AI Agent for DAO Governance

• This project implements an AI-powered agent for analyzing and voting on DAO governance proposals.
• It evaluates proposals based on treasury impact, community alignment, technical feasibility, and risk assessment, then casts votes (FOR, AGAINST, or ABSTAIN) in a mock environment.
• The agent generates visualizations of the analysis results using Matplotlib, displaying three charts: Proposal Analysis Scores, Overall Scores with Threshold, and Voting Summary.

Features

• Proposal Analysis: Evaluates proposals using weighted metrics (35% treasury, 30% community, 20% technical, 15% risk).
• Automated Voting: Casts votes based on a minimum score threshold (0.65).
• Visualization: Generates three charts using Matplotlib:
• Bar chart of individual scores per proposal.
• Bar chart of overall scores with a threshold line.
• Pie chart of voting distribution (e.g., 1 FOR, 0 AGAINST, 4 ABSTAIN).
• Mock Data Mode: Uses test_proposals.json for testing without blockchain interaction.
• Configurable: Metrics and settings defined in main.py and deploy_info.json.

Prerequisites

• Python 3.6+: Ensure Python is installed (python --version).
• Dependencies:
• web3.py: For blockchain interaction (mock mode doesn’t require a live blockchain).
• eth-account: For account management.
• matplotlib: For chart visualization.
• numpy: For numerical operations in charts.
• Operating System: Tested on Windows or Mac.
• Ganache (Optional): For future blockchain integration.

Installation

1. Clone or Set Up the Project:

• Place all project files in a directory (e.g., C:\Users\name\files\Program\title of the project).

2. Install Dependencies:

• pip install web3 eth-account matplotlib numpy

3. Verify Files: Ensure the following files are present:

• main.py: Main script for the AI agent.
• deployment_testing.py: Script to generate mock proposal data.
• deploy_info.json: Configuration file with mock deployment details (e.g., AI agent address, private key).

Usage

1. Generate Mock Proposals:

• Run the deployment testing script to create test_proposals.json:
• python deployment_testing.py
• This generates 5 mock proposals for testing.

2. Run the AI Agent:

• Execute the main script:
• python main.py
• The script will:
• Load 5 proposals from test_proposals.json.
• Analyze each proposal and print scores (e.g., Proposal #0: Treasury=0.30, Community=0.50, Technical=0.70, Risk=0.60, Overall=0.48).
• Cast votes (e.g., 1 FOR, 0 AGAINST, 4 ABSTAIN).
• Display three Matplotlib chart windows:
• Proposal Analysis Scores: Bar chart of scores per category.
• Overall Scores with Threshold: Bar chart with a 0.65 threshold line.
• Voting Summary: Pie chart of vote distribution.

3. Expected Output:

• Console output showing analysis and voting results.
• Three pop-up chart windows visualizing the results.
• Example console output:

✓: • Loaded 5 proposals from test_proposals.json
...
📊 VOTING SUMMARY
Total Votes: 5
FOR: 1
AGAINST: 0
ABSTAIN: 4

Configuration

• Metrics: Defined in main.py under custom_metrics:

    custom_metrics = VotingMetrics(
    treasury_impact_weight=0.35,
    community_alignment_weight=0.30,
    technical_feasibility_weight=0.20,
    risk_assessment_weight=0.15,
    min_score_to_support=0.65
  )
  
  

• Deployment Info: Stored in deploy_info.json (no .env file needed):

  {
  "network": "ganache-local",
  "governance_address": "0x5FbDB2315678afecb367f032d93F642f64180aa3",
  "ai_wallet_address": "0xe7f1725E7734CE288F8367e1Bb143E90bb3F0512",
  "metrics_registry_address": "0x9fE46736679d2D9a65F0992F2272dE9f3c7fa6e0",
  "ai_agent_address": "0xc0A606038c2E7c86a6acaDf9D9Dd0F55480E3149",
  "deployer_address": "0xf3dD9E569D8Ba6726FE3b7f54A1CFB19998bDc88",
  "ai_agent_private_key": "0x55d655def66eeca0ab86237c4407f3a92033b6cc32d9aa4f8af9f9f9c30fb87a",
  "note": "MOCK DEPLOYMENT - Contracts not actually deployed"
}

Troubleshooting

• Charts Not Displaying:
• Ensure matplotlib and numpy are installed (pip show matplotlib numpy).
• Verify you’re running on a system with a graphical interface (Windows should work fine).
• If charts don’t pop up, check for errors in the console and ensure test_proposals.json exists.
• Missing test_proposals.json:
• Run python deployment_testing.py to generate it.
• Python Errors:
• Ensure Python 3.6+ is used.
• Check console output for specific errors and address missing dependencies.
• Chart Data Mismatch:
• Verify test_proposals.json contains 5 proposals with expected descriptions.
• Ensure main.py is the Matplotlib version.

Notes

• The project currently runs in mock mode (use_mock_data=True), using test_proposals.json for testing.
• No .env file is required; configuration is handled via deploy_info.json.
• Charts are displayed as pop-up windows using Matplotlib, requiring no browser or internet connection.

Contributing

• Contributions welcome! Fork the repo, make changes, and submit a pull request. Open an issue for major updates.

License

• This project is for educational and testing purposes. No license is specified.