https://github.com/slalit360/fuzzy-word-search
fuzzy-word-search
https://github.com/slalit360/fuzzy-word-search
Last synced: 2 months ago
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fuzzy-word-search
- Host: GitHub
- URL: https://github.com/slalit360/fuzzy-word-search
- Owner: slalit360
- Created: 2019-11-06T04:45:40.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-04-21T20:40:25.000Z (about 2 years ago)
- Last Synced: 2025-01-06T04:12:14.325Z (4 months ago)
- Language: Python
- Size: 2.12 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 2
-
Metadata Files:
- Readme: Readme.md
- Security: SECURITY.md
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README
### Write a HTTP service that provides an endpoint for fuzzy search / autocomplete of English words.
You are given a dataset that contains 333,333 English words and the frequency of their usage in some corpus. A very small sample is shown below:
| track | 112385243|
|---|---|
| australia | 112197265 |
| discussion| 111973466 |
| archive | 111971865 |
| once | 111882023 |
| others | 111397714 |
| entertainment | 111394818 |
| agreement | 111356320 |
| format | 111279626 |
Let us assume we’re building a web app where the user types in a single word from this list in a search box. We wish to autocomplete the input in the search box.
Your objective is to write a Python app using Django framework that exposes a single endpoint:
**GET /search?word=**
where input is the (partial) word that the user has typed so far. For example, if the user is looking up procrastination, the service might receive this sequence of requests:
**GET /search?word=pro**
**GET /search?word=procr**
**GET /search?word=procra**
and so on.
The response should be a JSON array containing upto 25 results, ranked by some criteria (see below).
Constraints
1. Matches can occur anywhere in the string, not just at the beginning. For example, eryx should match archaeopteryx (among others).
2. The ranking of results should satisfy the following:
- [ 2.1 ] We assume that the user is typing the beginning of the word. Thus, matches at the start of a word should be ranked higher. For example, for the input pract, the result practical should be ranked higher than impractical.
- [ 2.2 ] Common words (those with a higher usage count) should rank higher than rare words.
- [ 2.3 ] Short words should rank higher than long words. For example, given the input environ, the result environment should rank higher than environmentalism.
3. As a corollary to the above, an exact match should always be ranked as the first result.
The search algorithm you develop should ideally incorporate some form of a weighted average of all qualifying parameters. The perfect weights, in production systems, are however derived through the use of ML algorithms.
**Steps:**
1. install python and
pip install virtualenv or pipenv
1. cd to project dir and
vitualenv .
1. activate virtualenv
.\Script\activate
1. install django
pip install Django
1. move to django project
cd WordSearchDjango
1. complete django code run django
python manage.py runserver 8080
1. install server
pip install gunicorn
1. django-heroku install
pip install django-heroku
1. save to requirements
pip freeze > requirements.txt
1. create heroku app after installing heroku cli
heroku login
heroku create fuzzy-word-search
1. git setup >
git init
git add . or git add --all
git commit -m "final upload"
heroku git:remote -a fuzzy-word-search
1. deploy and setup
git push heroku master
heroku run bash
python manage.py migrate
python manage.py createsuperuser
1. visit app
https://fuzzy-word-search.herokuapp.com/
project dir tree:-
+ Include
+ Lib
+ Scripts
- WordSearchDjango
- Procfile
- SearchApp/
- templates/
- index.html
- admin.py
- apps.py
- fuzzy.py
- views.py
- models.py
- tests.py
- urls.py
- WordSearchDjango/
- settings.py
- urls.py
- __init__.py
- db.sqlite3
- manage.py
- requirements.txt
- word_search.tsv