    hashtag : str\n",
    "        nom du hashtag que l'on veut compter\n",
    "\n",
    "    \"\"\"\n",
    "    if hashtag[0] != '#':\n",
    "        hashtag = '#' + hashtag\n",
    "    try:   \n",
    "        dic_hashtag = Tweet.used_hashtag[hashtag]\n",
    "        print(f\"Le hashtag {hashtag} apparaît dans {dic_hashtag} publication\"\n",
    "            f\"{'s' if dic_hashtag > 1 else ''}\")\n",
    "    except KeyError:\n",
    "        print(f'Le hashtag {hashtag} n\\'a jamais été utilisé')\n",
    "\n",
    "number_hashtag('python')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fonction qui retourne tous les tweets d'un utlisateur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tweet 1:\n",
      "\"RT @KingSazeeb//t.co/tti5TyDuU4\"\n",
      "\n",
      "Tweet 2:\n",
      "\"Real Artificial Intelligence vs. Fake Artificial Intelligence https://t.co/Obuv4DHpsj  #ArtificialIntelligence\"\n",
      "\n",
      "Tweet 3:\n",
      "\"Tech gives new tools for grieving. A clever programmer hooked GPT-3, an AI text generator, to his web site, Project December, and now, for a low fee, you can have text chats with your lost love. #hotscience https://t.co/rnSqbf3W9M\"\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def publication_author(author: str):\n",
    "    \"\"\"Tweets de l'auteur\n",
    "    Fonction qui affiche les tweets d'un utilisateur donné\n",
    "\n",
    "    Paramètres\n",
    "    ----------\n",
    "    author : str\n",
    "        nom de l'auteur dont on veut les tweets\n",
    "\n",
    "    \"\"\"\n",
    "    if author[0] != '@':\n",
    "        author = '@' + author\n",
    "    try:\n",
    "        i = 1\n",
    "        for tweet in Tweet.tweets_of_users[author]:\n",
    "            print(f'Tweet {i}:\\n\"{tweet}\"\\n')\n",
    "            i += 1\n",
    "    except KeyError:\n",
    "        print('Cet utilisateur n\\'a pas tweeté ou n\\'existe pas')\n",
    "\n",
    "publication_author('Chumlee')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fonction qui retourne le nombre de publication d'un utilisateur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "@Chumlee a tweeté 3 fois\n"
     ]
    }
   ],
   "source": [
    "def number_publication(author: str):\n",
    "    if author[0] != '@':\n",
    "        author = '@' + author\n",
    "    try:\n",
    "        print(f'{author} a tweeté {len(Tweet.tweets_of_users[author])} fois')\n",
    "    except KeyError:\n",
    "        print('Cet utilisateur n\\'a pas tweeté ou n\\'existe pas')\n",
    "\n",
    "number_publication('Chumlee')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fonction affichant un graphique montrant le pourcentage d'utilisation des k langues les plus utlisées (% en fonction des k langues et non l'ensemble des langues)"
   ]
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    "    top_k_languages = Tweet.languages_sorted[:k]  # Sélectionner les k premières langues\n",
    "\n",
    "    # Extraire les noms des langues et les compteurs\n",
    "    languages = [lang[0] for lang in top_k_languages]\n",
    "    counts = [lang[1] for lang in top_k_languages]\n",
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    "    fig = px.pie(names=languages, values=counts, title=f'Top {k} des langues les plus utilisées')\n",
    "    fig.show()\n",
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   "source": [
    "Fonction affichant un graphique montrant la pondérance des différentes polarité sur l'ensemble des tweets ou le taux de subjectivité/objectivité"
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         "text": "Représentation de l'objectivité des tweets"
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   "source": [
    "def show_pie_chart(list_used: list):\n",
    "    if list_used == Tweet.tweets_polarity:\n",
    "        labels = 'Négatif', 'Neutre', 'Positif'\n",
    "        sizes = Tweet.tweets_polarity\n",
    "        colors = ['silver', 'lightcoral', 'mediumaquamarine']\n",
    "        title = 'Représentation de la polarité des tweets'\n",
    "    elif list_used == Tweet.tweets_objectivity:\n",
    "        labels = 'Objectif', 'Subjectif'\n",
