import streamlit as st

import pandas as pd

import plotly.express as px

import plotly.graph_objects as go

from plotly.subplots import make_subplots

# Load the dataset

data = pd.read_csv('ItvaccinationMergedMap.csv')

# Replace NaN values in the 'gdp_tot' column with 0

data['gdp_tot'].fillna(0, inplace=True)

# Set the page width to a wider layout

st.set_page_config(layout="wide")

# Title of the dashboard

st.title('Italy Vaccination Dashboard')

# Sidebar for navigation

page = st.sidebar.selectbox("Select a Page", ["Dashboard", "Age Range Investigation", "GDP and Population", "Regions in Details","Overtime Investigation"])

if page == "Dashboard":

# Line chart for dailytotal over time

daily_chart = px.line(data, x='administration_date', y='dailytotal', title='Daily Vaccination Over Time')

daily_chart.update_layout(height=400) # Set the height to your desired value

st.plotly_chart(daily_chart, use_container_width=True) # Use container width for responsive plot size

# Create a layout with two columns (col1 and col2)

col1, col2 = st.columns(2)

# Bar plot for Total Number of Doses by Dose Type (placed in the first column)

with col1:

# st.subheader('Total Number of Doses by Dose Type')

dose_sums = data[['first_dose', 'second_dose', 'previous_infection', 'additional_booster_dose', 'second_booster', 'db3']].sum()

df_plot = pd.DataFrame({'Dose Type': dose_sums.index, 'Count': dose_sums.values})

fig_doses = px.bar(df_plot, x='Dose Type', y='Count', title= 'Total Number of Doses by Dose Type',

labels={'Count': 'Total Number of Doses'},

)

st.plotly_chart(fig_doses, use_container_width=True)

# Pie chart for suppliers (placed in the second column)

with col2:

# st.subheader('Vaccination Suppliers Distribution')

suppliers_pie = px.pie(data,title = 'Vaccination Suppliers Distribution', names='supplier')

st.plotly_chart(suppliers_pie, use_container_width=True) # Use container width for responsive plot size

# Assuming you have loaded your data into a DataFrame called data

# Assuming you have a DataFrame 'data' with columns like 'region_name', 'dailytotal', 'males', 'females', etc.

# data = ...

# Group by region and calculate the sum of dailytotal, males, and females for each region

region_data = data.groupby('region_name').agg({'dailytotal': 'sum', 'males': 'sum', 'females': 'sum', 'gdp_tot': 'first' , 'pop_resid' : 'first' }).reset_index()

# Sort the DataFrame by dailytotal in descending order

region_data = region_data.sort_values(by='dailytotal', ascending=False)

# Allow user to adjust the number of displayed rows

num_rows = st.slider("Number of Regions to Display:", min_value=1, max_value=len(region_data), value=5)

# Highlight the top N regions (adjusted by user)

top_regions = region_data.head(num_rows)

# Display the highlighted table

st.table(top_regions.style.highlight_max(axis=0, subset=['dailytotal'], color='black'))

elif page == "Age Range Investigation":

# New bar plot for Distribution of Age Range by Region (placed below col1 and col2)

#st.subheader('Distribution of Age Range by Region')

region_age_distribution = data.groupby(['region_name', 'age_range'])['dailytotal'].sum().reset_index()

fig_age_distribution = px.bar(region_age_distribution, title='Distribution of Age Range by Region', x='region_name', y='dailytotal', color='age_range',

labels={'dailytotal': 'Count', 'region_name': 'Region', 'age_range': 'Age Range'},

width=1000, height=700)

st.plotly_chart(fig_age_distribution, use_container_width=True) # Use container width for responsive plot size

# Sum the doses for each age range

df_summed = data.groupby('age_range').agg({

'first_dose': 'sum',

'second_dose': 'sum',

'previous_infection': 'sum',

'additional_booster_dose': 'sum',

'second_booster': 'sum',

'db3': 'sum'

}).reset_index()

# Melt the DataFrame to create a long-format for Plotly Express

df_melted = pd.melt(df_summed, id_vars=['age_range'], value_vars=['first_dose', 'second_dose', 'previous_infection', 'additional_booster_dose', 'second_booster', 'db3'],

var_name='Dose Type', value_name='Count')

# Create a bar plot

fig_age_investigation = px.bar(df_melted, x='Dose Type', y='Count', color='age_range',

labels={'Count': 'Count', 'age_range': 'Age Range', 'Dose Type': 'Dose Type'},

title='Distribution of Doses by Age Range',

width=1000, height=600)

# Show the plot

st.plotly_chart(fig_age_investigation, use_container_width=True) # Use container width for responsive plot size

# Assuming you have a DataFrame 'df' with columns 'age_range', 'supplier', and 'dailytotal'

# df = ...

# Group by 'age_range', 'supplier', and sum 'dailytotal'

df_summed = data.groupby(['age_range', 'supplier']).agg({'dailytotal': 'sum'}).reset_index()

