The-AIgent

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An AI tool to connect writers and representation

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# The AIgent v 0.1

## Using AI to connect writers & representation

### Challenge
Today, literary publishing is a $20 billion industry, with a serious bottleneck problem. Successful agents receive on the order of hundreds, if not thousands, of query letters daily. Before they can even sit down to the important task of reviewing a manuscript, hours of the day are devoted to scrolling through endless submission emails, and sorting the wheat from the chaff.

### App Solution
The <a href="http://www.insilicoveritas.net:5000/">AIgent</a> is an app designed to solve this problem, by helping literary agents rapidly identify pitches that are a good match with their current portfolio. It leverages state-of-the-art techniques from ML and NLP to pinpoint the genre a pitch falls into, and automatically contextualizes it in terms of similar titles. In a centralized agency workflow, these techniques could be scaled to route submissions directly to agents that best match the query letter, saving agents (and writers!) time, money, and frustration.

### Repo Overview
* <a href="https://github.com/moralwintertiger/flask-agent-v1/tree/master/templates">App Design</a>
* <a href="https://github.com/moralwintertiger/flask-agent-v1/tree/master/data">Data Overview</a>
* <a href="https://github.com/moralwintertiger/flask-agent-v1/tree/master/similars_model">Technical Overview</a>
* <a href="https://github.com/moralwintertiger/flask-agent-v1/tree/master/analyses">Classifier & Analyses</a>

### Build Workflow
The AIgent was built in a 3-week period as part of an <a href="https://www.insightdatascience.com/">Insight Data Science</a> fellowship.
* Scraped the metadata tags and synopses of >100k GoodReads titles using BeautifulSoup. 
* Leveraged pre-tained embeddings from the state-of-the-art BERT language model. 
* Built out a text classifier and transfer learning pipeline with TensorFlow, sklearn, and Pytorch. 
* Developed a robust classifier, achieving > 85% accuracy and > .9 F-scores, across 20 genres. 
* At inference, identified similar titles on the basis of embedding cosine similarity. 
* Deployed model as a Flask web app, hosted on AWS.

The-AIgent

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