pages/analysis_template.py

from dash import html, dcc, callback, Input, Output, State
import dash_mantine_components as dmc
import plotly.express as px
import pandas as pd
import glob
import json
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import plotly.figure_factory as ff
from collections import Counter
import numpy as np
import re
from wordcloud import WordCloud
import networkx as nx
import matplotlib.pyplot as plt
import base64
from io import BytesIO
import textwrap

def readExcel(data):
    return pd.read_excel(data)

def datatomultiplerowswithoutcomment(df, column_name, sep=', '):
    df_rows = df.copy()
    df_rows[column_name] = df_rows[column_name].str.split(sep)
    df_rows = df_rows.explode(column_name)
    df_rows = df_rows[df_rows[column_name] != '']
    df_rows[column_name] = df_rows[column_name].apply(lambda x: str(x).split(' (')).apply(lambda x: x[0])
    return df_rows

def barplotmonovariablecount(df, column_name, title):
    df_monovariablecount = df.groupby(column_name).size().reset_index(name='obs')
    df_monovariablecount = df_monovariablecount.sort_values(by=['obs'])
    fig_monovariablecount = px.bar(df_monovariablecount, x='obs', y=column_name, width=800, height=400, orientation='h', color='obs', template="plotly_dark",title=title, labels={'obs':'nombre'}, color_continuous_scale="Teal", text_auto=True).update_layout(paper_bgcolor="#060621",font=dict(size=10,color="#ffffff"),autosize=True, coloraxis_showscale=False).update_traces(hovertemplate=df_monovariablecount[column_name] + ' <br>Nombre : %{x}', y=[y[:100] + "..." for y in df_monovariablecount[column_name]], showlegend=False)
    return fig_monovariablecount

def barplotbivariablecount(df, column_name1, column_name2, title):
    df_bivariablecount = df.groupby([column_name1, column_name2]).size().reset_index(name='obs')
    df_bivariablecount = df_bivariablecount.sort_values(by=['obs'])
    fig_bivariablecount = px.bar(df_bivariablecount, y=column_name1, x='obs', orientation='h', width=800, height=400, color=column_name2, template="plotly_dark", title=title, labels={'obs':'nombre'}, color_discrete_sequence=px.colors.qualitative.Safe, text_auto=True).update_layout(font=dict(size=10,color="#ffffff"),paper_bgcolor="#060621",autosize=True, coloraxis_showscale=False)

    return fig_bivariablecount

def multiwordcloud(df):
    exclure_mots = ['ue', 'précisez', 'd', 'du', 'de', 'la', 'las', 'des', 'le', 'et', 'est', 'elle', 'une', 'en', 'que', 'aux', 'qui', 'ces', 'les', 'dans', 'sur', 'l', 'un', 'pour', 'par', 'il', 'ou', 'à', 'ce', 'a', 'sont', 'cas', 'plus', 'leur', 'se', 's', 'vous', 'au', 'c', 'aussi', 'toutes', 'autre', 'comme']

    countFigure = 1
    thematiques = ['Durable','Résilient','Sûr','Inclusive']
    figures_list = []
    for thematique in thematiques:
        fig = plt.figure(countFigure,figsize=(10,12),facecolor="#060621")
        count=1
        diplomes = ['BUT','Licence','Licence professionnelle','Master']
        for diplome in diplomes:
            df_test = df[(df['Thématiques ODD11'] == thematique) & (df['Diplôme'] == diplome)]
            list_test = df_test["Référence et intitulé de l'UE"].tolist()
            words=". ".join(list_test)
            words = words.lower()
            words=words.replace(r'[-./?!,":;()\']',' ')
            if words:
                wordcloud = WordCloud(background_color='#ffffff', stopwords=exclure_mots, max_words=100).generate(words)
                if count <= 2:
                    plt.subplot(1,2,count)
                else:
                    plt.subplot(2,2,count)
                plt.imshow(wordcloud,interpolation="bilinear")
                plt.axis('off')
                plt.title(thematique + ' - ' + diplome,fontdict={'fontsize':'medium','color':'#ffffff'})
                count = count + 1
        
