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Mobile Artificial Intelligence Projects

Mobile Artificial Intelligence Projects

By : NG, Padmanabhan, Matt Cole
5 (1)
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Mobile Artificial Intelligence Projects

Mobile Artificial Intelligence Projects

5 (1)
By: NG, Padmanabhan, Matt Cole

Overview of this book

We’re witnessing a revolution in Artificial Intelligence, thanks to breakthroughs in deep learning. Mobile Artificial Intelligence Projects empowers you to take part in this revolution by applying Artificial Intelligence (AI) techniques to design applications for natural language processing (NLP), robotics, and computer vision. This book teaches you to harness the power of AI in mobile applications along with learning the core functions of NLP, neural networks, deep learning, and mobile vision. It features a range of projects, covering tasks such as real-estate price prediction, recognizing hand-written digits, predicting car damage, and sentiment analysis. You will learn to utilize NLP and machine learning algorithms to make applications more predictive, proactive, and capable of making autonomous decisions with less human input. In the concluding chapters, you will work with popular libraries, such as TensorFlow Lite, CoreML, and PyTorch across Android and iOS platforms. By the end of this book, you will have developed exciting and more intuitive mobile applications that deliver a customized and more personalized experience to users.
Table of Contents (12 chapters)
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6
PyTorch Experiments on NLP and RNN
7
TensorFlow on Mobile with Speech-to-Text with the WaveNet Model
8
Implementing GANs to Recognize Handwritten Digits

Setting up a web application

We will use the Flask framework to build a simple application to detect the car's damage.

To learn more about Flask, please refer to https://www.fullstackpython.com/flask.html.

We are not going to go deeper into Flask basics here. Instead, we are simply adding our model with an existing file upload example from Flask.

The file's structure is shown in the following screenshot:

Here is a list of the contents in app.py:

import os
import glob
from classify import prediction
import tensorflow as tf
import thread
import time
from flask import Flask, render_template, request, redirect, url_for, send_from_directory,flash
from werkzeug import secure_filename
app = Flask(__name__)
app.config['UPLOAD_FOLDER'] = 'uploads/'
app.config['ALLOWED_EXTENSIONS'] = set(['jpg', 'jpeg'])
app.config['SECRET_KEY&apos...

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