let canvas;
let ctx;
document.addEventListener("DOMContentLoaded", () => {
       canvas = document.getElementById("drawingCanvas");
       ctx = canvas.getContext("2d");
       let isDrawing = false;

       canvas.addEventListener("mousedown", () => isDrawing = true);
       canvas.addEventListener("mouseup", () => isDrawing = false);
       canvas.addEventListener("mousemove", draw);

       function draw(event) {
           if (!isDrawing) return;
           ctx.fillStyle = "black";
           ctx.fillRect(event.offsetX, event.offsetY, 20, 20);
       }
   });

function clearCanvas() {
    ctx.clearRect(0, 0, canvas.width, canvas.height);
    document.getElementById("result").innerText = "";
}

// Inverting the images as the training images had a black background with white text,
// opposite of what we get from the canvas
function invertCanvasColors(inputCanvas) {
    const tempCanvas = document.createElement("canvas");
    const tempCtx = tempCanvas.getContext("2d");

    // Set the size of the temporary canvas to match the original canvas
    tempCanvas.width = inputCanvas.width;
    tempCanvas.height = inputCanvas.height;

    // Draw the original canvas onto the temporary canvas
    tempCtx.drawImage(inputCanvas, 0, 0);

    // Get the pixel data of the image
    const imageData = tempCtx.getImageData(0, 0, tempCanvas.width, tempCanvas.height);
    const data = imageData.data;

    // Invert each pixel's RGB values
    for (let i = 0; i < data.length; i += 4) {
        data[i] = 255 - data[i];     // Red channel
        data[i + 1] = 255 - data[i + 1]; // Green channel
        data[i + 2] = 255 - data[i + 2]; // Blue channel
    }
    tempCtx.putImageData(imageData, 0, 0);
    return tempCanvas;
}

function sendToServer() {
    const invertedCanvas = invertCanvasColors(canvas);
    let image = invertedCanvas.toDataURL("image/png");
    let blob = dataURItoBlob(image);
    let formData = new FormData();
    formData.append("image", blob, "drawing.png");

    fetch("https://ramachandrankulothungan-digit-doodle-recognition.hf.space/predict", {
        method: "POST",
        body: formData
    })
    .then(response => response.json())
    .then(data => document.getElementById("result").innerText = "Prediction: " + data.prediction)
    .catch(error => console.error("Error:", error));
}

function dataURItoBlob(dataURI) {
    let byteString = atob(dataURI.split(",")[1]);
    let arrayBuffer = new ArrayBuffer(byteString.length);
    let uint8Array = new Uint8Array(arrayBuffer);
    for (let i = 0; i < byteString.length; i++) {
        uint8Array[i] = byteString.charCodeAt(i);
    }
    return new Blob([uint8Array], { type: "image/png" });
}