    "        sizes = Tweet.tweets_objectivity\n",
    "        colors = ['cornflowerblue', 'lightcoral']\n",
    "        title = 'Représentation de l\\'objectivité des tweets'\n",
    "    else:\n",
    "        return f'La liste {list_used} n\\'est pas compatible avec la fonction show_pie_chart'\n",
    "    df = pd.DataFrame({'labels': labels, 'nombre de tweets': sizes})\n",
    "    fig = px.pie(df, names='labels', values='nombre de tweets', title=title, color_discrete_sequence=colors)\n",
    "    fig.show()\n",
    "\n",
    "show_pie_chart(Tweet.tweets_polarity)\n",
    "show_pie_chart(Tweet.tweets_objectivity)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fonction montrant la subjectivité des tweets en fonction de leur polarité"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
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         "text": "Visualisation des Tweets selon la Subjectivité et la Polarité"
        },
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         "anchor": "y",
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         "range": [
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        "yaxis": {
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   "source": [
    "def show_pie_chart2():\n",
    "    df = pd.DataFrame(Tweet.compass, columns=['Polarité', 'Subjectivité'])\n",
    "    fig = px.scatter(df, x='Subjectivité', y='Polarité',\n",
    "                     color='Polarité', color_continuous_scale='RdBu',\n",
    "                     title='Visualisation des Tweets selon la Subjectivité et la Polarité')\n",
    "    # Centrage du graphique\n",
    "    fig.update_layout(xaxis=dict(range=[-0.1, 1.1], zeroline=False, showline=False),\n",
    "                      yaxis=dict(range=[-1.1, 1.1], zeroline=True, zerolinewidth=1, zerolinecolor='black'),\n",
    "                      plot_bgcolor='darkseagreen', margin=dict(l=0, r=0, t=0, b=0))\n",
    "\n",
    "    # Ajout d'une ligne verticale à 0.5 sur l'axe de la subjectivité\n",
    "    fig.add_shape(type='line', x0=0.5, y0=-1.1, x1=0.5, y1=1.1, line=dict(color='black', width=1))\n",
    "\n",
    "    # Mise en forme du graphique\n",
    "    fig.update_traces(marker=dict(size=12, opacity=0.8), selector=dict(mode='markers+text'))\n",
    "    fig.update_layout(xaxis_title='Subjectivité', yaxis_title='Polarité')\n",
    "    fig.update_coloraxes(colorbar_title='Polarité')\n",
    "\n",
    "    # Réglage de l'axe x pour placer la ligne zéro à 0.5 et cacher les valeurs dépassant les seuils\n",
    "    fig.update_xaxes(showgrid=False, tickvals=[0, 0.5, 1], ticktext=['0', '0.5', '1'], tickmode='array')\n",
    "    fig.update_yaxes(showgrid=False, tickvals=[-1, 0, 1], ticktext=['-1', '0', '1'], tickmode='array')\n",
    "\n",
    "    return fig\n",
    "\n",
    "show_pie_chart2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fonction qui crée un fichier .html contenant une carte représentant la localisation de tous les tweets et le nombre de tweets par localisation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "def world_map(start=0, end=0, user='Géolocalisation_tweet'):\n",
    "    print('World Map')\n",
    "    global fin_map\n",
    "    global avancement_map\n",
    "    global analyse_finished\n",
    "    while analyse_finished != 1:\n",
    "        time.sleep(1)\n",
    "    if end == 0:\n",
    "        end = len(Tweet.tweets_localization)\n",
    "    debut = time.time()\n",
    "    geolocator = Nominatim(user_agent=user)\n",
    "    tweet_coordinates = {}\n",
    "    avancement_map = 1\n",
    "    for location in Tweet.tweets_localization[start:end]:\n",
    "        try:\n",
    "            location = location.strip()\n",
    "            if location not in tweet_coordinates:\n",
    "                location_data = geolocator.geocode(location)\n",
    "                if location_data:\n",
    "                    tweet_coordinates[location] = (location_data.latitude, location_data.longitude, location)\n",
    "            if avancement_map % 10 == 0 or avancement_map == 1:\n",
    "                print(avancement_map, '/', len(Tweet.tweets_localization))\n",