        # Save it to a temporary buffer.
        buf = BytesIO()
        fig.savefig(buf, format="png")
        # Embed the result in the html output.
        fig_data = base64.b64encode(buf.getbuffer()).decode("ascii")
        figures_list.append(f'data:image/png;base64,{fig_data}')
        countFigure = countFigure + 1
    return figures_list

def matrixlist(df):
    list_thematique = df["Intitulé"].values.tolist()
    #list_thematique = sorted(list_thematique)
    list_thematique = list(set(list_thematique))
    matrix = pd.DataFrame(0, index=list_thematique, columns=['Durable','Inclusive','Sûr','Résilient'])
    for formation in list_thematique:
        for thematique in ['Durable','Inclusive','Sûr','Résilient']:
            df_test = df[df['Thématiques ODD11'] == thematique]
            if formation in df_test.values :
                matrix.loc[formation, thematique] = 1 # Replace with actual condition logic

    return matrix

def matrixcorrelation(matrix,df):
    list = df["Intitulé"].values.tolist()
    fig = go.Figure(data=go.Heatmap(
    z=matrix.values,
    x=matrix.columns,
    y=matrix.index,
    colorscale=[
        [0, 'rgba(6,6,33,1)'],
        [0.2, 'rgba(6,6,33,1)'],
        [0.2, '#FF69B4'],  # Rose pour technique
        [0.4, '#FF69B4'],
        [0.4, '#4169E1'],  # Bleu pour management
        [0.6, '#4169E1'],
        [0.6, '#32CD32'],  # Vert pour environnement
        [0.8, '#32CD32'],
        [0.8, '#FFD700'],  # Jaune pour économie
        [1.0, '#32CD32']
        ],
        showscale=False,
    ))

    # Ajout des bordures aux cellules
    fig.update_traces(
        xgap=1,
        ygap=1,
    )
    
    # Mise en forme
    fig.update_layout(
        title='Matrice des thématiques ODD11<br>par formation',
        xaxis=dict(
            side='top',
            tickangle=45,
            tickfont=dict(size=10),
        ),
        yaxis=dict(
            autorange='reversed',
            tickfont=dict(size=10),
        ),
        width=1200,
        #height=300,
        height=len(list) * 20,
        template='plotly_dark',
        paper_bgcolor = 'rgba(6,6,33,1)',
        plot_bgcolor='rgba(6,6,33,1)',
        margin=dict(
            t=100,
            l=300,
            r=100,
            b=50
        ),
        #annotations=annotations,
        hovermode="x unified",hoverlabel=dict(
            bgcolor='rgba(8,8,74,1)',
            font_size=10,
        )
    )

    # Personnalisation du style des axes
    fig.update_xaxes(
        #showspikes=True,
        showgrid=True,
        gridwidth=1,
        gridcolor='lightgrey',
    )

    fig.update_yaxes(
        #showspikes=True,
        showgrid=True,
        gridwidth=1,
        gridcolor='lightgrey',
    )

    # Ajout d'un hover template personnalisé
    hover_text = []

    df_info = df[["Thématiques ODD11","Référence et intitulé de l'UE","Pratiques pédagogiques","Intitulé"]].copy()
    #df_info = df_info.drop_duplicates(subset=['Thématiques ODD11'])
    df_info.set_index("Thématiques ODD11", inplace=True)

    for idx in matrix.index:
        row = []
        for col in matrix.columns:
            if matrix.loc[idx,col] == 1:
                #df_psycho = df_score[(df_score['Thématiques Pedago'].str.contains(row['Thématiques Pedago'])) & (df_score['labStructName_s'] == row['labStructName_s'])]

