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| import os | |
| import subprocess | |
| import sys | |
| # Install plotly if not already installed | |
| try: | |
| import plotly | |
| except ImportError: | |
| subprocess.check_call([sys.executable, "-m", "pip", "install", "plotly"]) | |
| # import subprocess | |
| # import sys | |
| import pandas as pd | |
| import streamlit as st | |
| import plotly.express as px | |
| # Load Dataset | |
| # Example: 'country' column for country names, other columns for years | |
| data = pd.read_csv("https://huggingface.co/spaces/jiyachachan/fp2/resolve/main/child_mortality_0_5_year_olds_dying_per_1000_born.csv") | |
| # Melt the data to long format for easier filtering | |
| data_melted = data.melt(id_vars=["country"], var_name="year", value_name="mortality_rate") | |
| data_melted["year"] = pd.to_numeric(data_melted["year"]) | |
| # Streamlit App | |
| st.title("Global Child Mortality Rate (per 1000 children born)") | |
| st.write("""This interactive visualization provides an insightful overview of child mortality rates (number of deaths per 1,000 live births) across countries for a selected year. | |
| The data highlights disparities in healthcare, socioeconomic conditions, and development across the globe, making it a valuable tool for understanding global health challenges.""") | |
| # Add year selection | |
| # years = sorted(data_melted["year"].unique()) # Extract unique years from the dataset | |
| # selected_year = st.selectbox("Select Year", years) | |
| # Add year selection with a slider | |
| min_year = int(data_melted["year"].min()) | |
| max_year = int(data_melted["year"].max()) | |
| st.subheader("Child Mortality Trends around the Globe") | |
| st.write(""" | |
| This Chart reveals an important trend of how the child mortality rate have been changing across the years. | |
| This gives us a very important insight on how the present developed countries have successfully reduced the rate, and underdeveloped countries still faces challenges to curb child mortality successfully. | |
| We can utilise the trends in the graph to understand the factors which might be the responsible for high mortality or low mortality. | |
| This will help the policymakers in developing/under-developed countries to develop data-driven policy to reduce child mortality. | |
| """) | |
| selected_year = st.slider("Select Year", min_value=min_year, max_value=max_year, value = 2024, step = 5) | |
| # Filter data for the selected year | |
| filtered_data = data_melted[data_melted["year"] == selected_year] | |
| # Create the map | |
| fig = px.choropleth( | |
| filtered_data, | |
| locations="country", # Country names or ISO 3166-1 Alpha-3 codes | |
| locationmode="country names", # Use 'ISO-3' if you have country codes | |
| color="mortality_rate", | |
| title=f"Child Mortality Rate in {selected_year}", | |
| color_continuous_scale=px.colors.sequential.OrRd, # Customize the color scale | |
| ) | |
| # Display the map | |
| st.plotly_chart(fig) | |