import streamlit as st
import pandas as pd
from io import StringIO

st/markdown("""   

![image/png](https://cdn-uploads.huggingface.co/production/uploads/620630b603825909dcbeba35/AswjRkg3QMR503fAScTrV.png)

   """)


# Sample data for the inline CSV
data = """
Section,Title,White_Team_Libraries,Red_Team_Libraries
1.1,"Responsible and Trustworthy Artificial Intelligence","ai-fairness-360, tensorflow, pytorch","adversarial-robustness-toolbox, tensorflow, pytorch"
1.2,"Who is This For?","numpy, pandas","numpy, scipy"
1.3,"Why a Checklist?","pandas, matplotlib","scikit-learn, seaborn"
1.4,"Not Comprehensive","streamlit, dash","flask, django"
1.5,"Large Language Model Challenges","transformers, datasets","keras, tensorflow"
1.6,"LLM Threat Categories","threatmodeler, pytm","mitmproxy, burpsuite"
1.7,"Artificial Intelligence Security and Privacy Training","owasp, safecode","hackthebox, tryhackme"
1.8,"Incorporate LLM Security and governance with Existing, Established Practices and Controls","compliance-checker, policy-sentry","sqlmap, nmap"
1.9,"Fundamental Security Principles","secure-code-toolkit, bandit","metasploit, pwntools"
1.10,"Risk","risk-frameworks, gordon","exploit-db, cve-search"
1.11,"Vulnerability and Mitigation Taxonomy","vuln-code-db, flawfinder","yara, volatility"
2,"Determining LLM Strategy","strategic-planning, mindmaps","red-team-toolkit, blue-team-toolkit"
2.1,"Deployment Strategy","docker, kubernetes","ansible, puppet"
3.1,"Adversarial Risk","cleverhans, foolbox","gym, reinforcement-learning"
3.2,"Threat Modeling","pytm, sea-sponge","attackflow, evilginx"
3.3,"AI Asset Inventory","asset-inventory, snipe-it","openvas, qualys"
3.4,"AI Security and Privacy Training","security-training, cybrary","offensive-security, sans"
3.5,"Establish Business Cases","business-model-canvas, leanstack","swot-analysis, pestle-analysis"
3.6,"Governance","corporate-governance, iso-standards","cobit, itil"
3.7,"Legal","legal-docs, law-libraries","case-law-search, legal-hack"
3.8,"Regulatory","regulatory-compliance, gdpr","fcc-tools, eu-data-protection"
3.9,"Using or Implementing Large Language Model Solutions","model-deployment, ml-ops","shadow-it, devops-tools"
3.10,"Testing, Evaluation, Verification, and Validation (TEVV)","test-automation, quality-assurance","bug-bounty, fuzzing"
3.11,"Model Cards and Risk Cards","model-card-toolkit, aboutml","threat-modeling, risk-analysis"
3.12,"RAG: Large Language Model Optimization","rag-tokenizer, huggingface-transformers","genetic-algorithms, particle-swarm"
3.13,"AI Red Teaming","red-team-framework, adversary-simulation","kali-linux, pen-test-tools"
"""

# Creating a DataFrame
df = pd.read_csv(StringIO(data))

# Streamlit app
st.title('AI System Defense Strategy')

# Display DataFrame in the app
st.write(df)

# Instructions or additional app features can go here
st.markdown("""
This table includes sections relevant to defending AI systems, along with recommended Python libraries for both white team (defense) and red team (offensive security) developer leads. Use the information to guide the development and testing of responsible and secure AI systems.
""")