                df_extract = df_info.loc[col]
                df_test = df_extract[df_extract["Intitulé"] == idx]
                ue = df_test["Référence et intitulé de l'UE"].values.tolist()[0]
                pedagogie = df_test["Pratiques pédagogiques"].values.tolist()[0]
                label_y = idx
                row.append(
                  f'<b>💼 Formation: {"<br>".join(textwrap.wrap(label_y,width=70))}</b><br>' +
                  f'<b>📣 Thématique ODD11: {col.capitalize()}</b><br><br>' +
                  f'💡 Référence et intitulé de l\'UE : {"<br>".join(textwrap.wrap(ue,width=80))}<br><br>' +
                  f'📚 Pratiques pédagogiques: <br>' + str(pedagogie) + '<br><br>'
                )
            else:
                row.append('')

        hover_text.append(row)

    fig.update_traces(
        hovertemplate="%{customdata}<extra></extra>",
        customdata=hover_text,
        #y=[y[0:-10].replace('(','') if y.find('(essential)')!=-1 or y.find('(optional)')!=-1 else y for y in color_values.index]
    )
    return fig

def create_analysis_page(title, label, data):
    # This is dummy data for the bar chart
    df = readExcel(data)
    if label == "Analyse ODD 11 formation":
        df_figure = datatomultiplerowswithoutcomment(df, 'Thématiques ODD11', sep=', ')
        fig1 = barplotmonovariablecount(df_figure, f'Thématiques ODD11', f"Répartition des thématiques ODD11")
        fig2 = barplotbivariablecount(df_figure, f'Thématiques ODD11', f'Diplôme', f"Répartition des thématiques ODD11 par type diplôme")
        fig3 = multiwordcloud(df_figure)
        fig4 = matrixcorrelation(matrixlist(df_figure),df_figure)
    else:
        fig1 = go.Figure()
        fig1.add_annotation(text="Aucun fichier de données fourni pour cette analyse.", xref="paper", yref="paper", showarrow=False, font=dict(size=20))
        fig2 = go.Figure()
        fig2.add_annotation(text="Aucun fichier de données fourni pour cette analyse.", xref="paper", yref="paper", showarrow=False, font=dict(size=20))
        fig3 = plt.Figure()
        buf = BytesIO()
        fig3.savefig(buf, format="png")
        fig_data = base64.b64encode(buf.getbuffer()).decode("ascii")
        fig3 = f'data:image/png;base64,{fig_data}'
        fig4 = go.Figure()
        fig4.add_annotation(text="Aucun fichier de données fourni pour cette analyse.", xref="paper", yref="paper", showarrow=False, font=dict(size=20))
        
    #return dmc.Container(
    return dmc.Container(
        [
            dmc.Title(title, order=2, mb="lg"),
            dmc.Grid(
                [
                    dmc.GridCol(dcc.Loading(dcc.Graph(figure=fig1)), span=12),
                    dmc.GridCol(dcc.Loading(dcc.Graph(figure=fig2)), span=12),
                    dmc.GridCol(dcc.Loading(dcc.Graph(figure=fig4)), span=12),
                    dmc.GridCol(
                        [
                            dcc.Loading(html.Img(src=fig3[0], style={'width':'100%', 'height':'auto','padding':'0px','margin-top':'-300px'}),
                            ),
                            dcc.Loading(html.Img(src=fig3[1], style={'width':'100%', 'height':'auto','padding':'0px','margin-top':'-200px'}),
                            ),
                            dcc.Loading(html.Img(src=fig3[2], style={'width':'100%', 'height':'auto','padding':'0px','margin-top':'-200px'}),
                            ),
                            dcc.Loading(html.Img(src=fig3[3], style={'width':'100%', 'height':'auto','padding':'0px','margin-top':'-200px'}),),
                            
                        ],
                    span=12),
                    #dmc.GridCol(
                    #    [
                    #        dmc.Title("Résumé de l'analyse", order=3),
                    #        dmc.Text(
                    #            "Génération de la note de synthèse en temps réel...",
                    #            id="summary-note",
                    #        ),
                    #    ],
                    #    span=4,
                    #),
                ]
            ),
        ],
        fluid=True,
        p="lg",
        pt="xl"
    )