let session = null;

async function loadModel() {
    try {
        session = await ort.InferenceSession.create("iris_model.onnx");

        document.getElementById("result").innerText =
            "Model loaded successfully!";

        console.log("INPUT NAMES:", session.inputNames);
        console.log("OUTPUT NAMES:", session.outputNames);

    } catch (error) {
        document.getElementById("result").innerText =
            "Model loading error: " + error.message;

        console.error(error);
    }
}


async function predict() {

    const resultBox = document.getElementById("result");

    if (!session) {
        resultBox.innerText = "Model is not ready.";
        return;
    }

    const values = [
        parseFloat(document.getElementById("sepalLength").value),
        parseFloat(document.getElementById("sepalWidth").value),
        parseFloat(document.getElementById("petalLength").value),
        parseFloat(document.getElementById("petalWidth").value)
    ];

    if (values.some(v => Number.isNaN(v))) {
        resultBox.innerText = "Please enter all four values.";
        return;
    }

    try {

        const inputTensor = new ort.Tensor(
            "float32",
            new Float32Array(values),
            [1, 4]
        );

        const results = await session.run({
            float_input: inputTensor
        });

        console.log("========== ONNX RESULTS ==========");

        for (const name of session.outputNames) {

            const output = results[name];

            console.log("NAME:", name);
            console.log("OUTPUT:", output);
            console.log("TYPE:", typeof output);

            if (output) {
                console.log("DATA:", output.data);
                console.log("DIMS:", output.dims);
            }

            console.log("--------------------------------");
        }

        resultBox.innerText =
            "Prediction executed. Press F12 → Console and check the output.";

    } catch (error) {

        console.error("PREDICTION ERROR:", error);

        resultBox.innerText =
            "Prediction error: " + error.message;
    }
}


loadModel();