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https://github.com/teddarific/djwatson
https://github.com/teddarific/djwatson
Last synced: 14 days ago
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- Host: GitHub
- URL: https://github.com/teddarific/djwatson
- Owner: Teddarific
- Created: 2019-05-24T15:22:56.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2022-12-07T17:34:02.000Z (almost 2 years ago)
- Last Synced: 2023-03-08T09:37:17.050Z (over 1 year ago)
- Language: JavaScript
- Size: 4.22 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 24
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# DJ Watson
Clone the repo with
````
git clone https://github.com/Teddarific/djwatson.git
````
## Client
Change into the client directory,
````
cd client
````
Install all dependencies,
````
yarn
````
To run the client,
````
yarn start
````
The site should now be hosted at localhost:8080
## Server
Change into the server directory,
````
cd server
````
Install all dependencies,
````
yarn
````
To run the server,
````
yarn dev
````
The API should now be hosted at localhost:9090
## Data
To create new environment,
````
virtualenv -p python3 env
````
To activate environment,
````
source env/bin/activate
````
To deactivate environment,
````
deactive
````
To install all dependencies,
````
pip install -r requirements.txt
````
## Training the Net
@Teddy, we need to include the code to get the .csv file.
Label the first column of the .csv file "PAIR_WORKS." Label the rest of the columns "FEATURE 1," FEATURE 2, "FEATURE 3," etc.
Take the .csv and convert it to a JSON using . Take this file, and paste it into a text file named `training.json`. Place this file into the `train` directory. Run `parseJson.py`. There should now be a file called `new_training.json`. Use this JSON as `trainingData` in `training-data.js`.
From the `train` directory, run `python -m SimpleHTTPServer 9000`. Visit on Chrome and inspect the page using Chrome Dev Tools.
Wait for the net to train. It will output a JSON string in the console when it's done. Copy and paste this in `server/app/trainedNet.json`.
Congrats, you've successfully retrained your model.