Ever left a restaurant wanting to write a review, but thinking it wasn't worth the trouble to tap out all those words on your phone -- you just want to give the place your n stars and provide a few words of praise or condemnation? If only you could press a button to generate a plausible review. If this project happens, you will.
We'll use the Yelp API to grab as many reviews of certain types of restaurants as the terms of service allow (I assume "Use any robot, spider, site search/retrieval application, or other automated device, process or means to access, retrieve, scrape, or index any portion of the Site or any Site Content;" doesn't apply to API users -- otherwise it wouldn't be much of an API).
We'll investigate libraries like spaCy for doing natural language processing (in Python).
We'll dive into the research on Markov Chains for Natural Language Generation
Finally we'll put this functionality on the web, using Flask & SQLAlchemy in front of a postgres database. The idea is to generate a sample review, and allow the user to easily tweak it and copy it to the clipboard, not to automatically post the reviews to Yelp.
Looking for hackers with the skills:
This project is part of:
Hack Week 15
Activity
Comments
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almost 9 years ago by ericp | Reply
Mixed results on the hack.
On one hand, I learned how to use Python's
nltklibrary to do natural language processing on the reviews, to the point that I could lex each word.nltkhas parsing abilities as well, but I decided to see how far I could get with just lexing and statistical analysis. Also, theflaskandsqlalchemylibraries were straightforward Python analogs of Ruby'ssinatraandActiveRecord/SequelORMs, nothing new here. And the Yelp API was straightforward to work with.The downside was that the Yelp API only exposed the first 10 words of three reviews for each business. If we assume the average business has 100 reviews of about 200 words each, this wasn't going to give me the data I needed. However, each review in the resource returned by
https://api.yelp.com/v3/businessesBUSINESSID/reviewsalso contained a URL, and following that URL gave me the full text of 19 reviews.And apparently following that URL violated Yelp's general terms of service, but not the developers's ToS, and I was cut off after pulling down reviews for 350 restaurants. At least I randomized my selection procedure, so I ended up with a smattering of Mexican, Chinese, Japanese and American-style restaurants.
The best generated sentence might have been one of the first: "I travel the bone tender." I also liked "My wife had the chipotle pancakes." But most of the sentences were grammatically incorrect, or made no sense, or both. I did try tweaking the Markov generator to use a mix of single-word and double-word prefixes, but given the lack of data, I ended the hack and went back to work.
Similar Projects
Testing and adding GNU/Linux distributions on Uyuni by juliogonzalezgil
Join the Gitter channel! https://gitter.im/uyuni-project/hackweek
Uyuni is a configuration and infrastructure management tool that saves you time and headaches when you have to manage and update tens, hundreds or even thousands of machines. It also manages configuration, can run audits, build image containers, monitor and much more!
Currently there are a few distributions that are completely untested on Uyuni or SUSE Manager (AFAIK) or just not tested since a long time, and could be interesting knowing how hard would be working with them and, if possible, fix whatever is broken.
For newcomers, the easiest distributions are those based on DEB or RPM packages. Distributions with other package formats are doable, but will require adapting the Python and Java code to be able to sync and analyze such packages (and if salt does not support those packages, it will need changes as well). So if you want a distribution with other packages, make sure you are comfortable handling such changes.
No developer experience? No worries! We had non-developers contributors in the past, and we are ready to help as long as you are willing to learn. If you don't want to code at all, you can also help us preparing the documentation after someone else has the initial code ready, or you could also help with testing :-)
The idea is testing Salt (including bootstrapping with bootstrap script) and Salt-ssh clients
To consider that a distribution has basic support, we should cover at least (points 3-6 are to be tested for both salt minions and salt ssh minions):
- Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file)
- Onboarding (salt minion from UI, salt minion from bootstrap scritp, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)
- Package management (install, remove, update...)
- Patching
- Applying any basic salt state (including a formula)
- Salt remote commands
- Bonus point: Java part for product identification, and monitoring enablement
- Bonus point: sumaform enablement (https://github.com/uyuni-project/sumaform)
- Bonus point: Documentation (https://github.com/uyuni-project/uyuni-docs)
- Bonus point: testsuite enablement (https://github.com/uyuni-project/uyuni/tree/master/testsuite)
If something is breaking: we can try to fix it, but the main idea is research how supported it is right now. Beyond that it's up to each project member how much to hack :-)
- If you don't have knowledge about some of the steps: ask the team
- If you still don't know what to do: switch to another distribution and keep testing.
This card is for EVERYONE, not just developers. Seriously! We had people from other teams helping that were not developers, and added support for Debian and new SUSE Linux Enterprise and openSUSE Leap versions :-)
In progress/done for Hack Week 25
Guide
We started writin a Guide: Adding a new client GNU Linux distribution to Uyuni at https://github.com/uyuni-project/uyuni/wiki/Guide:-Adding-a-new-client-GNU-Linux-distribution-to-Uyuni, to make things easier for everyone, specially those not too familiar wht Uyuni or not technical.
openSUSE Leap 16.0
The distribution will all love!
https://en.opensuse.org/openSUSE:Roadmap#DRAFTScheduleforLeap16.0
Curent Status We started last year, it's complete now for Hack Week 25! :-D
[W]Reposync (this will require using spacewalk-common-channels and adding channels to the .ini file) NOTE: Done, client tools for SLMicro6 are using as those for SLE16.0/openSUSE Leap 16.0 are not available yet[W]Onboarding (salt minion from UI, salt minion from bootstrap scritp, and salt-ssh minion) (this will probably require adding OS to the bootstrap repository creator)[W]Package management (install, remove, update...). Works, even reboot requirement detection
Update M2Crypto by mcepl
There are couple of projects I work on, which need my attention and putting them to shape:
Goal for this Hackweek
- Put M2Crypto into better shape (most issues closed, all pull requests processed)
- More fun to learn jujutsu
- Play more with Gemini, how much it help (or not).
