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avinasshavinassh
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initial import
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‎Procfile

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web: gunicorn app:app

‎README.md

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# PyTorch Flask API
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## Requirements
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Install them from `requirements.txt`:
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pip install -r requirements.txt
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## Local Deployment
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Run the server:
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python app.py
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## Heroku Deployment
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[![Deploy](https://www.herokucdn.com/deploy/button.svg)](https://heroku.com/deploy?template=https://github.com/avinassh/pytorch-flask-api-heroku)

‎app.json

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{
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"name": "PyTorch Image Detection",
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"description": "Image Classifier built using PyTorch",
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"repository": "https://github.com/avinassh/pytorch-flask-api-heroku",
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"keywords": ["python", "flask", "pytorch", "bootstrap"]
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}

‎app.py

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import os
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from flask import Flask, render_template, request, redirect
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from inference import get_prediction
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from commons import format_class_name
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app = Flask(__name__)
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@app.route('/', methods=['GET', 'POST'])
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def upload_file():
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if request.method == 'POST':
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if 'file' not in request.files:
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return redirect(request.url)
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file = request.files.get('file')
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if not file:
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return
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img_bytes = file.read()
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class_id, class_name = get_prediction(image_bytes=img_bytes)
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class_name = format_class_name(class_name)
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return render_template('result.html', class_id=class_id,
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class_name=class_name)
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return render_template('index.html')
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if __name__ == '__main__':
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app.run(debug=True, port=int(os.environ.get('PORT', 5000)))

‎commons.py

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import io
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from PIL import Image
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from torchvision import models
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import torchvision.transforms as transforms
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def get_model():
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model = models.densenet121(pretrained=True)
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model.eval()
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return model
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def transform_image(image_bytes):
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my_transforms = transforms.Compose([transforms.Resize(255),
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transforms.CenterCrop(224),
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transforms.ToTensor(),
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transforms.Normalize(
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[0.485, 0.456, 0.406],
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[0.229, 0.224, 0.225])])
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image = Image.open(io.BytesIO(image_bytes))
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return my_transforms(image).unsqueeze(0)
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# ImageNet classes are often of the form `can_opener` or `Egyptian_cat`
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# will use this method to properly format it so that we get
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# `Can Opener` or `Egyptian Cat`
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def format_class_name(class_name):
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class_name = class_name.replace('_', ' ')
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class_name = class_name.title()
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return class_name

‎imagenet_class_index.json

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‎inference.py

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import json
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from commons import get_model, transform_image
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model = get_model()
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imagenet_class_index = json.load(open('imagenet_class_index.json'))
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def get_prediction(image_bytes):
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try:
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tensor = transform_image(image_bytes=image_bytes)
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outputs = model.forward(tensor)
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except Exception:
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return 0, 'error'
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_, y_hat = outputs.max(1)
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predicted_idx = str(y_hat.item())
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return imagenet_class_index[predicted_idx]

‎requirements.txt

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Flask==1.0.3
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torchvision==0.3.0
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numpy==1.16.4
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Pillow==6.0.0
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gunicorn==19.9.0

‎runtime.txt

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python-3.7.1

‎static/pytorch.png

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‎static/style.css

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html,
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body {
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height: 100%;
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}
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body {
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display: -ms-flexbox;
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display: flex;
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-ms-flex-align: center;
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align-items: center;
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padding-top: 40px;
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padding-bottom: 40px;
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background-color: #f5f5f5;
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}
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.form-signin {
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width: 100%;
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max-width: 330px;
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padding: 15px;
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margin: auto;
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}
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.form-signin .form-control {
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position: relative;
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box-sizing: border-box;
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height: auto;
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padding: 10px;
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font-size: 16px;
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}

