Update code.txt

code.txt

@@ -144,6 +144,7 @@ for i, label in enumerate(label_names):
 
 import React, { useState, useEffect } from "react";
 import * as tflite from "@tensorflow/tfjs-tflite";
+import * as tf from "@tensorflow/tfjs";
 
 function ObjectDetector() {
   const [model, setModel] = useState(null);
@@ -174,13 +175,29 @@ function ObjectDetector() {
 
     const imageElement = document.getElementById("inputImage");
 
-    // Prepare the image
-    const input = tflite.toTensor(imageElement, [1, 224, 224, 3]);  // Resize and convert to tensor, adjust size as per your model
-    const output = await model.predict(input);  // Run inference
+    // Load the image into a tensor
+    const inputTensor = preprocessImage(imageElement, [1, 224, 224, 3]);  // Adjust this size based on your model's expected input
 
-    // Interpret the results
-    // This part depends on the output format of your TFLite model (e.g., bounding boxes, class labels)
-    setPredictions(output);  // Adjust this based on your model's output structure
+    // Run inference
+    const output = await model.predict(inputTensor);
+
+    // Interpret the results (adjust according to your model's output format)
+    const outputData = output.arraySync();  // This converts the Tensor to regular JS array, adjust based on your model's output
+    setPredictions(outputData);  // Adjust this based on your model's output structure
+
+    // Clean up the tensor to free memory
+    tf.dispose([inputTensor]);
+  };
+
+  // Function to preprocess image (resize, normalize, and convert to tensor)
+  const preprocessImage = (image, inputShape) => {
+    const tensor = tf.browser.fromPixels(image)   // Load image into a tensor
+      .resizeNearestNeighbor([inputShape[1], inputShape[2]])  // Resize to model's input shape
+      .toFloat()
+      .div(tf.scalar(255.0))  // Normalize pixel values to [0, 1]
+      .expandDims();  // Add batch dimension (1, 224, 224, 3)
+
+    return tensor;
   };
 
   return (
@@ -220,4 +237,5 @@ function ObjectDetector() {
 }
 
 export default ObjectDetector;
+