import dash
from dash import Dash, html, dcc, callback, Output, Input
import plotly.express as px
from app import app
import pandas as pd

import datetime
import requests
from io import StringIO
from datetime import date

# from jupyter_dash import JupyterDash
# from dash.dependencies import Input, Output
import dash_bootstrap_components as dbc
import plotly.express as px

server = app.server

url='https://drive.google.com/file/d/1NaXOYHQFF5UO5rQr4rn8Lr3bkYMSOq4_/view?usp=sharing'
url='https://drive.google.com/uc?id=' + url.split('/')[-2]

# reading of file
df = pd.read_csv(url)

df['date'] = pd.to_datetime(df['date'])

unique_domains = df['domain_folder_name'].unique()
print(unique_domains)

unique_topics = df['Topic'].unique()
print(unique_topics)

#copying a column 
df["Veículos de notícias"] = df["domain_folder_name"]

# df = df.rename(columns={df.columns[4]: "Veículos de notícias"})

df['FinBERT_label'] = df['FinBERT_label'].astype(str)
df['FinBERT_label'].replace({
    '3.0': 'positive',
    '2.0': 'neutral',
    '1.0': 'negative'
}, inplace=True)



counts = df.groupby(['date', 'Topic', 'domain_folder_name', 'FinBERT_label']).size().reset_index(name='count')
counts['count'] = counts['count'].astype('float64')
counts['rolling_mean_counts'] = counts['count'].rolling(window=30, min_periods=2).mean()

df_pos = counts[[x in ['positive'] for x in counts.FinBERT_label]]
df_neu = counts[[x in ['neutral'] for x in counts.FinBERT_label]]
df_neg = counts[[x in ['negative'] for x in counts.FinBERT_label]]



app.layout = dbc.Container([   
    dbc.Row([ # row 1
        dbc.Col([html.H1('Evolução temporal de sentimento em títulos de notícias')],
        className="text-center mt-3 mb-1")]),
    
    dbc.Row([ # row 2
        dbc.Label("Selecione um período (mm/dd/aaaa):", className="fw-bold")]),

    dbc.Row([ # row 3
        dcc.DatePickerRange(
            id='date-range',
            min_date_allowed=df['date'].min().date(),
            max_date_allowed=df['date'].max().date(),
            initial_visible_month=df['date'].min().date(),
            start_date=df['date'].min().date(),
            end_date=df['date'].max().date())]),

    dbc.Row([ # row 4
        dbc.Label("Escolha um tópico:", className="fw-bold")
    ]),

    dbc.Row([ # row 5
        dbc.Col(
            dcc.Dropdown(
                id="topic-selector",
                options=[
                    {"label": topic, "value": topic} for topic in unique_topics
                ],
                value="Imigrantes",  # Set the initial value
                style={"width": "50%"})


        )
    ]),
    
    dbc.Row([ # row 6
            dbc.Col(dcc.Graph(id='line-graph-1'))
    ]),

    dbc.Row([ # row 7 but needs to be updated
        dbc.Col(dcc.Graph(id="bar-graph-1"))
    ]),

    dbc.Row([ # row 7
            dbc.Label("Escolha um site de notícias:", className="fw-bold")
    ]),

    dbc.Row([ # row 8
        dbc.Col(
            dcc.Dropdown(
                id="domain-selector",
                options=[
                    {"label": domain, "value": domain} for domain in unique_domains
                ],
                value="expresso-pt",  # Set the initial value
                style={"width": "50%"})


        )
    ]),

    dbc.Row([ # row 9
            dbc.Col(dcc.Graph(id='line-graph-2'),
                    )
    ]),

    dbc.Row([ # row 10
            dbc.Col(dcc.Graph(id='line-graph-3'),
                    )
    ]),
    
    dbc.Row([ # row 11
            dbc.Col(dcc.Graph(id='line-graph-4'),
                    )
    ])

])

# callback decorator
@app.callback(
    Output('line-graph-1', 'figure'),
    Output('bar-graph-1','figure'),
    Output('line-graph-2', 'figure'),
    Output('line-graph-3', 'figure'),
    Output('line-graph-4', 'figure'),
    Input("topic-selector", "value"),
    Input ("domain-selector", "value"),
    Input('date-range', 'start_date'),
    Input('date-range', 'end_date')
)
def update_output(selected_topic, selected_domain, start_date, end_date):
    #log
    print("topic",selected_topic,"domain",selected_domain,"start", start_date,"date", end_date)
    
    # filter dataframes based on updated data range
    mask_1 = ((df["Topic"] == selected_topic) & (df['date'] >= start_date) & (df['date'] <= end_date))
    df_filtered = df.loc[mask_1]

    #create line graphs based on filtered dataframes
    line_fig_1 = px.line(df_filtered, x="date", y="normalised results",
                     color='Veículos de notícias', title="O gráfico mostra a evolução temporal de sentimento dos títulos de notícias. Numa escala de -1 (negativo) a 1 (positivo), sendo 0 (neutro).")