‎templates/index.html

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<!doctype html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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<link rel="stylesheet" href="//stackpath.bootstrapcdn.com/bootstrap/4.2.1/css/bootstrap.min.css" integrity="sha384-GJzZqFGwb1QTTN6wy59ffF1BuGJpLSa9DkKMp0DgiMDm4iYMj70gZWKYbI706tWS" crossorigin="anonymous">
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<style>
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.bd-placeholder-img {
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font-size: 1.125rem;
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text-anchor: middle;
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}
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@media (min-width: 768px) {
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.bd-placeholder-img-lg {
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font-size: 3.5rem;
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}
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}
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</style>
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<link rel="stylesheet" href="/static/style.css">
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<title>Image Prediction using PyTorch</title>
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</head>
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<body class="text-center">
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<form class="form-signin" method=post enctype=multipart/form-data>
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<img class="mb-4" src="/static/pytorch.png" alt="" width="72">
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<h1 class="h3 mb-3 font-weight-normal">Upload any image</h1>
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<input type="file" name="file" class="form-control-file" id="inputfile">
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<br/>
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<button class="btn btn-lg btn-primary btn-block" type="submit">Upload</button>
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<p class="mt-5 mb-3 text-muted">Built using Pytorch, Flask and Love</p>
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</form>
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<script src="//code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
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<script src="//cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.6/umd/popper.min.js" integrity="sha384-wHAiFfRlMFy6i5SRaxvfOCifBUQy1xHdJ/yoi7FRNXMRBu5WHdZYu1hA6ZOblgut" crossorigin="anonymous"></script>
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<script src="//stackpath.bootstrapcdn.com/bootstrap/4.2.1/js/bootstrap.min.js" integrity="sha384-B0UglyR+jN6CkvvICOB2joaf5I4l3gm9GU6Hc1og6Ls7i6U/mkkaduKaBhlAXv9k" crossorigin="anonymous"></script>
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<script type="text/javascript">
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$('#inputfile').bind('change', function() {
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let fileSize = this.files[0].size/1024/1024; // this gives in MB
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if (fileSize > 1) {
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$("#inputfile").val(null);
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alert('file is too big. images more than 1MB are not allowed')
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return
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}
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let ext = $('#inputfile').val().split('.').pop().toLowerCase();
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if($.inArray(ext, ['jpg','jpeg']) == -1) {
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$("#inputfile").val(null);
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alert('only jpeg/jpg files are allowed!');
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}
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});
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</script>
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</body>
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</html>

‎templates/result.html

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<!doctype html>
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<html lang="en">
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<head>
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<meta charset="utf-8">
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<meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no">
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<link rel="stylesheet" href="//stackpath.bootstrapcdn.com/bootstrap/4.2.1/css/bootstrap.min.css" integrity="sha384-GJzZqFGwb1QTTN6wy59ffF1BuGJpLSa9DkKMp0DgiMDm4iYMj70gZWKYbI706tWS" crossorigin="anonymous">
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<style>
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.bd-placeholder-img {
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font-size: 1.125rem;
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text-anchor: middle;
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}
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@media (min-width: 768px) {
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.bd-placeholder-img-lg {
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font-size: 3.5rem;
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}
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}
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</style>
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<link rel="stylesheet" href="/static/style.css">
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<title>Image Prediction using PyTorch</title>
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</head>
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<body class="text-center">
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<form class="form-signin" method=post enctype=multipart/form-data>
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<img class="mb-4" src="/static/pytorch.png" alt="" width="72">
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<h1 class="h3 mb-3 font-weight-normal">Prediction</h1>
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<h5 class="h5 mb-3 font-weight-normal">Detected Image: {{ class_name }}</h5>
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<h5 class="h6 mb-3 font-weight-normal">ImageNet Class ID: {{ class_id }}</h5>
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<p class="mt-5 mb-3 text-muted">Built using Pytorch, Flask and Love</p>
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</form>
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<script src="//code.jquery.com/jquery-3.3.1.slim.min.js" integrity="sha384-q8i/X+965DzO0rT7abK41JStQIAqVgRVzpbzo5smXKp4YfRvH+8abtTE1Pi6jizo" crossorigin="anonymous"></script>
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<script src="//cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.6/umd/popper.min.js" integrity="sha384-wHAiFfRlMFy6i5SRaxvfOCifBUQy1xHdJ/yoi7FRNXMRBu5WHdZYu1hA6ZOblgut" crossorigin="anonymous"></script>
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<script src="//stackpath.bootstrapcdn.com/bootstrap/4.2.1/js/bootstrap.min.js" integrity="sha384-B0UglyR+jN6CkvvICOB2joaf5I4l3gm9GU6Hc1og6Ls7i6U/mkkaduKaBhlAXv9k" crossorigin="anonymous"></script>
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</body>
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</html>

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