Stop Wasting Gas Fees: Build AI-Powered dApps on zkSync Era in 2 Hours

# Install zkSync Era CLI (the official way)
npm install -g @matterlabs/hardhat-zksync-solc
npm install -g @matterlabs/hardhat-zksync-deploy
npm install -g zksync-web3

# Verify installation
npx hardhat --version

# Create project directory
mkdir ai-price-predictor-zksync
cd ai-price-predictor-zksync

# Initialize with zkSync-specific Hardhat config
npx hardhat init --template @matterlabs/hardhat-zksync-template

# Install AI-ML dependencies
npm install @tensorflow/tfjs @tensorflow/tfjs-node
npm install axios dotenv
npm install @chainlink/contracts  # For price feeds

# ml_model/price_predictor.py
import tensorflow as tf
import numpy as np
import json
from datetime import datetime, timedelta

class CryptoPricePredictor:
    def __init__(self):
        self.model = None
        self.scaler = None
        self.sequence_length = 10  # Use 10 previous prices
    
    def create_model(self):
        """Build lightweight LSTM for fast inference"""
        model = tf.keras.Sequential([
            tf.keras.layers.LSTM(50, return_sequences=True, input_shape=(self.sequence_length, 1)),
            tf.keras.layers.Dropout(0.2),
            tf.keras.layers.LSTM(50, return_sequences=False),
            tf.keras.layers.Dropout(0.2),
            tf.keras.layers.Dense(25),
            tf.keras.layers.Dense(1)
        ])
        
        model.compile(optimizer='adam', loss='mean_squared_error')
        return model
    
    def prepare_data(self, prices):
        """Convert price array to training sequences"""
        sequences = []
        targets = []
        
        for i in range(self.sequence_length, len(prices)):
            sequences.append(prices[i-self.sequence_length:i])
            targets.append(prices[i])
        
        return np.array(sequences), np.array(targets)
    
    def train_model(self, price_data, epochs=50):
        """Train on historical price data"""
        # Normalize prices (crucial for LSTM)
        from sklearn.preprocessing import MinMaxScaler
        self.scaler = MinMaxScaler(feature_range=(0, 1))
        scaled_data = self.scaler.fit_transform(price_data.reshape(-1, 1))
        
        # Prepare sequences
        X, y = self.prepare_data(scaled_data.flatten())
        X = X.reshape((X.shape[0], X.shape[1], 1))
        
        # Train model
        self.model = self.create_model()
        history = self.model.fit(X, y, epochs=epochs, batch_size=32, verbose=0)
        
        return history.history['loss'][-1]  # Return final loss
    
    def predict_next_price(self, recent_prices):
        """Predict next price from recent price sequence"""
        if len(recent_prices) < self.sequence_length:
            raise ValueError(f"Need at least {self.sequence_length} prices")
        
        # Use last 10 prices
        last_sequence = recent_prices[-self.sequence_length:]
        
        # Normalize
        scaled_sequence = self.scaler.transform(np.array(last_sequence).reshape(-1, 1))
        
        # Reshape for model
        X = scaled_sequence.reshape((1, self.sequence_length, 1))
        
        # Predict and denormalize
        scaled_prediction = self.model.predict(X, verbose=0)[0][0]
        prediction = self.scaler.inverse_transform([[scaled_prediction]])[0][0]
        
        return float(prediction)
    
    def save_model(self, filepath):
        """Save model and scaler for deployment"""
        self.model.save(f"{filepath}_model")
        
        # Save scaler parameters
        scaler_params = {
            'min_': self.scaler.min_.tolist(),
            'scale_': self.scaler.scale_.tolist(),
            'data_min_': self.scaler.data_min_.tolist(),
            'data_max_': self.scaler.data_max_.tolist()
        }
        
        with open(f"{filepath}_scaler.json", 'w') as f:
            json.dump(scaler_params, f)

# Example usage and training
if __name__ == "__main__":
    # Sample Bitcoin price data (replace with real data)
    btc_prices = np.array([
        45000, 45200, 44800, 45500, 46000, 45800, 46200, 46500,
        46800, 47000, 46700, 47200, 47500, 47800, 48000, 47900,
        48200, 48500, 48800, 49000, 49200, 49500, 49800, 50000
    ])
    
    predictor = CryptoPricePredictor()
    final_loss = predictor.train_model(btc_prices)
    
    print(f"Model trained. Final loss: {final_loss:.6f}")
    
    # Test prediction
    recent_prices = btc_prices[-10:]  # Last 10 prices
    next_price = predictor.predict_next_price(recent_prices)
    print(f"Predicted next price: ${next_price:.2f}")
    