    "            avancement_map += 1\n",
    "        except ConnectionError:\n",
    "            print(f'Une erreur de connection est survenue lors de la création de la carte avec la localisation '\n",
    "                  f'suivante: {location}')\n",
    "            avancement_map += 1\n",
    "        except TimeoutError:\n",
    "            print(f'Une erreur de type Timeout est survenue lors de la création de la carte avec la localisation '\n",
    "                  f'suivante: {location}')\n",
    "            avancement_map += 1\n",
    "        except geopy.exc.GeocoderUnavailable:\n",
    "            print(f'Une erreur est survenue lors de la création de la carte avec la localisation '\n",
    "                  f'suivante: {location}. Soit la connexion n\\'a pas pu être établis avec le service de géolocalisation'\n",
    "                  f' soit le service n\\'est pas disponible')\n",
    "        except geopy.exc.GeocoderQuotaExceeded:\n",
    "            print(f'Une erreur est survenue lors de la création de la carte avec la localisation suivante: {location}'\n",
    "                  f'à cause d\\'un trop grand nombre de requête en un certain laps de temps.')\n",
    "        except Exception as error:\n",
    "            print(f'Une erreur de type {type(error)}est survenue lors de la création de la carte avec la localisation '\n",
    "                  f'suivante: {location}')\n",
    "            avancement_map += 1\n",
    "\n",
    "    tweet_counts = Counter(Tweet.tweets_localization)\n",
    "    m = folium.Map()\n",
    "\n",
    "    for location, count in tweet_counts.items():\n",
    "        if location in tweet_coordinates:\n",
    "            lat, lon = tweet_coordinates[location][:2]\n",
    "            popup_content = f'<p><b>{tweet_coordinates[location][2]}</b></p>' \\\n",
    "                            f'<p>{count} tweet{\"s\" if count > 1 else \"\"}' \\\n",
    "                            f' posté{\"s\" if count > 1 else \"\"} depuis cet endroit</p>'  # utilisation de html qui\n",
    "            # est nécessaire étant donné que le fichier contenant la carte est en html\n",
    "            popup = folium.Popup(popup_content, parse_html=False, max_width=100)\n",
    "            \"\"\"icon_image = \"icone_twitter.png\"\n",
    "            coeff = 1 + count*0.05\n",
    "            icon = folium.CustomIcon(\n",
    "                icon_image,\n",
    "                icon_size=(50*coeff, 50*coeff),\n",
    "                icon_anchor=(0, 0),\n",
    "                popup_anchor=(0, 0)\n",
    "            )\"\"\"\n",
    "            folium.Marker(location=(lat, lon), popup=popup).add_to(m)\n",
    "\n",
    "    m.save('tweet_map.html')\n",
    "    fin = time.time()\n",
    "    print('temps final:', fin - debut)\n",
    "    fin_map = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fonction permettant de visualiser le nombre de tweet en fonction de l'heure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
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           "bgcolor": "white",
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           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
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          },
          "hoverlabel": {
           "align": "left"
          },
          "hovermode": "closest",
          "mapbox": {
           "style": "light"
          },
          "paper_bgcolor": "white",
          "plot_bgcolor": "#E5ECF6",
          "polar": {
           "angularaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "radialaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
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           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "title": {
         "text": "Nombre de Tweets par Heure"
        },
        "xaxis": {
         "anchor": "y",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Heure"
         }
        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "Nombre de Tweets"
         }
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def visualize_tweet_time():\n",
