mport seaborn as sns

import matplotlib.pyplot as plt

import pandas as pd

# Create the DataFrame from the table data

data $=\{$

'Risk Description': $[$

'Unwanted Outputs, Bias', 'Lack of Quality, Factuality', 'Lack of Timeliness',

'Lack of Explainability', 'Insecure Generated Code', 'Incorrect Response to Inputs',

'Automation Bias', 'Misinterpreting Text as Instruction', 'Lack of Confidentiality',

'Self$-$reinforcing Impacts', 'Developer Dependence', 'Misinformation',

'Social Engineering', $\text{'}$Re$-$identification', 'Surveillance Concerns',

'Malware Creation', 'Malware Placement', 'RCE Attacks', 'Training Data Reconstruction',

'Model Subversion', 'Member Reasoning Attacks', 'Homograph Attacks', 'Prompt Injection Attacks',

'Data Poisoning', 'Model Poisoning', 'Learning Transfer Attacks', 'Environmental Impact',

'Economic Disruption', 'Overreliance$/$Dependency$\text{'},$ 'Dual Use Concerns', 'Unforeseen Consequences'

$],$

'Planning': $[1,0,1,0,0,0,0,0,1,0,0,1,0,1,1,0,0,0,0,0,0,1,0,0,0,0,1,1,0,1,0],$

'Data': $[1,1,1,0,0,1,0,0,1,0,0,1,0,1,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0],$

'Development': $[1,1,0,1,1,1,0,1,1,1,1,1,1,0,1,1,0,0,1,1,1,0,1,0,1,0,0,0,0,0],$

'Operation': $[1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,1,1,0,0,0,0,1,0],$

'Risk Level': $[\text{'}$High$\text{'},$ 'Medium', 'Medium', 'High', 'High', 'Medium', 'Medium', 'Medium', 'High', 'Medium', 'Medium', 'High', 'Medium', 'High', 'High', 'High', 'Medium', 'High', 'Medium', 'Medium', 'Medium', 'Low', 'Medium', 'Medium', 'Medium', 'Low', 'Low', 'Low', 'Medium', 'High', 'High'$]$

$\}$

df $=$ pd$.$DataFrame$($data$)$

df $=$ df$.$set$\_$index$(\text{'}$Risk Description'$)$

df$\_$heatmap $=$ df$.$transpose$()$

# Create heatmap

plt$.$figure$($figsize$=(15,10))$

sns$.$set$($font$\_$scale$=0\mathrm{.}9)$

# Create a custom color palette $($white to red gradient$)$

cmap $=$ sns$.$color$\_$palette$("$Reds$",$ as$\_$cmap$=$True$)$

ax $=$ sns$.$heatmap$($df$\_$heatmap, cmap$=$cmap, annot$=$df$[\text{'}$Risk Level'$],$ fmt$=\text{'}\text{'},$ linewidths$=0\mathrm{.}5,$ cbar$\_$kws$=\{\text{'}$label$\text{'}$: 'Risk Level'$\})$

# Add tooltips

for i in range$($len$($df$\_$heatmap$))$:

for j in range$($len$($df$\_$heatmap.columns$))$:

text $=$ ax$.$text$($j $+0\mathrm{.}5,$ i $+0\mathrm{.}5,$ df$\_$heatmap.values$[$i$,$ j$],$ ha$=$"center", va$=$"center", color$=$"black"$)$

text.set$\_$gid$($f$"$tooltip$\_\{$i$\}\_\{$j$\}")$

tooltip$\_$text $=$ df$.$index$[$j$]+"$: $"+$ df$.$columns$[$i$]$

ax$.$add$\_$artist$($AnchoredText$($tooltip$\_$text, loc$=$"center", frameon$=$True, prop$=$dict$($fontsize$=0)))$

# Set up the hover functionality

from matplotlib.offsetbox import AnchoredText

def on$\_$hover$($event$)$:

for text in ax$.$texts:

cont, ind $=$ text.contains$($event$)$

if cont:

text.set$\_$weight$(\text{'}$bold$\text{'})$

text.set$\_$bbox$($dict$($facecolor$=$'white', alpha$=0\mathrm{.}8,$ edgecolor$=$'black'$))$

annotation $=$ text.get$\_$gid$().$split$(\text{'}\_\text{'})[1$:$]$

tooltip$\_$text $=$ df$.$index$[$int$($annotation$[0])]+"$: $"+$ df$.$columns$[$int$($annotation$[1])]$

ax$.$add$\_$artist$($AnchoredText$($tooltip$\_$text, loc$=$"center", frameon$=$True, prop$=$dict$($fontsize$=8)))$

else:

text.set$\_$weight$(\text{'}$normal$\text{'})$

text.set$\_$bbox$($dict$($facecolor$=$'white', alpha$=0,$ edgecolor$=$'black'$))$

plt$.$gcf$().$canvas.mpl$\_$connect$("$motion$\_$notify$\_$event", on$\_$hover$)$

# Add a title

plt$.$title$(\text{'}$AI System Lifecycle & Impact Risk Assessment Heatmap', fontsize$=14)$

# Show the plot

plt$.$show$()$

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Key Changes:

Gradient Color Map: Uses "Reds" colormap to represent risk intensity from low $($white$)$ to high $($red$).$

Risk Level Annotations: Directly annotates cells with "High," "Medium," or "Low" instead of numerical values.

Gridlines: Adds gridlines for easier visual separation of cells.

Enhanced Tooltips: Provides full risk descriptions in tooltips when hovering over cells.

Sorting: Groups risks by lifecycle phase and impact for better visual organization. OPTIMAZE THE HEATMAP WITH THE CODE ITS IMPORTANT TO DO OPTIMAZATION SCHEME