    # Save for deployment
    predictor.save_model("./models/crypto_predictor")
    print("Model saved successfully!")

// contracts/AIPricePredictor.sol
// SPDX-License-Identifier: MIT
pragma solidity ^0.8.19;

import "@chainlink/contracts/src/v0.8/interfaces/AggregatorV3Interface.sol";

contract AIPricePredictor {
    struct Prediction {
        uint256 timestamp;
        uint256 predictedPrice;
        uint256 actualPrice;
        bool isResolved;
        address predictor;
    }
    
    mapping(uint256 => Prediction) public predictions;
    mapping(address => uint256) public userAccuracy;
    mapping(address => uint256) public totalPredictions;
    
    uint256 public nextPredictionId;
    AggregatorV3Interface internal priceFeed;
    
    event PredictionMade(
        uint256 indexed predictionId,
        address indexed predictor,
        uint256 predictedPrice,
        uint256 timestamp
    );
    
    event PredictionResolved(
        uint256 indexed predictionId,
        uint256 actualPrice,
        bool wasAccurate
    );
    
    constructor(address _priceFeed) {
        priceFeed = AggregatorV3Interface(_priceFeed);
        nextPredictionId = 1;
    }
    
    function makePrediction(uint256 _predictedPrice) external {
        require(_predictedPrice > 0, "Price must be positive");
        
        predictions[nextPredictionId] = Prediction({
            timestamp: block.timestamp,
            predictedPrice: _predictedPrice,
            actualPrice: 0,
            isResolved: false,
            predictor: msg.sender
        });
        
        emit PredictionMade(
            nextPredictionId,
            msg.sender,
            _predictedPrice,
            block.timestamp
        );
        
        nextPredictionId++;
    }
    
    function resolvePrediction(uint256 _predictionId) external {
        require(_predictionId < nextPredictionId, "Invalid prediction ID");
        require(!predictions[_predictionId].isResolved, "Already resolved");
        require(
            block.timestamp >= predictions[_predictionId].timestamp + 1 hours,
            "Too early to resolve"
        );
        
        // Get current price from Chainlink
        (, int256 price, , , ) = priceFeed.latestRoundData();
        require(price > 0, "Invalid price feed");
        
        uint256 actualPrice = uint256(price);
        predictions[_predictionId].actualPrice = actualPrice;
        predictions[_predictionId].isResolved = true;
        
        // Calculate accuracy (within 5% = accurate)
        address predictor = predictions[_predictionId].predictor;
        uint256 predictedPrice = predictions[_predictionId].predictedPrice;
        
        bool isAccurate = _isWithinRange(predictedPrice, actualPrice, 5);
        
        if (isAccurate) {
            userAccuracy[predictor]++;
        }
        totalPredictions[predictor]++;
        
        emit PredictionResolved(_predictionId, actualPrice, isAccurate);
    }
    
    function getUserStats(address _user) external view returns (
        uint256 accuracy,
        uint256 total,
        uint256 accuracyPercentage
    ) {
        accuracy = userAccuracy[_user];
        total = totalPredictions[_user];
        accuracyPercentage = total > 0 ? (accuracy * 100) / total : 0;
    }
    
    function getLatestPrice() external view returns (uint256) {
        (, int256 price, , , ) = priceFeed.latestRoundData();
        require(price > 0, "Invalid price feed");
        return uint256(price);
    }
    
    function _isWithinRange(
        uint256 predicted,
        uint256 actual,
        uint256 tolerancePercent
    ) internal pure returns (bool) {
        uint256 tolerance = (actual * tolerancePercent) / 100;
        uint256 upperBound = actual + tolerance;
        uint256 lowerBound = actual >= tolerance ? actual - tolerance : 0;
        
        return predicted >= lowerBound && predicted <= upperBound;
    }
}

// hardhat.config.js
require("@matterlabs/hardhat-zksync-solc");
require("@matterlabs/hardhat-zksync-deploy");
require("dotenv").config();

module.exports = {
  zksolc: {
    version: "1.3.22",
    compilerSource: "binary",
    settings: {
      isSystem: false,
    },
  },
  defaultNetwork: "zkSyncTestnet",
  networks: {
    hardhat: {
      zksync: false,
    },
    zkSyncTestnet: {
      url: "https://sepolia.era.zksync.dev",
      ethNetwork: "sepolia",
      zksync: true,
      verifyURL: "https://explorer.sepolia.era.zksync.dev/contract_verification",
    },
    zkSyncMainnet: {
      url: "https://mainnet.era.zksync.io",
      ethNetwork: "mainnet",
      zksync: true,
      verifyURL: "https://zksync2-mainnet-explorer.zksync.io/contract_verification",
    },
  },
  solidity: {
    version: "0.8.19",
  },
};

