app.py

# INSTRUCTIONS:
# 1. Open a "Terminal" by: View --> Terminal OR just the "Terminal" through the hamburger menu
# 2. run in terminal with: streamlit run app.py
# 3. click the "Open in Browser" link that pops up OR click on "Ports" and copy the URL
# 4. Open a Simple Browswer with View --> Command Palette --> Simple Browser: Show
# 5. use the URL from prior steps as intput into this simple browser


import streamlit as st
import altair as alt
import pandas as pd
from vega_datasets import data
 
st.title('Vizualization for Experts')

st.subheader('This page will contain the entire dashboard.')

st.text("*Dashboard Introduction*")


st.text("*Brief description of Narrative*")


st.text("*World map viz here*")


st.text("*Interactive viz here*")


'''
####### Loading Data #######

# 1. Contracts data
data = pd.read_csv("/Users/sharanya/Documents/SEMESTERS/7- FALL 2024/IS445/FinalProject/IS445_VizForExperts/contract_awards_in_investment_project_financing.csv")
# 2. World Map dataset
world_map = alt.topo_feature(data.world_110m.url, feature='countries')

####### Loading Data #######
from iso3166 import countries_by_name
'''

import pandas as pd
import altair as alt
import ipywidgets as widgets
from IPython.display import display, clear_output

alt.data_transformers.disable_max_rows()

url = "contract_awards_in_investment_project_financing_22-11-2024.csv"
df = pd.read_csv(url)

numeric_columns = df.select_dtypes(include=['float64', 'int64']).columns

col1_dropdown = widgets.Dropdown(
    options=numeric_columns,
    value=numeric_columns[0],
    description="Column 1:"
)

col2_dropdown = widgets.Dropdown(
    options=numeric_columns,
    value=numeric_columns[1],
    description="Column 2:"
)

output = widgets.Output()

def update_correlation(change):
    with output:
        clear_output(wait=True)
        
        col1 = col1_dropdown.value
        col2 = col2_dropdown.value
        
        correlation_value = df[col1].corr(df[col2])
        print(f"Correlation between {col1} and {col2}: {correlation_value:.2f}")
        
        # Create a scatter plot
        correlation_plot = (
            alt.Chart(df)
            .mark_circle(size=60)
            .encode(
                x=alt.X(f"{col1}:Q", title=col1),
                y=alt.Y(f"{col2}:Q", title=col2),
                color="Region:N",
                tooltip=["Project Name", "Supplier", col1, col2],
            )
            .properties(
                title=f"Correlation Plot: {col1} vs. {col2}",
                width=600,
                height=400
            )
        )
        display(correlation_plot)

col1_dropdown.observe(update_correlation, names='value')
col2_dropdown.observe(update_correlation, names='value')

print("Select columns for correlation analysis:")
display(col1_dropdown, col2_dropdown, output)