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add express app
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‎README.md

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# example-expressjs
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Example PostgresML application using Express JS
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# Express JS PostgresML example
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## Overview
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The purpose of this repository is to demonstrates how to quickly get up and running with machine learning in an Express application.
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This application is a simple note taking application. It analyzes all the notes taken in a day and performs summarization and sentiment analysis on them.
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All our ML occurs in the database where the notes are stored. This means just by connecting to the database we are able to train and deploy ML/AI models and have a production ready pipeline.
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## Setup
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This example application requires a PostgreSQL database with the pgml and pgvector extension installed. The easiest way to do this is with a free database at [postgresml.org](https://postgresml.org/).
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Clone this repository. Once you have your PostgresML database, or local PostgreSQL with the extensions installed, create a .env file and add `DATABASE_URL=<your_db_url>` replacing your_db_url.
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Next install all required node packages with `node install`.
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## Running the app
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Start the express server by running the following
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```bash
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npm run devStart
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```
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This will launch the application at [localhost:3000](http://localhost:3000/)
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## Usage
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In a browser, navigate to localhost:3000. Recored a note in the text area on the left and click submit. Do this a couple times. On the right click Analyze Day, this will produce a sentiment analysis score and a summarization of your day.
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## Code
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We can augment this code to perform all types of ML. Checkout the [postgresML docs](https://postgresml.org/docs/api/sql-extension/) for a full list.
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If you are unhappy with the quality of the results, rest assured, higher quality models are available.
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In this example we directly interacted with the extension using PostgreSQL. If you would prefer JS, see our [JS SDK](https://postgresml.org/docs/api/client-sdk/)

‎assets/css/style.css

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body {
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background-color: #212224;
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color: white;
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}
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h1 {
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color: #fff;
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}
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form, .note-container {
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width: 75%;
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}
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.left-container {
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background-color: #212224;
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}
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.right-container {
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background-color: #17181a;
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}
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.daily-recap-container {
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background-color: #17181a;
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min-width: 30rem;
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}
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.note-date {
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color: #abacb0;
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}
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.btn-primary {
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--bs-btn-padding-x: 1rem;
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--bs-btn-padding-y: 1rem;
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--bs-btn-bg: #5162FF;
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--bs-btn-hover-bg: #7381FF;
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--bs-btn-active-bg: #7381FF;
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--bs-btn-line-height: 16px;
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border: 0px;
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}
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overflow: hidden;
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left: 50%;
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top: 65%;
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position: absolute;
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width: 1329.767px;
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height: 602.685px;
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transform: rotate(-47.563deg);
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flex-shrink: 0;
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border-radius: 1329.767px;
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background: radial-gradient(76.18% 64.48% at 55.97% 35.8%, rgba(255, 152, 214, 0.60) 0%, rgba(26, 6, 255, 0.60) 73.96%);
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filter: blur(112.498291015625px);
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}

‎assets/images/owl_gradient.png

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