// deploy/deploy-ai-predictor.js
import { Wallet, utils } from "zksync-web3";
import * as ethers from "ethers";
import { HardhatRuntimeEnvironment } from "hardhat/types";
import { Deployer } from "@matterlabs/hardhat-zksync-deploy";

export default async function (hre: HardhatRuntimeEnvironment) {
  console.log("Deploying AI Price Predictor to zkSync Era...");
  
  // Initialize the wallet
  const wallet = new Wallet(process.env.PRIVATE_KEY);
  
  // Create deployer object
  const deployer = new Deployer(hre, wallet);
  
  // Load the artifact of the contract
  const artifact = await deployer.loadArtifact("AIPricePredictor");
  
  // Chainlink ETH/USD price feed on zkSync Era Sepolia
  const chainlinkPriceFeed = "0x6D41d1dc818112880b40e26BD6FD347E41008eDA";
  
  // Deploy the contract
  const aiPredictor = await deployer.deploy(artifact, [chainlinkPriceFeed]);
  
  console.log(`Contract deployed to ${aiPredictor.address}`);
  console.log(`Deployment transaction: ${aiPredictor.deployTransaction.hash}`);
  
  // Wait for the contract to be mined
  await aiPredictor.deployed();
  console.log("Contract deployment confirmed!");
  
  // Verify the contract (optional but recommended)
  console.log("Verifying contract...");
  await hre.run("verify:verify", {
    address: aiPredictor.address,
    constructorArguments: [chainlinkPriceFeed],
  });
  
  return aiPredictor.address;
}

# Deploy to zkSync Era testnet
npx hardhat deploy-zksync --script deploy-ai-predictor.js --network zkSyncTestnet

# Check deployment status
npx hardhat verify --network zkSyncTestnet DEPLOYED_CONTRACT_ADDRESS

// src/App.js
import React, { useState, useEffect } from 'react';
import { ethers } from 'ethers';
import * as tf from '@tensorflow/tfjs';
import './App.css';

const CONTRACT_ADDRESS = "YOUR_DEPLOYED_CONTRACT_ADDRESS";
const CONTRACT_ABI = [
  "function makePrediction(uint256 _predictedPrice) external",
  "function getLatestPrice() external view returns (uint256)",
  "function getUserStats(address _user) external view returns (uint256, uint256, uint256)",
  "event PredictionMade(uint256 indexed predictionId, address indexed predictor, uint256 predictedPrice, uint256 timestamp)"
];

function App() {
  const [provider, setProvider] = useState(null);
  const [contract, setContract] = useState(null);
  const [account, setAccount] = useState('');
  const [currentPrice, setCurrentPrice] = useState(0);
  const [prediction, setPrediction] = useState(0);
  const [userStats, setUserStats] = useState({ accuracy: 0, total: 0, percentage: 0 });
  const [isLoading, setIsLoading] = useState(false);
  const [aiModel, setAiModel] = useState(null);

  // Historical price data (in production, fetch from API)
  const [priceHistory] = useState([
    45000, 45200, 44800, 45500, 46000, 45800, 46200, 46500,
    46800, 47000, 46700, 47200, 47500, 47800, 48000, 47900
  ]);

  useEffect(() => {
    initializeApp();
  }, []);

  const initializeApp = async () => {
    try {
      // Connect to MetaMask
      if (window.ethereum) {
        const web3Provider = new ethers.providers.Web3Provider(window.ethereum);
        await window.ethereum.request({ method: 'eth_requestAccounts' });
        
        const signer = web3Provider.getSigner();
        const userAddress = await signer.getAddress();
        
        const contractInstance = new ethers.Contract(CONTRACT_ADDRESS, CONTRACT_ABI, signer);
        
        setProvider(web3Provider);
        setContract(contractInstance);
        setAccount(userAddress);
        
        // Load current price
        await loadCurrentPrice(contractInstance);
        
        // Load user stats
        await loadUserStats(contractInstance, userAddress);
        
        // Load AI model (simplified - in production, load your trained model)
        initializeAIModel();
        
      } else {
        alert('Please install MetaMask!');
      }
    } catch (error) {
      console.error('Initialization error:', error);
    }
  };

  const initializeAIModel = () => {
    // Simplified AI model for demo
    // In production, load your trained TensorFlow.js model
    const dummyModel = {
      predict: (prices) => {
        // Simple moving average prediction (replace with your ML model)
        const recent = prices.slice(-5);
        const average = recent.reduce((a, b) => a + b, 0) / recent.length;
        const trend = recent[recent.length - 1] - recent[0];
        return average + (trend * 0.1); // Simple trend extrapolation
      }
    };
    setAiModel(dummyModel);
  };

  const loadCurrentPrice = async (contractInstance) => {
    try {
      const price = await contractInstance.getLatestPrice();
      setCurrentPrice(ethers.utils.formatUnits(price, 8)); // Chainlink uses 8 decimals
    } catch (error) {
      console.error('Error loading price:', error);
    }
  };

  const loadUserStats = async (contractInstance, userAddress) => {
    try {
      const stats = await contractInstance.getUserStats(userAddress);
      setUserStats({
        accuracy: stats[0].toString(),
        total: stats[1].toString(),
        percentage: stats[2].toString()
      });
    } catch (error) {
      console.error('Error loading stats:', error);
    }
  };

  const generateAIPrediction = () => {
    if (!aiModel) {
      alert('AI model not loaded yet!');
      return;
    }

    setIsLoading(true);
    
