chore: import upstream snapshot with attribution

code/ch09/movieclassifier/app.py

@@ -0,0 +1,77 @@
+from flask import Flask, render_template, request
+from wtforms import Form, TextAreaField, validators
+import pickle
+import sqlite3
+import os
+import numpy as np
+
+# import HashingVectorizer from local dir
+from vectorizer import vect
+
+app = Flask(__name__)
+
+######## Preparing the Classifier
+cur_dir = os.path.dirname(__file__)
+clf = pickle.load(open(os.path.join(cur_dir,
+                 'pkl_objects',
+                 'classifier.pkl'), 'rb'))
+db = os.path.join(cur_dir, 'reviews.sqlite')
+
+def classify(document):
+    label = {0: 'negative', 1: 'positive'}
+    X = vect.transform([document])
+    y = clf.predict(X)[0]
+    proba = np.max(clf.predict_proba(X))
+    return label[y], proba
+
+def train(document, y):
+    X = vect.transform([document])
+    clf.partial_fit(X, [y])
+
+def sqlite_entry(path, document, y):
+    conn = sqlite3.connect(path)
+    c = conn.cursor()
+    c.execute("INSERT INTO review_db (review, sentiment, date)"\
+    " VALUES (?, ?, DATETIME('now'))", (document, y))
+    conn.commit()
+    conn.close()
+
+######## Flask
+class ReviewForm(Form):
+    moviereview = TextAreaField('',
+                                [validators.DataRequired(),
+                                validators.length(min=15)])
+
+@app.route('/')
+def index():
+    form = ReviewForm(request.form)
+    return render_template('reviewform.html', form=form)
+
+@app.route('/results', methods=['POST'])
+def results():
+    form = ReviewForm(request.form)
+    if request.method == 'POST' and form.validate():
+        review = request.form['moviereview']
+        y, proba = classify(review)
+        return render_template('results.html',
+                                content=review,
+                                prediction=y,
+                                probability=round(proba*100, 2))
+    return render_template('reviewform.html', form=form)
+
+@app.route('/thanks', methods=['POST'])
+def feedback():
+    feedback = request.form['feedback_button']
+    review = request.form['review']
+    prediction = request.form['prediction']
+
+    inv_label = {'negative': 0, 'positive': 1}
+    y = inv_label[prediction]
+    if feedback == 'Incorrect':
+        y = int(not(y))
+    train(review, y)
+    sqlite_entry(db, review, y)
+    return render_template('thanks.html')
+
+if __name__ == '__main__':
+    app.run(debug=True)