ch09 up

README.md

@@ -32,15 +32,15 @@ If you are interested in keeping in touch, I have quite a lively twitter stream
 
 Excerpts from the [Foreword](./docs/foreword_ro.pdf) and [Preface](./docs/preface_sr.pdf).
 
-1. Machine Learning - Giving Computers the Ability to Learn from Data [[./code/ch01](./code/ch01)] [[ipynb](./code/ch01/ch01.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch01/ch01.ipynb)]
-2. Training Machine Learning Algorithms for Classification [[./code/ch02](./code/ch02)] [[ipynb](./code/ch02/ch02.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch02/ch02.ipynb)]
-3. A Tour of Machine Learning Classifiers Using Scikit-Learn [[./code/ch03](./code/ch03)] [[ipynb](./code/ch03/ch03.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch03/ch03.ipynb)]
-4. Building Good Training Sets – Data Pre-Processing [[./code/ch04](./code/ch04)] [[ipynb](./code/ch04/ch04.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch04/ch04.ipynb)]
-5. Compressing Data via Dimensionality Reduction [[./code/ch05](./code/ch05)] [[ipynb](./code/ch05/ch05.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch05/ch05.ipynb)] 
-6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[./code/ch06](./code/ch06)] [[ipynb](./code/ch06/ch06.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch06/ch06.ipynb)]
-7. Combining Different Models for Ensemble Learning [[./code/ch07](./code/ch07)] [[ipynb](./code/ch07/ch07.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch07/ch07.ipynb)]
-8. Applying Machine Learning to Sentiment Analysis
-9. Embedding a Machine Learning Model into a Web Application
+1. Machine Learning - Giving Computers the Ability to Learn from Data [[dir](./code/ch01)] [[ipynb](./code/ch01/ch01.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch01/ch01.ipynb)]
+2. Training Machine Learning Algorithms for Classification [[dir](./code/ch02)] [[ipynb](./code/ch02/ch02.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch02/ch02.ipynb)]
+3. A Tour of Machine Learning Classifiers Using Scikit-Learn [[dir](./code/ch03)] [[ipynb](./code/ch03/ch03.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch03/ch03.ipynb)]
+4. Building Good Training Sets – Data Pre-Processing [[dir](./code/ch04)] [[ipynb](./code/ch04/ch04.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch04/ch04.ipynb)]
+5. Compressing Data via Dimensionality Reduction [[dir](./code/ch05)] [[ipynb](./code/ch05/ch05.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch05/ch05.ipynb)] 
+6. Learning Best Practices for Model Evaluation and Hyperparameter Optimization [[dir](./code/ch06)] [[ipynb](./code/ch06/ch06.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch06/ch06.ipynb)]
+7. Combining Different Models for Ensemble Learning [[dir](./code/ch07)] [[ipynb](./code/ch07/ch07.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch07/ch07.ipynb)]
+8. Applying Machine Learning to Sentiment Analysis [[dir](./code/ch08)] [[ipynb](./code/ch08/ch08.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch08/ch08.ipynb)]
+9. Embedding a Machine Learning Model into a Web Application [[dir](./code/ch09)] [[ipynb](./code/ch09/ch09.ipynb)] [[nbviewer](http://nbviewer.ipython.org/github/rasbt/python-machine-learning-book/blob/master/code/ch09/ch09.ipynb)]
 10. Predicting Continuous Target Variables with Regression Analysis
 11. Working with Unlabeled Data – Clustering Analysis
 12. Training Artificial Neural Networks for Image Recognition

code/ch09/1st_flask_app_1/app.py

@@ -0,0 +1,10 @@
+from flask import Flask, render_template
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    return render_template('first_app.html')
+
+if __name__ == '__main__':
+    app.run(debug=True)

code/ch09/1st_flask_app_1/templates/first_app.html

@@ -0,0 +1,13 @@
+<!doctype html>
+<html>
+  <head>
+    <title>First app</title>
+  </head>
+  <body>
+
+  <div>
+	Hi, this is my first Flask web app!
+  </div>
+
+  </body>
+</html>

code/ch09/1st_flask_app_2/app.py

@@ -0,0 +1,23 @@
+from flask import Flask, render_template, request
+from wtforms import Form, TextAreaField, validators
+
+app = Flask(__name__)
+
+class HelloForm(Form):
+    sayhello = TextAreaField('',[validators.DataRequired()])
+
+@app.route('/')
+def index():
+    form = HelloForm(request.form)
+    return render_template('first_app.html', form=form)
+
+@app.route('/hello', methods=['POST'])
+def hello():
+    form = HelloForm(request.form)
+    if request.method == 'POST' and form.validate():
+        name = request.form['sayhello']
+        return render_template('hello.html', name=name)
+    return render_template('first_app.html', form=form)
+
+if __name__ == '__main__':
+    app.run(debug=True)

code/ch09/1st_flask_app_2/static/style.css

@@ -0,0 +1,3 @@
+body {
+	font-size: 2em;
+}

code/ch09/1st_flask_app_2/templates/_formhelpers.html

@@ -0,0 +1,12 @@
+{% macro render_field(field) %}
+  <dt>{{ field.label }}
+  <dd>{{ field(**kwargs)|safe }}
+  {% if field.errors %}
+    <ul class=errors>
+    {% for error in field.errors %}
+      <li>{{ error }}</li>
+    {% endfor %}
+    </ul>
+  {% endif %}
+  </dd>
+{% endmacro %}

code/ch09/1st_flask_app_2/templates/first_app.html

@@ -0,0 +1,23 @@
+<!doctype html>
+<html>
+  <head>
+    <title>First app</title>
+	<link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
+  </head>
+  <body>
+
+{% from "_formhelpers.html" import render_field %}
+
+<div>What's your name?</div>
+<form method=post action="/hello">
+
+  <dl>
+	  {{ render_field(form.sayhello) }}
+  </dl>
+
+  <input type=submit value='Say Hello' name='submit_btn'>
+
+</form>
+
+  </body>
+</html>

code/ch09/1st_flask_app_2/templates/hello.html

@@ -0,0 +1,13 @@
+<!doctype html>
+<html>
+  <head>
+    <title>First app</title>
+	<link rel="stylesheet" href="{{ url_for('static', filename='style.css') }}">
+  </head>
+  <body>
+
+<div>Hello {{ name }}</div>
+
+
+  </body>
+</html>

code/ch09/README.md

@@ -3,4 +3,26 @@ Sebastian Raschka, 2015
 # Python Machine Learning
 # Chapter 9 Code Examples
 
-## Embedding a Machine Learning Model into a Web Application
+## Embedding a Machine Learning Model into a Web Application
+
+
+The code for the Flask web applications can be found in the following directories:
+
+- `1st_flask_app_1/`: A simple Flask web app
+- `1st_flask_app_2/`: `1st_flask_app_1` extended with flexible form validation and rendering
+- `movieclassifier/`: The movie classifier embedded in a web application
+- `movieclassifier_with_update/`: same as `movieclassifier` but with update from sqlite database upon start
+
+
+To run the web applications locally, `cd` into the respective directory (as listed above) and execute the main-application script, for example,
+
+    cd ./1st_flask_app_1
+    python3 app.py
+
+Now, you should see something like
+
+     * Running on http://127.0.0.1:5000/
+     * Restarting with reloader
+
+in your terminal.
+Next, open a web browsert and enter the address displayed in your terminal (typically http://127.0.0.1:5000/) to view the web application.