    "    time = list(Tweet.tweets_time.keys())\n",
    "    nb_tweets = list(Tweet.tweets_time.values())\n",
    "    df = pd.DataFrame({'Heure': time, 'Nombre de Tweets': nb_tweets})\n",
    "    plot_time = px.line(df, x='Heure', y='Nombre de Tweets', markers=True, line_shape='linear',\n",
    "                        labels={'Heure': 'Heure', 'Nombre de Tweets': 'Nombre de Tweets'},\n",
    "                        title='Nombre de Tweets par Heure')\n",
    "    return plot_time\n",
    "\n",
    "visualize_tweet_time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interface graphique réalisée avec Gradio (ne marche pas dans le notebook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'def start():\\n    global file_path\\n    print(file)\\n    interface_init.close()\\n    interface.launch()\\n    Tweet.instantiate_from_file(file_path)\\n    return {\\n        welcome_label: gr.Label(visible=False),\\n        analyze_button: gr.Button(visible=False),\\n        upload_file_button: gr.UploadButton(visible=False),\\n        analysis: gr.Radio(visible=True)\\n    }\\n\\n\\ndef change_r(choice: str):\\n    if choice == Radio_Choices[-1]:\\n        return {plot: gr.Plot(visible=False),\\n                top: gr.Dropdown(visible=False),\\n                others: gr.Dropdown(visible=False)}\\n\\n    if choice == Radio_Choices[0]:\\n        return {top: gr.Dropdown(visible=True),\\n                others: gr.Dropdown(visible=False),\\n                plot: gr.Plot(visible=False)}\\n\\n    if choice == Radio_Choices[1]:\\n        return {others: gr.Dropdown(visible=True),\\n                top: gr.Dropdown(visible=False),\\n                plot: gr.Plot(visible=False)}\\n\\n\\ndef change_top(choice: str):\\n    if choice == \"Top hashtags\":\\n        return {plot: gr.Plot(value=get_top(list_used=Tweet.used_hashtag_sorted, k=10), visible=True)}\\n    if choice == \"Top utilisateurs\":\\n        return {plot: gr.Plot(value=get_top(list_used=Tweet.tweets_users, k=10), visible=True)}\\n    if choice == \"Top utilisateurs mentionnés\":\\n        return {plot: gr.Plot(value=get_top(list_used=Tweet.user_mentioned_sorted, k=10), visible=True)}\\n    if choice == \"Top topics\":\\n        return {plot: gr.Plot(value=get_top(list_used=Tweet.topics_sorted, k=10), visible=True)}\\n\\n\\ndef change_act(choice: str):\\n    if choice == \"Nombre de publications par utilisateur\":\\n        return {publi_user: gr.Textbox(value=number_publication(\"@Martin\"), visible=True)}\\n    if choice == \"Tous les Tweets d\\'un utilisateur\":\\n        pass\\n        # return {plot  : gr.Plot(visible=True)} #ajouter value\\n    if choice == \"Tous les utilisateurs mentionnés par un utilisateur\":\\n        pass\\n        # return {plot  : gr.Plot(visible=True)} #ajouter value\\n\\n\\ndef change_publi(choice: str):\\n    if choice == \"Nombre de publications par topic\":\\n        return {topic: gr.Textbox(visible=True)}\\n        # return {plot  : gr.Plot(visible=True)} #ajouter value\\n    if choice == \"Nombre de publications par hashtag\":\\n        return {hashtag: gr.Textbox(visible=True)}\\n        # return {plot  : gr.Plot(visible=True)} #ajouter value\\n    if choice == \"Tous les utilisateurs d\\'un hashtag\":\\n        return {hashtag: gr.Textbox(visible=True)}\\n        # return {plot  : gr.Plot(visible=True)} #ajouter value\\n\\n\\ndef change_others(choice: str):\\n    if choice == \"Heures de Tweet\":\\n        return {plot: gr.Plot(value=visualize_tweet_time(), visible=True)}\\n    if choice == \"Polarité/Subjectivité\":\\n        return {plot: gr.Plot(value=show_pie_chart2(), visible=True)}\\n    if choice == \"Répartition mondiale\":\\n        return {carte: gr.HTML(value=\\'tweet_map.html\\', visible=True)}\\n\\n\\ndef change_slider(value: int):\\n    val = value if value != 0 else 10\\n    return {plot: gr.Plot(value=get_top(k=value), visible=True),\\n            slider: gr.Slider(1, 50, val, step=1, label=\"Les Top combien voulez-vous voir ?