Upload 8 files
Browse files- classes.json +1 -0
- group1-shard1of4.bin +3 -0
- group1-shard2of4.bin +3 -0
- group1-shard3of4.bin +3 -0
- group1-shard4of4.bin +3 -0
- image.jpg +0 -0
- index.html +49 -0
- model.json +0 -0
classes.json
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{"amina mostafa2024020001": 0, "misaa babnjki2024020002": 1}
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group1-shard1of4.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:ebbdab1e709749b997c7eaf21301f50427a8e75f8a3eeafffb35f596a581a15a
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size 4194304
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group1-shard2of4.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:7b33fb298e8b05906c4add9c2579a37a141eb8fe9477b2644d000019707d5e4e
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size 4194304
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group1-shard3of4.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:0f64967234f3678d87ee2df83ee84db0b4c41b83748b506bd45c1674c4ca7be1
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size 4194304
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group1-shard4of4.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:f5081ee48bbd735bb0f963864fad07db7d5cb543b5e8d1f0d00574a2a94fa046
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size 4020860
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image.jpg
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index.html
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<html>
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<head>
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<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
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</head>
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<body>
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Make sure to run this via a server
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<br>
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<input accept="image/*" type='file' id="input_button" />
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<br>
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<img id="img1" src="./image.jpg" style="width: 300px " />
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<br> <br>
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<button onclick="predict()">predict</button>
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<br>
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<p id="result"> </p>
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<script>
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let image = document.getElementById('img1');
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let input_button = document.getElementById('input_button');
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input_button.onchange = evt => {
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const [file] = input_button.files
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if (file) {
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image.src = URL.createObjectURL(file)
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}
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}
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async function predict() {
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var model = await tf.loadGraphModel('./model.json');
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let example = tf.browser.fromPixels(document.getElementById("img1") , 3 ).cast('float32');
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console.log( example.shape )
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example = example.reshape([1,example.shape[0], example.shape[1] ,example.shape[2]]);
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let prediction = await model.predict(example);
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let class_scores = await prediction.data();
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let max_score_id = class_scores.indexOf(Math.max(...class_scores));
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let classes = [ "amina mostafa2024020001" , "misaa babnjki2024020002" , ] ;
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console.log(class_scores);
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document.getElementById("result").innerHTML = classes[max_score_id].toString();
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}
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</script>
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</body>
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</html>
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model.json
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