My gradient descent it not converging. Does someone know what my error is?

I am testing machine learning for my first time. I am only using gradient descent and the cost function. I have two variables. I want to be able to predict a y value when imputing an x value. But right now, I can't get my gradient descent to get anywhere near zero. I've messed with alpha and the initial theta values and I can't get anywhere, I have a feeling I programmed it wrong. Here is my code:

x = [ 7, 8, 3, 2, 12, 1, 5]
y = [500000, 600000, 100000, 75000, 1000000, 5000, 200000]

let m = x.length 

const alpha = 0.0001

let iterations = 1000

var theta0 = 0
var theta1 = 0

var hypothesis = x => theta0 + theta1* x;

function sumPre(){
    sum = 0
    for (let i = 0; i < m; i++) {
        sum += hypothesis(x[i]) - y[i];
      }
      return sum
}

function sumPre2(){
    sum = 0
    for (let i = 0; i < m; i++) {
        sum += (hypothesis(x[i]) - y[i]) * x[i];
      }
      return sum
}


function gD(){
    let m = y.length;

    var theta0 = 0
    var theta1 = 0

    for (let i = 0; i < iterations; i++) {
    
       var thetaZero = theta0 - alpha * (1 / m) * sumPre();
       var thetaOne = theta1 - alpha* (1 / m) * sumPre2();
    
       var theta0 = thetaZero;
       var theta1 = thetaOne;}
       
  return [theta0,theta1]
}
var thetaZero = gD()[0];
var thetaOne = gD()[1]

console.log(thetaZero)
console.log(thetaOne)

var hypothesis = x => thetaZero + thetaOne* x;


//cost function
function cost(){
    let sum = 0;

    for (let i = 0; i < m; i++) {
        sum += Math.pow(hypothesis(x[i]) - y[i], 2);
      }
      return sum / ( 2 * m)
    }

console.log(cost())