Mailman Presentation

Presentation slides for Mailman project.

Transcript

1. Mailman Presentation prepared by Group 10
2. About Ariel Wang Senior, Biology Zuhayer Kalam Senior, Aerospace Eng Andrew Yang Junior, Computational Physics Najia Sherwani Senior, Chemical Eng Aram Baali Senior, Radio-Television- Film
3. Demo
4. import unittest import main from models import database, Message, Sender, Category class Test (unittest.TestCase): #models def setUp(self): main.app.config["TESTING"] = True self.client = main.app.test_client() def testIndex(self): with self.client: response = self.client.get("/") self.assertEqual(response.status_code, 200) #database def test_insert_senders(self): with main.app.app_context(): s = Sender(name ="John Doe", message_count="20", total_size="1000", email="address@domain.com", domain="domain.com") database.session.add(s) database.session.commit() exists = database.session.query(Sender).filter_by(email = "address@domain.com").first() is not None self.assertTrue(exists) def test_read_senders(self): with main.app.app_context(): q = database.session.query(Sender).filter_by(email = "address@domain.com").one() self.assertEqual(str(q.email), "address@domain.com") self.assertEqual(str(q.name), "John Doe') self.assertEqual(str(q.domain), "domain.com") self.assertEqual(str(q.message_count), "20") self.assertEqual(str(q.total_size), "1000") #instances def testOneSender(self): with self.client: response = self.client.get("/oneSender/address@domain.com") self.assertEqual(response.status_code, 200) Unit Tests
5. Strengths & Goals • High-utility through large data set ◦ Gmail API provides access to user’s email inbox (permission required) • Modularized authentication procedure ◦ Easier debugging • Structured data with normalized database design ◦ Efficient data storage ◦ Allows for quick retrieval • Efficient Sorting and Searching is very efficient and both can happen simultaneously (used SQLAlchemy functions in Flask Backend)
6. Self-critique
7. Learnings • Authenticating with Google APIs ◦ Unlocks invaluable, large, custom datasets • Designing normalized databases ◦ Critical for efficient storage and retrieval • Unit-testing web applications • Implementing searching and sorting features
8. Learnings Cont’d connect() token.json creds.json service get_current_user() get_labels() get_messages() get_message_metadata() [id] [label] user message
9. Opportunities & Improvements • Have more useful data analytics (value) ◦ Data reported is simple, not highly impactful • Use nested SQL queries to eliminate redundant loops ◦ Currently relies on querying full database to conduct filtering within Python • Authentication path: Login & Logout • Communication • Node.js + Aesthetics • Address security risk (google thinks we are phishing) • If emails don’t have very robust data, website looked plain • Could load more emails and create data visualizations
10. Our Confusions & Issues :) • Authentication flow interruption on Google Cloud ◦ Successful authentication on venv, while credentials are rejected on CGP deployment • GCP has errors but website will still run ◦ no emails were found sometimes • GCP takes 10-15 minutes to set up • Namecheap can’t have the name gmail • Reasons behind slow API calls ◦ 2 minutes for 500 emails (we have 80 right now) ◦ Only requesting headers/metadata
11. Critique: Group 11
12. Strengths • Easy Navigation • Creative Idea • Displaying the top offenders was a nice touch
13. Learnings • We can see players according to how many yellow and red cards they earned • We learned the founding dates of Premier League clubs • You can get information on soccer players and teams through an API
14. Opportunities • Website coloring could be better • Sorting could use some improvement • Some data instances are blank and don’t seem necessary (Manchester City players with 0 games)
15. Questions • There seem to be a very limited amount of players displayed per club instead of the full roster (players with 0 apps), what went into which players were selected?