\",\\n                              info=\"Déplacez le curseur\", visible=True, interactive=True)}\\n\\n\\ndef upload_file(file):\\n    global file_path\\n    file_path = file\\n    start()\\n\\n\\nRadio_Choices = [\"Top (4)\",\\n                 \"Caractéristiques (3)\",\\n                 \"Masquer\"]\\n\\nwith gr.Blocks(theme=gr.themes.Soft(neutral_hue=\\'cyan\\')) as interface:\\n    title = gr.Label(label=\"InPoDa\", value=\"InPoDa\", color=\"#00ACEE\")\\n    welcome_label = gr.Label(label=\"Bonjour\", value=\"Bienvenue sur InPoDa, la plateforme d\\'analyse de données de\"\\n                                                    \"réseaux sociaux.\\nVeuillez choisir un fichier à analyser\")\\n    # file_output = gr.File()\\n    upload_file_button = gr.UploadButton(\"Cliquez pour choisir le fichier à analyser\",\\n                                         file_types=[\".json\"], file_count=\"single\", visible=False)\\n    analyze_button = gr.Button(value=\"Lancer l\\'analyse\", visible=True)\\n    carte = gr.HTML(visible=False)\\n    analysis = gr.Radio(choices=Radio_Choices,\\n                        value=\"Masquer\",\\n                        label=\"Analyses\",\\n                        info=\"Que voulez-vous analyser ?\",\\n                        visible=False,\\n                        interactive=True)\\n    top = gr.Dropdown(choices=[\"Top hashtags\", \"Top utilisateurs\", \"Top utilisateurs mentionnés\", \"Top topics\"],\\n                      label=\"Top\", info=\"Veuillez sélectionner l\\'élément dont vous voulez voir le Top :\", visible=False)\\n    act = gr.Dropdown(choices=[\"Nombre de publications par utilisateur\", \"Tous les Tweets d\\'un utilisateur\",\\n                               \"Tous les utilisateurs mentionnés par un utilisateur\"],\\n                      label=\"Activité\", info=\"Veuillez sélectionner l\\'activité que vous souhaitez observer :\",\\n                      visible=False)\\n    publi = gr.Dropdown(choices=[\"Nombre de publications par topic\", \"Nombre de publications par hashtag\",\\n                                 \"Tous les utilisateurs d\\'un hashtag\"],\\n                        label=\"Catégories\", info=\"Veuillez sélectionner les publications que vous voulez analyser :\",\\n                        visible=False)\\n    others = gr.Dropdown(choices=[\"Heures de Tweet\", \"Polarité/Subjectivité\", \"Répartition mondiale\"],\\n                         label=\"Autres\", info=\"Veuillez sélectionner ce que vous souhaitez analyser :\", visible=False)\\n\\n    hashtag = gr.Textbox(info=\"Rentrez le hashtag\", visible=False, interactive=True)\\n    topic = gr.Textbox(info=\"Rentrez le topic :\", visible=False, interactive=True)\\n    plot = gr.Plot(visible=False)\\n    slider = gr.Slider(visible=False)\\n    publi_user = gr.Textbox(info=\"Quel utilisateur ?\", visible=False)\\n    # slider.change(change_r,inputs=[analysis,slider],outputs=plot)\\n    analysis.change(change_r, inputs=[analysis], outputs=[top, others, plot])\\n    top.change(change_top, inputs=[top], outputs=[plot])\\n    act.change(change_act, inputs=[act], outputs=[publi_user])\\n    publi.change(change_publi, inputs=[publi], outputs=[hashtag, topic])\\n    others.change(change_others, inputs=[others], outputs=[plot])\\n    hashtag.change(change_hashtag, inputs=[hashtag], outputs=[plot])\\n    topic.change(change_topic, inputs=[topic], outputs=[plot])\\n    interface.load(change_r, inputs=[analysis], outputs=[plot])\\n    analyze_button.click(start, outputs=[welcome_label, upload_file_button, analyze_button, analysis])\\n    upload_file_button.click(upload_file, outputs=[welcome_label, upload_file_button, analyze_button, analysis])\\n\\n\\nif __name__ == \\'__main__\\':\\n    #Tweet.instantiate_from_file(\\'aitweets.json\\')\\n    thread_map = threading.Thread(target=world_map)\\n    thread_map.start()\\n\\n    # Partie qui permettrait d\\'accélérer le processus de world map mais qui est impossible, car geopy refuse un trop\\n    # grand nombre de requêtes en un certain laps de temps. Pour régler ce problème, on pourrait essayer de passer par\\n    # un API nœud de TOR avant de changer les ip\\n\\n    thread_map = []\\n    for i in range(0, len(Tweet.tweets_localization), 100):\\n        print(\\'création du thread\\', i % 100)\\n        end = min(i + 100,\\n                  len(Tweet.tweets_localization))\\n        t = threading.Thread(target=world_map, args=(i, end, get_password(10)))\\n        thread_map.append(t)\\n        t.start()\\n        time.sleep(random.randint(1,5))\\n\\n    interface.launch()'"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"def start():\n",