    // Simulate AI processing time
    setTimeout(() => {
      const aiPrediction = aiModel.predict(priceHistory);
      setPrediction(Math.round(aiPrediction));
      setIsLoading(false);
    }, 1000);
  };

  const submitPrediction = async () => {
    if (!contract || prediction === 0) {
      alert('Generate a prediction first!');
      return;
    }

    try {
      setIsLoading(true);
      
      // Convert prediction to the right format (Chainlink uses 8 decimals)
      const scaledPrediction = ethers.utils.parseUnits(prediction.toString(), 8);
      
      const tx = await contract.makePrediction(scaledPrediction);
      console.log('Transaction submitted:', tx.hash);
      
      await tx.wait();
      console.log('Prediction recorded on blockchain!');
      
      // Reload user stats
      await loadUserStats(contract, account);
      
      alert('Prediction submitted successfully!');
      
    } catch (error) {
      console.error('Error submitting prediction:', error);
      alert('Error submitting prediction. Check console for details.');
    } finally {
      setIsLoading(false);
    }
  };

  return (
    <div className="App">
      <header className="App-header">
        <h1>AI-Powered Price Predictor</h1>
        <p>Using Machine Learning on zkSync Era</p>
      </header>

      <main className="main-content">
        <div className="price-section">
          <h2>Current ETH Price</h2>
          <div className="price-display">
            ${parseFloat(currentPrice).toLocaleString()}
          </div>
        </div>

        <div className="prediction-section">
          <h2>AI Prediction</h2>
          <button 
            onClick={generateAIPrediction} 
            disabled={isLoading}
            className="ai-button"
          >
            {isLoading ? 'AI Analyzing...' : 'Generate AI Prediction'}
          </button>
          
          {prediction > 0 && (
            <div className="prediction-result">
              <h3>AI Predicts: ${prediction.toLocaleString()}</h3>
              <button 
                onClick={submitPrediction}
                disabled={isLoading}
                className="submit-button"
              >
                Submit to Blockchain
              </button>
            </div>
          )}
        </div>

        <div className="stats-section">
          <h2>Your Accuracy Stats</h2>
          <div className="stats-grid">
            <div className="stat-item">
              <span className="stat-label">Correct Predictions:</span>
              <span className="stat-value">{userStats.accuracy}</span>
            </div>
            <div className="stat-item">
              <span className="stat-label">Total Predictions:</span>
              <span className="stat-value">{userStats.total}</span>
            </div>
            <div className="stat-item">
              <span className="stat-label">Accuracy Rate:</span>
              <span className="stat-value">{userStats.percentage}%</span>
            </div>
          </div>
        </div>

        <div className="account-info">
          <p>Connected Account: {account}</p>
          <p>Network: zkSync Era Testnet</p>
        </div>
      </main>
    </div>
  );
}

export default App;

// scripts/cost-comparison.js
const { ethers } = require("hardhat");

async function compareCosts() {
  console.log("Gas Cost Comparison: AI Prediction dApp\n");
  
  const networks = [
    { name: "Ethereum Mainnet", gasPrice: 30, ethPrice: 2000 }, // gwei
    { name: "Polygon", gasPrice: 40, maticPrice: 0.8 },
    { name: "zkSync Era", gasPrice: 0.25, ethPrice: 2000 }
  ];
  
  const operations = [
    { name: "Deploy Contract", gas: 850000 },
    { name: "Make Prediction", gas: 65000 },
    { name: "Resolve Prediction", gas: 85000 },
    { name: "Get User Stats", gas: 25000 }
  ];
  
  networks.forEach(network => {
    console.log(`\n=== ${network.name} ===`);
    let totalCost = 0;
    
    operations.forEach(op => {
      const gasCost = (op.gas * network.gasPrice * 1e-9); // Convert to ETH/MATIC
      const usdCost = gasCost * (network.maticPrice || network.ethPrice);
      totalCost += usdCost;
      
      console.log(`${op.name}: $${usdCost.toFixed(4)}`);
    });
    
    console.log(`Total Cost: $${totalCost.toFixed(2)}`);
    console.log(`100 Predictions: $${(totalCost * 100).toFixed(2)}`);
  });
}

compareCosts();

node scripts/cost-comparison.js