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feat: enhance scripts #6

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Merged
jajera merged 1 commit into from
Nov 27, 2025
Merged

feat: enhance scripts #6

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78 changes: 64 additions & 14 deletions dashboard/dashboard.py
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Original file line number Diff line number Diff line change
Expand Up @@ -524,33 +524,83 @@ def main():
if fig:
st.plotly_chart(fig, width='stretch')

# Top client IPs
# Top 10 Client IPs
if analytics['slowness'].get('top_request_ips'):
st.subheader("Top Client IPs by Request Volume")
st.subheader("Top 10 Client IPs by Request Volume")
st.markdown("**Identifying clients generating the most traffic can help detect bots, crawlers, or potential abuse.**")


top_ips = analytics['slowness']['top_request_ips']
# Get top 10
top_10_ips = dict(sorted(top_ips.items(), key=lambda x: x[1], reverse=True)[:10])

col1, col2 = st.columns([2, 1])

with col1:
top_ips = analytics['slowness']['top_request_ips']
fig = create_bar_chart(
top_ips,
"Top Client IPs",
top_10_ips,
"Top 10 Client IPs",
"IP Address",
"Request Count",
limit=15
limit=10
)
if fig:
st.plotly_chart(fig, width='stretch')

with col2:
# Table with top IPs
df_ips = pd.DataFrame({
'IP Address': list(top_ips.keys())[:20],
'Requests': list(top_ips.values())[:20]
})
# Table with top 10 IPs - include user agent if available
if analytics['slowness'].get('top_request_ips_with_ua'):
ip_ua_data = analytics['slowness']['top_request_ips_with_ua']
df_ips = pd.DataFrame([
{
'IP Address': ip,
'Requests': data['request_count'],
'Top User Agent': data['top_user_agent'][:80] if len(data['top_user_agent']) > 80 else data['top_user_agent'] # Truncate long UAs
}
for ip, data in ip_ua_data.items()
])
else:
# Fallback if user agent data not available
df_ips = pd.DataFrame({
'IP Address': list(top_10_ips.keys()),
'Requests': list(top_10_ips.values())
})
st.dataframe(df_ips, width='stretch', hide_index=True)

# Top 10 IPs by Request Rate (requests per minute)
if analytics['slowness'].get('top_ips_by_request_rate'):
st.subheader("Top 10 Client IPs by Request Rate (Requests per Minute)")
st.markdown("**IPs with high request rates may indicate automated traffic, bots, or potential abuse.**")

ip_rates = analytics['slowness']['top_ips_by_request_rate']

col1, col2 = st.columns([2, 1])

with col1:
# Create chart with requests per minute
rate_data = {ip: data['requests_per_minute'] for ip, data in ip_rates.items()}
fig = create_bar_chart(
rate_data,
"Top 10 IPs by Request Rate",
"IP Address",
"Requests per Minute",
limit=10
)
if fig:
st.plotly_chart(fig, width='stretch')

with col2:
# Table with detailed metrics
df_rates = pd.DataFrame([
{
'IP Address': ip,
'Req/min': f"{data['requests_per_minute']:.2f}",
'Total Requests': int(data['total_requests']),
'Time Span (min)': f"{data['time_span_minutes']:.1f}"
}
for ip, data in ip_rates.items()
])
st.dataframe(df_rates, width='stretch', hide_index=True)


if __name__ == "__main__":
main()
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