    "    global file_path\n",
    "    print(file)\n",
    "    interface_init.close()\n",
    "    interface.launch()\n",
    "    Tweet.instantiate_from_file(file_path)\n",
    "    return {\n",
    "        welcome_label: gr.Label(visible=False),\n",
    "        analyze_button: gr.Button(visible=False),\n",
    "        upload_file_button: gr.UploadButton(visible=False),\n",
    "        analysis: gr.Radio(visible=True)\n",
    "    }\n",
    "\n",
    "\n",
    "def change_r(choice: str):\n",
    "    if choice == Radio_Choices[-1]:\n",
    "        return {plot: gr.Plot(visible=False),\n",
    "                top: gr.Dropdown(visible=False),\n",
    "                others: gr.Dropdown(visible=False)}\n",
    "\n",
    "    if choice == Radio_Choices[0]:\n",
    "        return {top: gr.Dropdown(visible=True),\n",
    "                others: gr.Dropdown(visible=False),\n",
    "                plot: gr.Plot(visible=False)}\n",
    "\n",
    "    if choice == Radio_Choices[1]:\n",
    "        return {others: gr.Dropdown(visible=True),\n",
    "                top: gr.Dropdown(visible=False),\n",
    "                plot: gr.Plot(visible=False)}\n",
    "\n",
    "\n",
    "def change_top(choice: str):\n",
    "    if choice == \"Top hashtags\":\n",
    "        return {plot: gr.Plot(value=get_top(list_used=Tweet.used_hashtag_sorted, k=10), visible=True)}\n",
    "    if choice == \"Top utilisateurs\":\n",
    "        return {plot: gr.Plot(value=get_top(list_used=Tweet.tweets_users, k=10), visible=True)}\n",
    "    if choice == \"Top utilisateurs mentionnés\":\n",
    "        return {plot: gr.Plot(value=get_top(list_used=Tweet.user_mentioned_sorted, k=10), visible=True)}\n",
    "    if choice == \"Top topics\":\n",
    "        return {plot: gr.Plot(value=get_top(list_used=Tweet.topics_sorted, k=10), visible=True)}\n",
    "\n",
    "\n",
    "def change_act(choice: str):\n",
    "    if choice == \"Nombre de publications par utilisateur\":\n",
    "        return {publi_user: gr.Textbox(value=number_publication(\"@Martin\"), visible=True)}\n",
    "    if choice == \"Tous les Tweets d'un utilisateur\":\n",
    "        pass\n",
    "        # return {plot  : gr.Plot(visible=True)} #ajouter value\n",
    "    if choice == \"Tous les utilisateurs mentionnés par un utilisateur\":\n",
    "        pass\n",
    "        # return {plot  : gr.Plot(visible=True)} #ajouter value\n",
    "\n",
    "\n",
    "def change_publi(choice: str):\n",
    "    if choice == \"Nombre de publications par topic\":\n",
    "        return {topic: gr.Textbox(visible=True)}\n",
    "        # return {plot  : gr.Plot(visible=True)} #ajouter value\n",
    "    if choice == \"Nombre de publications par hashtag\":\n",
    "        return {hashtag: gr.Textbox(visible=True)}\n",
    "        # return {plot  : gr.Plot(visible=True)} #ajouter value\n",
    "    if choice == \"Tous les utilisateurs d'un hashtag\":\n",
    "        return {hashtag: gr.Textbox(visible=True)}\n",
    "        # return {plot  : gr.Plot(visible=True)} #ajouter value\n",
    "\n",
    "\n",
    "def change_others(choice: str):\n",
    "    if choice == \"Heures de Tweet\":\n",
    "        return {plot: gr.Plot(value=visualize_tweet_time(), visible=True)}\n",
    "    if choice == \"Polarité/Subjectivité\":\n",
    "        return {plot: gr.Plot(value=show_pie_chart2(), visible=True)}\n",
    "    if choice == \"Répartition mondiale\":\n",
    "        return {carte: gr.HTML(value='tweet_map.html', visible=True)}\n",
    "\n",
    "\n",
    "def change_slider(value: int):\n",
    "    val = value if value != 0 else 10\n",
    "    return {plot: gr.Plot(value=get_top(k=value), visible=True),\n",
    "            slider: gr.Slider(1, 50, val, step=1, label=\"Les Top combien voulez-vous voir ?\",\n",
