Re-updated recommender README with instructions.

apps/algorithm/recommend/readme.md

@@ -2,39 +2,32 @@
 
 The recommender is built using TensorFlow, with its API in FastAPI.
 
-## Running Instructions
+## Testing Instructions
 
 1. Clone the repository
 
-2. Navigate into the repository
+2. Navigate into the repository folder.
+
+3. Run `docker-compose up --build`.
 
-3. Run `docker-compose up --build`. This will start the Martletplace application,
+   This will start the Martletplace application,
    spinning up docker containers for all of the different parts. If there are any
    problems with this working, you may not have docker open, or there may be an
-   application currently running on the ports necessary for this application to
-   run.
-
-4. Once the docker containers are all up, you may navigate around the recommender
-   using the API endpoint (in your browser, Postman, or whatever you prefer):
-
-   `GET http://localhost/api/recommendations?authorization=5`
-
-   User 5 is the same user that will be used for the search endpoint, so it's
-   important this user is in use (auth was not fully implemented in this spring). 
-
-   If you would like to see the integration with frontend, you can navigate to
-   the homepage at `http://localhost/`. Once you make an account, you can see
-   the default recommendations. Perform a search using the search bar. Then, navigate back to the
-   homepage, and notice that some of your recommendations have changed. The term
-   you searched may appear in the title of these items, or their descriptions. Next, you can test
-   recommendations based on clicks. If you're looking at the recommendations page and you click on
-   an item, then you can go back to the homepage and see how the recommendations change by showing
-   more of the same item you just clicked on. Finally, you can look at your recommended items, and
-   choose that you're not interested in the given item. If you refresh your homepage, this recommendation
-   will disappear along with "similar" recommendations, assuming the recommendation strength of the
-   your searches and clicks doesn't overpower the similar not interested items, since not interested
-   items are negated whereas searches and clicks are additive. For example, if you search up "banana"
-   many times and click on many banana products, and choose to stop suggesting items similar to a
+   application currently running on the ports necessary for this application to run.
+
+4. Once the docker containers are all up, you can navigate to `localhost` in your
+   browser and create a new account. Be sure to use an email ending in `@uvic.ca`.
+
+   **Note:** Remember to scan the TOTP code in an authenticator app, such as Google Authenticator. 
+
+   Once you register, an email will be sent to you. This link should send to your email to confirm your account (check your junk). If this does not appear, use the confirm email link displayed in your terminal running the application and login.
+
+   Once you make an account, you can see the default recommendations. Perform a search using the search bar. Then, navigate back to the homepage, and notice that some of your recommendations have changed. 
+
+   The term you searched may appear in the title of these items, or their descriptions. Next, you can test recommendations based on clicks. If you're looking at the recommendations page and you click on an item, then you can go back to the homepage and see how the recommendations change by showing more of similar item you just clicked on.
+
+   Finally, you can look at your recommended items, and choose that you're not interested in the given item. If you refresh your homepage, this recommendation
+   will disappear along with "similar" recommendations, assuming the recommendation strength of the your searches and clicks doesn't overpower the similar not interested items, since not interested items are negated whereas searches and clicks are additive. For example, if you search up "banana" many times and click on many banana products, and choose to stop suggesting items similar to a
    banana product, chances are you will still see some banana products. 
 
 ## Testing Instructions