# Create a bar plot

fig = px.bar(df_summed, x='supplier', y='dailytotal', color='age_range',

labels={'dailytotal': 'Total Count', 'age_range': 'Age Range', 'supplier': 'Supplier'},

title='Distribution of Counts by Age Range and Supplier',

width=1000, height=600)

# Show the plot using st.plotly_chart

st.plotly_chart(fig, use_container_width=True)

elif page == "GDP and Population":

# st.subheader('Comparison of Daily Total with Population Residual for Each Region')

region_stats_daily_total = data.groupby('region_name').agg({'dailytotal': 'sum', 'pop_resid': 'first'}).reset_index()

scatter_plot_daily_total = px.scatter(region_stats_daily_total, title='Comparison of Daily Total with Population Residual for Each Region', x='dailytotal', y='pop_resid', color='region_name',

labels={'dailytotal': 'Daily Total', 'pop_resid': 'Population Residual'},

width=800, height=400)

st.plotly_chart(scatter_plot_daily_total, use_container_width=True) # Use container width for responsive plot size

# Scatter plot for Comparison of dailytotal with GDP and Population for Each Region (placed in the fourth column)

# st.subheader('Comparison of dailytotal with GDP and Population for Each Region')

region_stats_previous_infection = data.groupby('region_name').agg({'dailytotal': 'sum', 'gdp_tot': 'first', 'pop_resid': 'first'}).reset_index()

region_stats_previous_infection['gdp_tot'] *= 1000000

region_stats_previous_infection['pop_resid'].fillna(0, inplace=True)

scatter_plot_previous_infection = px.scatter(region_stats_previous_infection,title='Comparison of dailytotal with GDP and Population for Each Region',x='dailytotal', y='gdp_tot', size='pop_resid', color='region_name',

labels={'dailytotal': 'Sum of dailytotal', 'gdp_tot': 'GDP Total', 'pop_resid': 'Population'},

width=800, height=400)

st.plotly_chart(scatter_plot_previous_infection, use_container_width=True) # Use container width for responsive plot size

# Scatter plot for GDP, Population Residual, and Daily Total for Each Region (Bubble Plot)

# st.subheader('GDP, Population Residual, and Daily Total for Each Region (Bubble Plot)')

region_stats_bubble = data.groupby('region_name').agg({

'pop_resid': 'first',

'gdp_tot': 'first',

'dailytotal': 'sum'

}).reset_index()

# Create a bubble plot using Plotly Express

fig_bubble = px.scatter(region_stats_bubble, x='gdp_tot', y='pop_resid', size='dailytotal', color='region_name',

labels={'gdp_tot': 'GDP Total (Billion Euros)', 'pop_resid': 'Population Residual', 'dailytotal': 'Total Daily Vaccinations'},

title='GDP, Population Residual, and Daily Total for Each Region ',

size_max=30) # Adjust size_max as needed

# Show the bubble plot

st.plotly_chart(fig_bubble, use_container_width=True)

# Population Residual, GDP, and Daily Total for Each Region (3D Scatter Plot)

# st.subheader('Population Residual, GDP, and Daily Total for Each Region (3D Scatter Plot)')

region_stats_3d = data.groupby('region_name').agg({

'pop_resid': 'first',

'gdp_tot': 'first',

'dailytotal': 'sum'

}).reset_index()

region_stats_3d = region_stats_3d.dropna(subset=['pop_resid', 'gdp_tot']) # Remove rows with NaN values

# Create a 3D scatter plot

fig_3d_scatter = px.scatter_3d(region_stats_3d, x='pop_resid', y='gdp_tot', z='dailytotal',

color='region_name', size='dailytotal',

labels={'pop_resid': 'Population Residual', 'gdp_tot': 'GDP Total (Billion Euros)', 'dailytotal': 'Total Daily Vaccinations'},

title='Population Residual, GDP, and Daily Total for Each Region (3D Scatter Plot)',

size_max=30) # Adjust size_max as needed

st.plotly_chart(fig_3d_scatter, use_container_width=True)

# ... (previous code)

elif page == "Regions in Details":

# Population Residual for Each Region (Horizontal Bar Plot)

# st.subheader('Population Residual for Each Region')

unique_pop_resid = data.groupby('region_name').agg({'pop_resid': 'first', 'dailytotal': 'sum'}).reset_index()

unique_pop_resid = unique_pop_resid[unique_pop_resid['pop_resid'].notnull()] # Remove empty columns

# Create a horizontal bar plot

fig_population_residual_bar = px.bar(unique_pop_resid, x='pop_resid', y='region_name',

labels={'pop_resid': 'Population Residual', 'dailytotal': 'Total Daily Vaccinations'},

title='Population Residual for Each Region (Horizontal Bar Plot)',

orientation='h')

# Update x-axis title

fig_population_residual_bar.update_layout(xaxis_title='Population Residual')

st.plotly_chart(fig_population_residual_bar, use_container_width=True)

