New app.py

app.py

@@ -0,0 +1,68 @@
+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("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)