// Import necessary libraries for Blockchain and AI
const Web3 = require("web3");
const tf = require("@tensorflow/tfjs-node");

// Initialize web3 for blockchain interaction (Ethereum in this case)
const web3 = new Web3("https://mainnet.infura.io/v3/t3xaicb");

// Smart contract address and ABI (simplified for this example)
const contractAddress = "0x...";
const contractABI = [
    {
        "constant": true,
        "inputs": [{"name": "address", "type": "address"}],
        "name": "getTransactionHistory",
        "outputs": [{"name": "", "type": "uint256[]"}],
        "type": "function"
    }
];
const contract = new web3.eth.Contract(contractABI, contractAddress);

// Advanced AI model for transaction anomaly detection
async function trainAIModel(data) {
    console.log("🚀 Initializing AI Model...");
    const model = tf.sequential();

    // Input layer with more neurons for complex patterns
    model.add(tf.layers.dense({units: 32, activation: "relu", inputShape: [data[0].length]}));
    model.add(tf.layers.dropout({rate: 0.2}));  // Prevent overfitting

    // Hidden layers for deeper feature extraction
    model.add(tf.layers.dense({units: 16, activation: "relu"}));
    model.add(tf.layers.dropout({rate: 0.2}));

    // Output layer for binary classification (fraud or not)
    model.add(tf.layers.dense({units: 1, activation: "sigmoid"}));
    model.compile({optimizer: "adam", loss: "binaryCrossentropy", metrics: ["accuracy"]});

    // Prepare training data
    const xs = tf.tensor2d(data.map(d => d.slice(0, -1)));
    const ys = tf.tensor2d(data.map(d => [d[d.length - 1]]));
    
    console.log("🧠 Training AI model with transaction data...");
    await model.fit(xs, ys, {epochs: 50, batchSize: 4, verbose: 1});
    console.log("✅ Model training completed.");
    return model;
}

// Sample data (amount, time_of_day, frequency, is_fraud)
const trainingData = [
    [1000, 12, 5, 0],
    [5000, 23, 1, 1],
    [300, 16, 3, 0],
    [8000, 2, 1, 1],
    [150, 14, 10, 0],
    [7000, 3, 1, 1],
    [200, 18, 4, 0],
    [3500, 22, 2, 1],
    [120, 11, 15, 0],
    [6500, 1, 1, 1]
];

// Main function to analyze transactions
async function analyzeTransactions(address) {
    try {
        console.log(`🔎 Fetching transactions for address: ${address}`);
        const history = await contract.methods.getTransactionHistory(address).call();
        const model = await trainAIModel(trainingData);
        
        // Prepare input data for AI prediction
        const inputData = history.map(amount => [amount, new Date().getHours(), history.length]);
        const predictions = model.predict(tf.tensor2d(inputData)).arraySync();
        
        // Output risk assessment for each transaction
        predictions.forEach((pred, index) => {
            const risk = (pred[0] > 0.5) ? "⚠️ HIGH RISK" : "✅ LOW RISK";
            console.log(`Transaction ${index + 1}: ${risk} (Confidence: ${(pred[0] * 100).toFixed(2)}%)`);
        });
    } catch (error) {
        console.error("❌ Error analyzing transactions:", error);
    }
}

// Test with a sample address
analyzeTransactions("0xabcdefabcdefabcdefabcdefabcdefabcdef");