# Stacked bar plot for Distribution of Vaccination Doses by Region

# st.subheader('Distribution of Vaccination Doses by Region')

df_summed = data.groupby('region_name').agg({

'first_dose': 'sum',

'second_dose': 'sum',

'previous_infection': 'sum',

'additional_booster_dose': 'sum',

'second_booster': 'sum',

'db3': 'sum'

}).reset_index()

df_melted = pd.melt(df_summed, id_vars=['region_name'], value_vars=['first_dose', 'second_dose', 'previous_infection', 'additional_booster_dose', 'second_booster', 'db3'],

var_name='Dose Type', value_name='Count')

fig_vaccination_doses = px.bar(df_melted, x='Count', y='region_name', color='Dose Type',

labels={'Count': 'Count', 'Dose Type': 'Dose Type'},

title='Distribution of Vaccination Doses by Region',

orientation='h', barmode='stack')

st.plotly_chart(fig_vaccination_doses, use_container_width=True)

# Bar plot for Distribution of Suppliers by Region (Weighted by dailytotal)

# st.subheader('Distribution of Suppliers by Region (Weighted by dailytotal)')

supplier_region_weighted_counts = data.groupby(['region_name', 'supplier'])['dailytotal'].sum().reset_index(name='Weighted_Count')

fig_supplier_distribution = px.bar(supplier_region_weighted_counts, x='Weighted_Count', y='region_name', color='supplier',

labels={'Weighted_Count': 'Weighted Occurrences'},

title='Distribution of Suppliers by Region (Weighted by dailytotal)',

orientation='h')

st.plotly_chart(fig_supplier_distribution, use_container_width=True)

# GDP for Each Region (Bar Plot)

st.subheader('GDP for Each Region (Bar Plot)')

# Assuming you have a DataFrame 'data' with columns like 'region_name', 'gdp_tot', etc.

# data = ...

# Group by region and get the first GDP value for each region

region_gdp = data.groupby('region_name')['gdp_tot'].first().reset_index()

# Drop rows with NaN values in the 'gdp_tot' column

region_gdp = region_gdp.dropna(subset=['gdp_tot'])

# List of regions to exclude

regions_to_exclude = ['Valle d\'Aosta / Vallée d\'Aoste', 'Provincia Autonoma Trento', 'Provincia Autonoma Bolzano / Bozen', 'Friuli-Venezia Giulia']

# Drop specified regions

region_gdp = region_gdp[~region_gdp['region_name'].isin(regions_to_exclude)]

# Drop regions with no valid GDP values

region_gdp = region_gdp[region_gdp['gdp_tot'].notna()]

# Check if there are any valid rows left after dropping NaN values and regions with no GDP

if not region_gdp.empty:

# Create a bar plot

fig_gdp_bar = px.bar(region_gdp, x='region_name', y='gdp_tot',

labels={'gdp_tot': 'GDP Total'},

title='GDP for Each Region (Bar Plot)')

# Update layout for better presentation

fig_gdp_bar.update_layout(xaxis_title='Region', yaxis_title='GDP Total (Billion Euros)')

# Show the bar plot

st.plotly_chart(fig_gdp_bar, use_container_width=True)

else:

st.write("No valid data available for GDP. Please check your dataset.")

elif page == "Overtime Investigation":

# Line plot for Daily Vaccination Counts Over Time by Supplier

# st.subheader('Daily Vaccination Counts Over Time by Supplier')

# Create an interactive line plot using Plotly Express

fig_supplier_overtime = px.line(data, x='administration_date', y='dailytotal', color='supplier',

labels={'dailytotal': 'Daily Vaccination Count'},

title='Vaccination Counts Over Time by Supplier')

# Add a range slider for date selection

fig_supplier_overtime.update_layout(xaxis_rangeslider_visible=True)

# Show the plot

st.plotly_chart(fig_supplier_overtime, use_container_width=True)

# Subplot for Number of Doses Administered Over Time

# st.subheader('Number of Doses Administered Over Time')

DosesOverTime_df = data[['administration_date', 'first_dose', 'second_dose', 'previous_infection', 'additional_booster_dose', 'second_booster', 'db3']].copy()

DosesOverTime_df.set_index('administration_date', inplace=True)

# Define a custom color palette with bold colors

custom_palette = ["#3498db", "#2ecc71", "#e74c3c", "#f39c12", "#9b59b6", "#34495e"]

fig_doses_overtime = make_subplots(rows=1, cols=1)

# Iterate over the columns and add traces

for column in DosesOverTime_df.columns:

fig_doses_overtime.add_trace(go.Scatter(x=DosesOverTime_df.index, y=DosesOverTime_df[column], mode='lines', name=column, line=dict(width=2.5)))

# Add a range slider for date selection

fig_doses_overtime.update_layout(xaxis_rangeslider_visible=True)

# Update layout for better presentation

fig_doses_overtime.update_layout(title_text='Number of Doses Administered Over Time',

xaxis_title='Administration Date',

yaxis_title='Cumulative Count',

legend_title='Dose Type')

# Show the plot

st.plotly_chart(fig_doses_overtime, use_container_width=True)