Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import plotly.express as px
+
+# Load the data
+def load_data():
+    file_path = 'digital_identity_data.xlsx'
+    return pd.read_excel(file_path)
+
+data = load_data()
+
+# Streamlit app layout
+st.title("Digital Identity Dashboard")
+
+# Sidebar filters
+st.sidebar.header("Filter Options")
+country_filter = st.sidebar.multiselect("Select Countries:", options=data["Country"].unique(), default=data["Country"].unique())
+gender_filter = st.sidebar.multiselect("Select Genders:", options=data["Gender"].unique(), default=data["Gender"].unique())
+account_status_filter = st.sidebar.multiselect("Select Account Status:", options=data["Account Status"].unique(), default=data["Account Status"].unique())
+
+# Apply filters
+filtered_data = data[
+    (data["Country"].isin(country_filter)) &
+    (data["Gender"].isin(gender_filter)) &
+    (data["Account Status"].isin(account_status_filter))
+]
+
+# Display filtered data
+st.subheader("Filtered Data")
+st.dataframe(filtered_data)
+
+# Visualization: Number of logins by country
+st.subheader("Number of Logins by Country")
+logins_by_country = filtered_data.groupby("Country")["Number of Logins"].sum().reset_index()
+fig = px.bar(logins_by_country, x="Country", y="Number of Logins", title="Logins by Country", color="Country")
+st.plotly_chart(fig)
+
+# Visualization: Session duration by gender
+st.subheader("Average Session Duration by Gender")
+session_duration_by_gender = filtered_data.groupby("Gender")["Session Duration (Minutes)"].mean().reset_index()
+fig2 = px.bar(session_duration_by_gender, x="Gender", y="Session Duration (Minutes)", title="Session Duration by Gender", color="Gender")
+st.plotly_chart(fig2)
+
+# Visualization: Data breaches reported
+st.subheader("Data Breaches Reported")
+fig3 = px.pie(filtered_data, names="Country", values="Data Breaches Reported", title="Data Breaches by Country")
+st.plotly_chart(fig3)
+
+# Visualization: 2FA usage
+st.subheader("2FA Adoption")
+two_fa_usage = filtered_data["2FA Enabled"].value_counts().reset_index()
+two_fa_usage.columns = ["2FA Enabled", "Count"]
+fig4 = px.pie(two_fa_usage, names="2FA Enabled", values="Count", title="2FA Usage")
+st.plotly_chart(fig4)
+
+# Footer
+st.markdown("---")
+st.caption("Digital Identity Dashboard - Built with Streamlit")