Py.Cafe

import dash
from dash import dcc, html
import dash_bootstrap_components as dbc
from dash.dependencies import Input, Output
import pandas as pd
import plotly.express as px
import string
import nltk
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.util import ngrams
from collections import Counter
from nltk.stem import WordNetLemmatizer
from nltk.sentiment.vader import SentimentIntensityAnalyzer

nltk.download('punkt')
nltk.download('stopwords')
nltk.download('wordnet')
nltk.download('vader_lexicon')

# Prepare the data
df = pd.read_excel("NYT Fiction Bestsellers.xlsx", sheet_name=1)[::-1]
df_subset = df.drop_duplicates(subset=['title'])
df_subset['title'] = df_subset['title'].astype(str)
df_subset['desc'] = df_subset['desc'].astype(str)
df_subset['year'] = df_subset.bestsellers_date.dt.year

lemmatizer = WordNetLemmatizer()
stop_words = set(stopwords.words('english')) | set(string.punctuation)

def generate_ngrams(text, n):
    tokens = word_tokenize(text.lower())
    tokens = [word for word in tokens if word.isalpha() and word not in stop_words]
    tokens = [lemmatizer.lemmatize(word) for word in tokens]
    n_grams = ngrams(tokens, n)
    return [' '.join(gram) for gram in n_grams]

def plot_top_ngrams_plotly(freq_counter):
    top_n = freq_counter.most_common(10)
    ngrams, counts = zip(*top_n)
    fig = px.bar(y=list(counts), x=list(ngrams), template="ggplot2", labels={'x':'', 'y':''}, 
                 text_auto=True,
                )
    fig.update_layout(paper_bgcolor='rgb(240, 240, 240)', plot_bgcolor='rgb(240, 240, 240)'
    )
   
    fig.update_yaxes(visible=False)
    return fig

def get_sentiment(text):
    analyzer = SentimentIntensityAnalyzer()
    scores = analyzer.polarity_scores(text)
    return scores['compound']

radio_style = {
    'display': 'flex',
    'flex-direction': 'row',
    'justify-content': 'space-between',
    'padding': '5px',
    'border': '2px solid',
    'border-radius': '5px',
    'boxShadow': '3px 3px 3px rgba(10, 10, 10, 0.3)',
    'font-family': 'Aharoni, sans-serif',
    'font-size': '20px',
}

header_style={'text-align': 'center', 'margin': '10px','padding': '10px'}
    
# Initialize Dash app
app = dash.Dash(__name__, external_stylesheets=[dbc.themes.JOURNAL])

app.title = "NY Times Fiction Best-Sellers"

app.layout = dbc.Container([
    dbc.Row([
        dbc.Col(html.H1("NYT Bestseller Sentiment & Trends", className="title"), width=12)  # Clase CSS para el título
    ]),
    dbc.Row([
        dbc.Col(html.H5("Sentiment analysis reveals trends in NYT fiction bestsellers: titles, descriptions, sentiment, bigrams/trigrams, and author sentiment."), width=12) 
    ]),
    dbc.Row([
        dbc.Col(dbc.RadioItems(
            id='radio-buttons',
            options=[{'label': str(year), 'value': year} for year in df_subset.year.unique()],
            value=2018,
            inline=True,
            style=radio_style
        ), width=12)
    ]),
    dbc.Row([
       
        dbc.Col([
            html.H5("Top 10 Title Bigrams", style=header_style),html.Hr(),
            dcc.Graph(id='bigrams-chart', className="dash-graph")], width=6),  
        dbc.Col([
            html.H5("Top 10 Description Trigrams", style=header_style),html.Hr(),
            dcc.Graph(id='trigrams-chart', className="dash-graph")], width=6)  
    ]),
    html.Hr(),
    dbc.Row([
            dbc.Col([
            html.H5("Book Description Sentiment by Year", style=header_style),
            html.Hr(),
            dcc.Graph(id='sentiment-chart')], width=5),
        dbc.Col(html.Div([
            html.H5("Comparative Sentiment Distribution of Bestselling Authors", style=header_style),
            html.Hr(),
            html.Button('Update Authors', id='update-authors-button', className="update-button"),
            dcc.Graph(id='sentiment_author-boxchart')
    ]), width=7), 
        dbc.Col(
            html.H5("Sentiment scores range from -1 (VERY NEGATIVE) to +1 (VERY POSITIVE). Scores close to 0 indicate neutral sentiment.", style=header_style), width=12)
]),
   
], fluid=True, style={'backgroundColor': '#f0f0f0'})  

@app.callback(
    Output('bigrams-chart', 'figure'),
    Output('trigrams-chart', 'figure'),
    Output('sentiment-chart', 'figure'),
    Output('sentiment_author-boxchart', 'figure'),
    Input('radio-buttons', 'value'),
    Input('update-authors-button', 'n_clicks') # Input del botón
)
def update_charts(year, n_clicks):
    filtered_df = df_subset[df_subset.year == year]

    bigrams = [generate_ngrams(title, 2) for title in filtered_df['title']]
    bigram_freq = Counter([gram for sublist in bigrams for gram in sublist])
    bigrams_fig = plot_top_ngrams_plotly(bigram_freq)
    

    trigrams = [generate_ngrams(desc, 3) for desc in filtered_df['desc']]
    trigram_freq = Counter([gram for sublist in trigrams for gram in sublist])
    trigrams_fig = plot_top_ngrams_plotly(trigram_freq)
    

    filtered_df['sentiment'] = filtered_df['desc'].apply(get_sentiment)
    sentiment_fig = px.histogram(
        filtered_df, x="sentiment", nbins=10, histnorm='percent',range_x=[-1,1],
        template='ggplot2', text_auto= '.2f', labels={'sentiment':''}
    )
    sentiment_fig.update_layout(paper_bgcolor='rgb(240, 240, 240)', plot_bgcolor='rgb(240, 240, 240)')

    sentiment_fig.update_yaxes(visible=False)


    if n_clicks is None or n_clicks == 0:  
        author_to_watch = filtered_df.author.sample(5).tolist()
    elif n_clicks > 0: 
        author_to_watch = filtered_df.author.sample(5).tolist()

    author_to_watch = filtered_df.author.sample(5).tolist()
    author_df = filtered_df[filtered_df['author'].isin(author_to_watch)]
    author_df['sentiment'] = author_df['desc'].apply(get_sentiment)
    sentimen_author_fig = px.box(author_df, x="sentiment", y='author',
                                 range_x=[-1,1],
                                 color_discrete_sequence=px.colors.sequential.Bluered_r,
                                 template='ggplot2', labels={'sentiment':'', 'author':''})
    sentimen_author_fig.update_layout(paper_bgcolor='rgb(240, 240, 240)', plot_bgcolor='rgb(240, 240, 240)')

    

    return bigrams_fig, trigrams_fig, sentiment_fig, sentimen_author_fig