    # Veículos de notícias
    #set x-axis title and y-axis title in line graphs
    line_fig_1.update_layout(
                   xaxis_title='Data',
                   yaxis_title='Classificação de Sentimento')

    #set label format on y-axis in line graphs
    line_fig_1.update_xaxes(tickformat="%b %d<br>%Y")

    # Bar Graph start
    grouped_df = df_filtered.groupby(['date', 'Veículos de notícias']).size().reset_index(name='occurrences')
    
    # Sort DataFrame by 'period' column
    grouped_df = grouped_df.sort_values(by='date')
    
    # Create a list of all unique media
    all_media = df_filtered['domain_folder_name'].unique()
    
    # Create a date range from Jan/2000 to the last month in the dataset
    date_range = pd.date_range(start=df_filtered['date'].min(), end=df_filtered['date'].max(), freq='MS')
    
    # Create a MultiIndex with all combinations of date_range and all_media
    idx = pd.MultiIndex.from_product([date_range, all_media], names=['date', 'Veículos de notícias'])
    
    # Reindex the DataFrame to include all periods and media
    grouped_df = grouped_df.set_index(['date', 'Veículos de notícias']).reindex(idx, fill_value=0).reset_index()

    bar_fig_1 = px.bar(grouped_df, x='date', y='occurrences', color='Veículos de notícias',
             labels={'date': 'Período', 'occurrences': 'Número de notícias', 'Veículos de notícias': 'Portal'},
             title='Número de notícias por período de tempo')
    # Bar Graph ends
                       
    # filter dataframes based on updated data range
    mask_2 = ((df_pos["Topic"] == selected_topic) & (df_pos["domain_folder_name"] == selected_domain) & (df_pos['date'] >= start_date) & (df_pos['date'] <= end_date))
    mask_3 = ((df_neu["Topic"] == selected_topic) & (df_neu["domain_folder_name"] == selected_domain) & (df_neu['date'] >= start_date) & (df_neu['date'] <= end_date))
    mask_4 = ((df_neg["Topic"] == selected_topic) & (df_neg["domain_folder_name"] == selected_domain) & (df_neg['date'] >= start_date) & (df_neg['date'] <= end_date))
    df2_filtered = df_pos.loc[mask_2]
    df3_filtered = df_neu.loc[mask_3]
    df4_filtered = df_neg.loc[mask_4]

#create line graphs based on filtered dataframes
    line_fig_2 = px.line(df2_filtered, x="date", y="rolling_mean_counts", line_group="FinBERT_label",
                     title="Positive")
    line_fig_3 = px.line(df3_filtered, x="date", y="rolling_mean_counts", line_group="FinBERT_label",
                     title="Neutral")
    line_fig_4 = px.line(df4_filtered, x="date", y="rolling_mean_counts", line_group="FinBERT_label",
                     title="Negative")

#set x-axis title and y-axis title in line graphs
    line_fig_2.update_layout(
                   xaxis_title='Data',
                   yaxis_title='Número de notícias com sentimento positivo')
    line_fig_3.update_layout(
                   xaxis_title='Data',
                   yaxis_title='Número de notícias com sentimento neutro')
    line_fig_4.update_layout(
                   xaxis_title='Data',
                   yaxis_title='Número de notícias com sentimento negativo')

#set label format on y-axis in line graphs
    line_fig_2.update_xaxes(tickformat="%b %d<br>%Y")
    line_fig_3.update_xaxes(tickformat="%b %d<br>%Y")
    line_fig_4.update_xaxes(tickformat="%b %d<br>%Y")

#set label format on y-axis in line graphs
    line_fig_2.update_traces(line_color='#1E88E5')
    line_fig_3.update_traces(line_color='#004D40')
    line_fig_4.update_traces(line_color='#D81B60')

    return line_fig_1, bar_fig_1, line_fig_2, line_fig_3, line_fig_4

    # return line_fig_1
    


# df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder_unfiltered.csv')



# app.layout = html.Div([
#     html.H1(children='Title of Dash App', style={'textAlign':'center'}),
#     dcc.Dropdown(df.country.unique(), 'Canada', id='dropdown-selection'),
#     dcc.Graph(id='graph-content')
# ])

# @callback(
#     Output('graph-content', 'figure'),
#     Input('dropdown-selection', 'value')
# )
# def update_graph(value):
#     dff = df[df.country==value]
#     return px.line(dff, x='year', y='pop')


if __name__ == '__main__':
    app.run_server(debug=True)