- Perhaps, also (just slightly related), help to fix vis to work with LuaJIT, particularly to make vis-lspc working.
Song Search with CLAP by gcolangiuli
Description
Contrastive Language-Audio Pretraining (CLAP) is an open-source library that enables the training of a neural network on both Audio and Text descriptions, making it possible to search for Audio using a Text input. Several pre-trained models for song search are already available on huggingface
Goals
Evaluate how CLAP can be used for song searching and determine which types of queries yield the best results by developing a Minimum Viable Product (MVP) in Python. Based on the results of this MVP, future steps could include:
- Music Tagging;
- Free text search;
- Integration with an LLM (for example, with MCP or the OpenAI API) for music suggestions based on your own library.
The code for this project will be entirely written using AI to better explore and demonstrate AI capabilities.
Result
In this MVP we implemented:
- Async Song Analysis with Clap model
- Free Text Search of the songs
- Similar song search based on vector representation
- Containerised version with web interface
We also documented what went well and what can be improved in the use of AI.
You can have a look at the result here:
Future implementation can be related to performance improvement and stability of the analysis.
References
- CLAP: The main model being researched;
- huggingface: Pre-trained models for CLAP;
- Free Music Archive: Creative Commons songs that can be used for testing;
Bring to Cockpit + System Roles capabilities from YAST by miguelpc
Bring to Cockpit + System Roles features from YAST
Cockpit and System Roles have been added to SLES 16 There are several capabilities in YAST that are not yet present in Cockpit and System Roles We will follow the principle of "automate first, UI later" being System Roles the automation component and Cockpit the UI one.
Goals
The idea is to implement service configuration in System Roles and then add an UI to manage these in Cockpit. For some capabilities it will be required to have an specific Cockpit Module as they will interact with a reasource already configured.
Resources
A plan on capabilities missing and suggested implementation is available here: https://docs.google.com/spreadsheets/d/1ZhX-Ip9MKJNeKSYV3bSZG4Qc5giuY7XSV0U61Ecu9lo/edit
Linux System Roles:
- https://linux-system-roles.github.io/
- https://build.opensuse.org/package/show/openSUSE:Factory/ansible-linux-system-roles Package on sle16 ansible-linux-system-roles
First meeting Hackweek catchup
- Monday, December 1 · 11:00 – 12:00
- Time zone: Europe/Madrid
- Google Meet link: https://meet.google.com/rrc-kqch-hca
Collection and organisation of information about Bulgarian schools by iivanov
Description
To achieve this it will be necessary:
- Collect/download raw data from various government and non-governmental organizations
- Clean up raw data and organise it in some kind database.
- Create tool to make queries easy.
- Or perhaps dump all data into AI and ask questions in natural language.
Goals
By selecting particular school information like this will be provided:
- School scores on national exams.
- School scores from the external evaluations exams.
- School town, municipality and region.
- Employment rate in a town or municipality.
- Average health of the population in the region.
Resources
Some of these are available only in bulgarian.
- https://danybon.com/klasazia
- https://nvoresults.com/index.html
- https://ri.mon.bg/active-institutions
- https://www.nsi.bg/nrnm/ekatte/archive
Results
- Information about all Bulgarian schools with their scores during recent years cleaned and organised into SQL tables
- Information about all Bulgarian villages, cities, municipalities and districts cleaned and organised into SQL tables
- Information about all Bulgarian villages and cities census since beginning of this century cleaned and organised into SQL tables.
- Information about all Bulgarian municipalities about religion, ethnicity cleaned and organised into SQL tables.
- Data successfully loaded to locally running Ollama with help to Vanna.AI
- Seems to be usable.
TODO
- Add more statistical information about municipalities and ....
Code and data
Collection and organisation of information about Bulgarian schools by iivanov
Description
To achieve this it will be necessary:
- Collect/download raw data from various government and non-governmental organizations
- Clean up raw data and organise it in some kind database.
- Create tool to make queries easy.
- Or perhaps dump all data into AI and ask questions in natural language.
Goals
By selecting particular school information like this will be provided:
- School scores on national exams.
- School scores from the external evaluations exams.
- School town, municipality and region.
- Employment rate in a town or municipality.
- Average health of the population in the region.
Resources
Some of these are available only in bulgarian.
- https://danybon.com/klasazia
- https://nvoresults.com/index.html
- https://ri.mon.bg/active-institutions
- https://www.nsi.bg/nrnm/ekatte/archive
Results
- Information about all Bulgarian schools with their scores during recent years cleaned and organised into SQL tables
- Information about all Bulgarian villages, cities, municipalities and districts cleaned and organised into SQL tables
- Information about all Bulgarian villages and cities census since beginning of this century cleaned and organised into SQL tables.
- Information about all Bulgarian municipalities about religion, ethnicity cleaned and organised into SQL tables.
- Data successfully loaded to locally running Ollama with help to Vanna.AI
- Seems to be usable.
TODO
- Add more statistical information about municipalities and ....
Code and data