Heads up: this repo is currently a work in progress.

Imagine an evening news cast personalized to your interests...

This application is an experiment with news aggregation and summarization. The goal is to replicate the experience of a newscaster or news team aggregating the most important news items related to a subject (e.g. world news or NASA) and aggregating them into a summary of summaries.

The API has an extractor which pulls raw data from RSS feeds and extracts the content using BeautifulSoup. Articles are then sanitized and keywords are extracted using nltk and gensim. However this method is limited to Python 2.7 and does not yield the kind of results we could get from modern NLP techniques.

This branch has two goals: to implement a modern keyword extraction and topic mapping pipeline using huggingface and tensorflow as well migrating away from AWS to reduce compute costs. To that end content_fixer.py and topic_mapper.py demonstrate the new pipeline using tensorflow. This pipeline is being tested on a Raspberry Pi 4 with a refactor to isolate incompatibility issues with the current environment. The extraction pipeline is being migrated to a Raspberry Pi 3B.

Down the line the idea would be to expose the summarization service to a cloud hosted API for consumption by the frontend, thereby containing costs by isolating the heavy lifting to the on-prem RPis.

Build and Run

API

1. Install and run with conda from the environment.yml file (conda create -n news-feeder -f environment.yml)

Note: For the API you may have to run pip install flask-cors --upgrade for flask_cors to work correctly ([see this issue.] (corydolphin/flask-cors#194))

Vue Frontend

1. Install node modules with yarn.
2. Run the application with yarn serve.

React Frontend

1. Install node modules with yarn.
2. Run the application with yarn start.