Spaces:
Running
Running
File size: 1,062 Bytes
52ae49b 8dbc465 50bed66 ac1e18e 52ae49b 50bed66 8dbc465 ac1e18e 8dbc465 50bed66 8dbc465 50bed66 8dbc465 ac1e18e 9cee292 ac1e18e 50bed66 8dbc465 50bed66 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 |
import streamlit as st
import sqlite3
import pandas as pd
import streamlit as st
import pygwalker as pyg
import streamlit.components.v1 as components
st.set_page_config(
page_title="Financial Data",
page_icon="📈",
layout="wide",
initial_sidebar_state="expanded",
)
st.title('Financial Data')
st.subheader('This is a BI tool to analyze news sentiment data')
conn = sqlite3.connect('fin_data.db')
c = conn.cursor()
c.execute("""
select * from company_news
""")
rows = c.fetchall()
# Extract column names from the cursor
column_names = [description[0] for description in c.description]
conn.commit()
conn.close()
# Create a DataFrame
df = pd.DataFrame(rows, columns=column_names)
# setup pygwalker configuration: https://github.com/Kanaries/pygwalker, https://docs.kanaries.net/pygwalker/use-pygwalker-with-streamlit.en
#pyg_html = pyg.to_html(df, dark="dark")
pyg_html = pyg.walk(df, dark = 'dark', return_html=True)
components.html(pyg_html, height=1000, scrolling=True)
# show the dataframe just to test
st.dataframe(df)
|