Inspiration

When we were brainstorming ideas, we discussed things that we like and what interconnectivity means to us. Food was a topic that we can all relate to, we all love food!

What it does

Our BCSFoodBot takes any user's mention of the bot with a given food and location, then replies them back with a suggested location to try.

How we built it

For this project, we used the Tweepy API, the Google Search API, and Python to build our bot. We constantly updated our list to check for any new mentions on our BCSFoodBot page. For every new mention we took the submitted tweet’s food and location and we query the text object using the Google API and returned the top result as a URL. We then parsed the Tweet for the user’s Twitter handle and returned the recommendation to the original tweet using Tweepy’s API function calls. We updated our status to post a reply tweet to the user with their Twitter handle and the recommended food Twitter page attached.

Challenges we ran into

Location of the tweet, the user must have this turn on in their setting. If the user doesn’t have this setting turned on then we cannot suggest a local restaurant.

Accomplishments that we're proud of

Many of us did not have much Python experience prior to this project, but we were still able to learn from each other and gain valuable insight. In particular, learning how to use APIs such as Tweepy and Google Search were completely new experiences for many of us, and we learnt a lot more in how to take these systems forward with other projects.

What we learned

  1. Python Language
  2. Twitter and Google Search APIs
  3. Coding Together in Groups

What's next for BCSFoodBot - Live Twitter Restaurant Recommendations!

  1. Alternative suggestions beyond the first result (e.g. 2nd top searched)
  2. Providing an image of said recommendation, alongside the URL
  3. Add data persistence preferences for each twitter user

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