Food Finder

1. We were inspired to encourage people to cook for themselves and lead more healthy lifestyles. Many people are often discouraged from cooking because of how time consuming it may be or because they don’t know where to start, even if they have health goals that they want to achieve. To solve this issue, we wanted to build a website that allows users to find relevant recipes by inputting the ingredients they have access to and a calorie goal they have in mind for the specific meal. Furthermore, recipes have a difficulty level to invite users with a wide range of cooking experience.

2. Food finder works by taking in important ingredients that users want to cook with along with their calorie goal for a meal. Based on those goals, we create an index (0-1) for how closely it matches their caloric needs and their desired ingredient usage. It combines those two factors to create an overall index for the result and we suggest the best recipes out of the 200k available. We provide summaries of each recipe in a way that is easy to read and scroll so that users can choose their favorite ones.

3. We built our project by taking an online data source of a csv file that contained several recipes as well as their ingredients, nutritional facts, difficulty, and other important information. From there, we used SQL to parse the csv data into a database in Pycharm and manipulated the data to fit our needs. Through Flask and HTML, we displayed our data in an organized fashion where the user can see the recipes that fit their needs the best.

4. One challenge we ran into was how some members had little experience working with Python. We overcame this by having them observe the code the others wrote and learn from it. As the project progressed, they learned and contributed to the code. Another challenge we ran into was working with data frames as none of us had a lot of experience working with it. We were able to use resources to find out how they worked and implemented what we learned into our code.

5. One accomplishment we were proud of was being able to make our project thorough. Because of how expansive our database was and how restrictive our search algorithms were, we provided results that were very relevant to what the user was searching for.

6. We learned how to incorporate existing data sets into our own program. We also learned how to reformat this data to make it of use to us, and how to then manipulate this data in many different ways, helping us to establish many of the nuances of our product. We learned a lot about front end development and the importance of establishing aesthetics for a finished program. We also utilized data frames a good amount throughout our backend programming, which is certainly a new skill that we adopted. The most novel part for us, though, was exploring the communication between the front and back end of development, and specifically how to utilize HTML when dealing with a predominately python environment.

7. We want to expand food finder to make smarter searches based on other criteria that users may want, such as pricing, cuisine, tags, and micronutrients. This would give more filtered and accurate results out of the over 200k recipes. We would also like to add more interactive features such as saving recipes and reserving them for particular days in the future to plan out a cooking schedule.

Built With

• csv
• flask
• html
• pandas
• python
• sqlite