    "                              info=\"Déplacez le curseur\", visible=True, interactive=True)}\n",
    "\n",
    "\n",
    "def upload_file(file):\n",
    "    global file_path\n",
    "    file_path = file\n",
    "    start()\n",
    "\n",
    "\n",
    "Radio_Choices = [\"Top (4)\",\n",
    "                 \"Caractéristiques (3)\",\n",
    "                 \"Masquer\"]\n",
    "\n",
    "with gr.Blocks(theme=gr.themes.Soft(neutral_hue='cyan')) as interface:\n",
    "    title = gr.Label(label=\"InPoDa\", value=\"InPoDa\", color=\"#00ACEE\")\n",
    "    welcome_label = gr.Label(label=\"Bonjour\", value=\"Bienvenue sur InPoDa, la plateforme d'analyse de données de\"\n",
    "                                                    \"réseaux sociaux.\\nVeuillez choisir un fichier à analyser\")\n",
    "    # file_output = gr.File()\n",
    "    upload_file_button = gr.UploadButton(\"Cliquez pour choisir le fichier à analyser\",\n",
    "                                         file_types=[\".json\"], file_count=\"single\", visible=False)\n",
    "    analyze_button = gr.Button(value=\"Lancer l'analyse\", visible=True)\n",
    "    carte = gr.HTML(visible=False)\n",
    "    analysis = gr.Radio(choices=Radio_Choices,\n",
    "                        value=\"Masquer\",\n",
    "                        label=\"Analyses\",\n",
    "                        info=\"Que voulez-vous analyser ?\",\n",
    "                        visible=False,\n",
    "                        interactive=True)\n",
    "    top = gr.Dropdown(choices=[\"Top hashtags\", \"Top utilisateurs\", \"Top utilisateurs mentionnés\", \"Top topics\"],\n",
    "                      label=\"Top\", info=\"Veuillez sélectionner l'élément dont vous voulez voir le Top :\", visible=False)\n",
    "    act = gr.Dropdown(choices=[\"Nombre de publications par utilisateur\", \"Tous les Tweets d'un utilisateur\",\n",
    "                               \"Tous les utilisateurs mentionnés par un utilisateur\"],\n",
    "                      label=\"Activité\", info=\"Veuillez sélectionner l'activité que vous souhaitez observer :\",\n",
    "                      visible=False)\n",
    "    publi = gr.Dropdown(choices=[\"Nombre de publications par topic\", \"Nombre de publications par hashtag\",\n",
    "                                 \"Tous les utilisateurs d'un hashtag\"],\n",
    "                        label=\"Catégories\", info=\"Veuillez sélectionner les publications que vous voulez analyser :\",\n",
    "                        visible=False)\n",
    "    others = gr.Dropdown(choices=[\"Heures de Tweet\", \"Polarité/Subjectivité\", \"Répartition mondiale\"],\n",
    "                         label=\"Autres\", info=\"Veuillez sélectionner ce que vous souhaitez analyser :\", visible=False)\n",
    "\n",
    "    hashtag = gr.Textbox(info=\"Rentrez le hashtag\", visible=False, interactive=True)\n",
    "    topic = gr.Textbox(info=\"Rentrez le topic :\", visible=False, interactive=True)\n",
    "    plot = gr.Plot(visible=False)\n",
    "    slider = gr.Slider(visible=False)\n",
    "    publi_user = gr.Textbox(info=\"Quel utilisateur ?\", visible=False)\n",
    "    # slider.change(change_r,inputs=[analysis,slider],outputs=plot)\n",
    "    analysis.change(change_r, inputs=[analysis], outputs=[top, others, plot])\n",
    "    top.change(change_top, inputs=[top], outputs=[plot])\n",
    "    act.change(change_act, inputs=[act], outputs=[publi_user])\n",
    "    publi.change(change_publi, inputs=[publi], outputs=[hashtag, topic])\n",
    "    others.change(change_others, inputs=[others], outputs=[plot])\n",
    "    hashtag.change(change_hashtag, inputs=[hashtag], outputs=[plot])\n",
    "    topic.change(change_topic, inputs=[topic], outputs=[plot])\n",
    "    interface.load(change_r, inputs=[analysis], outputs=[plot])\n",
    "    analyze_button.click(start, outputs=[welcome_label, upload_file_button, analyze_button, analysis])\n",
    "    upload_file_button.click(upload_file, outputs=[welcome_label, upload_file_button, analyze_button, analysis])\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    #Tweet.instantiate_from_file('aitweets.json')\n",
