# Hangry back-end

### To-Do List

<ul>
<li>User login authentication</li>
<li>Secure endpoints</li>
<li>User favorites</li>
<li>User dislikes</li>
</ul>

## Other possible additions:
<ul>
<li>User accounts: Allow users to create accounts so they can save their favorite restaurants or cuisines, share their recommendations with friends, or receive personalized recommendations based on their past behavior.</li>

<li>Save favorites: Allow users to save their favorite restaurants to a list within the app so they can easily refer back to them later.</li>

<li>Randomize food categories: Instead of just randomizing restaurants, you could allow users to choose from different food categories such as Italian, Mexican, or Chinese, and then randomize within that category.</li>

<li>Include filters for price or distance: Add filters to allow users to limit the results to restaurants within a certain price range or within a certain distance from their location.</li>

<li>Include a map view: Add a map view that shows the user's location and the location of the selected restaurant</li>

<li>Share on social media: Include a sharing feature that allows users to share their random restaurant selection on social media platforms such as Facebook, Twitter, or Instagram.</li>

<li>Reviews: Include a simple rating system that allows users to rate restaurants based on their experience.</li>

<li>Add images: Include images of the restaurant to give users a better idea of what to expect.</li>

<li>Reviews and ratings: Allow users to leave reviews and ratings for restaurants, and display the overall rating for each restaurant to help other users make informed decisions.</li>

<li>Filtering and sorting options: Allow users to filter restaurants based on criteria like price, distance, cuisine, or dietary restrictions, and sort them by rating or proximity.</li>

<li>Social sharing: Enable users to share their random restaurant picks on social media or messaging apps, and provide them with a referral system to earn rewards or discounts for inviting their friends to use the app.</li>

<li>Personalization: Use machine learning algorithms to analyze user behavior and provide personalized recommendations based on their preferences, location, time of day, or other factors.</li>
</ul>
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