Create index.py

index.py

@@ -0,0 +1,135 @@
+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
+
+import dash
+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['FinBERT_label'] = df['FinBERT_label'].astype(str)
+df['date'] = pd.to_datetime(df['date'])
+df = df.rename(columns={df.columns[4]: "Veículos de notícias"})
+
+unique_topics = df['Topic'].unique()
+print(unique_topics)
+
+
+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'),
+                )
+]),
+
+
+])
+
+# callback decorator
+@app.callback(
+    Output('line-graph-1', 'figure'),
+    Input("topic-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")
+
+    return line_fig_1
+    
+
+# df['FinBERT_label'].replace({
+#     '3.0': 'positive',
+#     '2.0': 'neutral',
+#     '1.0': 'negative'
+# }, inplace=True)
+
+# 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)