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'])

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)

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

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
        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('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')
)
# callback function
def update_output(selected_topic, start_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).")

    #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")

    # 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, 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)