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A NLP driven script which will give you a quote according to the sentence you feed it. πŸ’¬ It pulls data from several API's and makes up a relation by f.e. sentiment of the sentenceπŸ’«

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grumpyp/NLP-quote-maker

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NLP-quote-maker

A NLP driven script which will give you a quote according to the sentence you feed it. It pulls data from several API's and makes up a relation by f.e. sentiment of the sentence.

Start-up

To run the app

  • Clone this repository git clone [email protected]:grumpyp/NLP-quote-maker.git
  • Setup virtual environment if required python -m venv venv
  • Install requirements.txt pip install -r requirements.txt
  • Run app with python app\main.py

⭐ Current features & Interface

  • NLTK SentimentIntensityAnalyzer was used to gpositive/neutral/negative rating scores and normalized into a composite score.
  • Entity labelling with spacy was used to attach one or more classes to each quote.
  • A text box is provided for user input where text is assessed, rated, matched to existing quotes within a quote database, and outputted with associated rating score.

πŸ‘Ύ Repo Setup

Main branches containing all relevant files for use/development are found in the deployment and development branch respectively. Proposed changes should be done and pulled on development branch first prior to merge with depolyment.

app
β”œβ”€β”€ src
β”‚   β”œβ”€β”€ db.py
β”‚   └── functions.py
β”œβ”€β”€ static
β”‚   β”œβ”€β”€ css
β”‚   β”‚   └── style.css
β”‚   └── js
β”‚       └── main.js
β”œβ”€β”€ templates
β”‚   β”œβ”€β”€ index.html
β”‚   └── layout.html
β”œβ”€β”€ config.py
β”œβ”€β”€ main.py   
└── views.py 

Support & Contributing

Feature Requests? Please file an issue

I am happy to see all kind of contributions!

ToDo

  • Setup Flask environment
  • Deployment on Heroku
  • Build frontend with dynamic search field
  • Find API's for quotes
  • Use Twitter / Reddit API
  • Find features to do a rating of the sentence
  • Find suitable NLP libraries
  • Write inital readme

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A NLP driven script which will give you a quote according to the sentence you feed it. πŸ’¬ It pulls data from several API's and makes up a relation by f.e. sentiment of the sentenceπŸ’«

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