https://github.com/elizabethsiegle/loves-me-loves-me-not-tensorflow-python-sms
https://www.twilio.com/blog/classify-texts-with-tensorflow-and-twilio-to-answer-loves-me-loves-me-not
https://github.com/elizabethsiegle/loves-me-loves-me-not-tensorflow-python-sms
ai artificial-intelligence bag-of-words deep-learning deep-neural-networks machine-learning machinelearning ml neural-network neural-networks sms tensorflow tensorflow-tutorials twilio
Last synced: about 2 months ago
JSON representation
https://www.twilio.com/blog/classify-texts-with-tensorflow-and-twilio-to-answer-loves-me-loves-me-not
- Host: GitHub
- URL: https://github.com/elizabethsiegle/loves-me-loves-me-not-tensorflow-python-sms
- Owner: elizabethsiegle
- Created: 2020-02-02T18:20:23.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2023-02-16T00:35:59.000Z (over 3 years ago)
- Last Synced: 2025-10-28T22:42:01.751Z (8 months ago)
- Topics: ai, artificial-intelligence, bag-of-words, deep-learning, deep-neural-networks, machine-learning, machinelearning, ml, neural-network, neural-networks, sms, tensorflow, tensorflow-tutorials, twilio
- Language: Python
- Homepage:
- Size: 2.48 MB
- Stars: 3
- Watchers: 1
- Forks: 0
- Open Issues: 11
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
### Answer the "loves me, loves me not" question with Twilio and TensorFlow.
Complete tutorial can be found [here on the Twilio blog](https://www.twilio.com/blog/classify-texts-with-tensorflow-and-twilio-to-answer-loves-me-loves-me-not).
To run, install requirements.txt with `pip3 install -r requirements.txt` then run the Flask app with
```
export FLASK_APP=main
export FLASK_ENV=development
flask run --without-threads
```
Please ignore some of the commits and commit messages, it was a pain to deploy to Heroku because the TensorFlow model was computationally-intensive.