    "    thread_map = threading.Thread(target=world_map)\n",
    "    thread_map.start()\n",
    "\n",
    "    # Partie qui permettrait d'accélérer le processus de world map mais qui est impossible, car geopy refuse un trop\n",
    "    # grand nombre de requêtes en un certain laps de temps. Pour régler ce problème, on pourrait essayer de passer par\n",
    "    # un API nœud de TOR avant de changer les ip\n",
    "\n",
    "    thread_map = []\n",
    "    for i in range(0, len(Tweet.tweets_localization), 100):\n",
    "        print('création du thread', i % 100)\n",
    "        end = min(i + 100,\n",
    "                  len(Tweet.tweets_localization))\n",
    "        t = threading.Thread(target=world_map, args=(i, end, get_password(10)))\n",
    "        thread_map.append(t)\n",
    "        t.start()\n",
    "        time.sleep(random.randint(1,5))\n",
    "\n",
    "    interface.launch()\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lancement de l'interface graphique (et donc de l'analyse des tweets) et du thread world map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'with gr.Blocks(theme=gr.themes.Soft()) as demo:\\n    title = gr.Label(label = \"InPoDa\", value=\"InPoda\", color=\"black\")\\n    debut_label = gr.Label(label=\"\",value=\"Bienvenue sur la plateforme qui va te permettre d\\'analyser des tweets\")\\n    with gr.Tab(\"Toutes les langues utilisées\"):\\n        plot = gr.Plot(label=\"Plot\", value=languages_plot)\\n        demo.load(languages_plot, outputs=[plot])\\n    with gr.Tab(\"Lieux\"):\\n        gr.Button(\"là c\\'est bon c\\'est la map \")\\n        gr.HTML(value=\\'arattaa.html\\', visible=True)\\n    with gr.Tab(\"Hashtags\"):\\n        plot1 = gr.Plot(label=\"Hashtags\", value=get_top)\\n        demo.load(get_top(Tweet.used_hashtag_sorted), outputs=[plot1])   \\n        inp = gr.Textbox(placeholder=\"What is your name?\")\\n        out = gr.Textbox()\\n        inp.change(mention_specific, inp, out)        \\n    with gr.Tab(\"Top 8\"):\\n        gr.Button(\"Utilisateurs\")\\n        gr.Button(\"Utilisateurs mentionnés\")\\n        gr.Button(\"Les hashtags\")\\n        gr.Button(\"Topic\")\\n    with gr.Tab(\"Utilisateurs\"):\\n        plot2 = gr.Plot(label = \"Utilisateurs\", value=mention_specific)\\n        demo.load(mention_specific(Tweet.user_mentioned_sorted), outputs=[plot2])\\n    with gr.Tab(\\'Sentiments\\'):\\n        gr.Plot(value=show_pie_chart2, visible=True)'"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"with gr.Blocks(theme=gr.themes.Soft()) as demo:\n",
    "    title = gr.Label(label = \"InPoDa\", value=\"InPoda\", color=\"black\")\n",
    "    debut_label = gr.Label(label=\"\",value=\"Bienvenue sur la plateforme qui va te permettre d'analyser des tweets\")\n",
    "    with gr.Tab(\"Toutes les langues utilisées\"):\n",
    "        plot = gr.Plot(label=\"Plot\", value=languages_plot)\n",
    "        demo.load(languages_plot, outputs=[plot])\n",
    "    with gr.Tab(\"Lieux\"):\n",
    "        gr.Button(\"là c'est bon c'est la map \")\n",
    "        gr.HTML(value='arattaa.html', visible=True)\n",
    "    with gr.Tab(\"Hashtags\"):\n",
    "        plot1 = gr.Plot(label=\"Hashtags\", value=get_top)\n",
    "        demo.load(get_top(Tweet.used_hashtag_sorted), outputs=[plot1])   \n",
    "        inp = gr.Textbox(placeholder=\"What is your name?\")\n",
    "        out = gr.Textbox()\n",
    "        inp.change(mention_specific, inp, out)        \n",
    "    with gr.Tab(\"Top 8\"):\n",
    "        gr.Button(\"Utilisateurs\")\n",
    "        gr.Button(\"Utilisateurs mentionnés\")\n",
    "        gr.Button(\"Les hashtags\")\n",
    "        gr.Button(\"Topic\")\n",
    "    with gr.Tab(\"Utilisateurs\"):\n",
    "        plot2 = gr.Plot(label = \"Utilisateurs\", value=mention_specific)\n",
    "        demo.load(mention_specific(Tweet.user_mentioned_sorted), outputs=[plot2])\n",
    "    with gr.Tab('Sentiments'):\n",
    "        gr.Plot(value=show_pie_chart2, visible=True)\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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