The Career Quacker

Chat with an AI assistant to either fill out a questionnaire about your interests, skills, hobbies, etc. or ask it about salaries.

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https://github.com/JainAnnanya/Career-exploration/tree/main

Our Prompt: ADP (AI-Powered Career & Compensation Navigator)

Inspiration

The prompt inspired us to help students navigate potential careers in an easy way. In today’s world, choosing the right career path can be overwhelming. People are often unsure about which career would be the best fit for them, especially when it comes to salary expectations and career growth potential. Our project aims to help users explore different career options, understand salary trends, and make informed decisions based on real-time data.

What it does

We’ve developed an application that offers two main features:

Career Exploration Questionnaire: This feature allows users to answer a series of questions to help identify career paths that best align with their skills, interests, and preferences. It provides personalized career suggestions based on the answers provided by the user.

Salary and Growth Rate Chatbot: In addition to career exploration, we also integrated a chatbot functionality where users can ask questions about different job areas, specifically related to salary information, job responsibilities, and growth potential. The bot gives personalized responses based on real job data, providing salary insights and career outlooks from our database.

How we built it

Our app is built using Flask, a lightweight Python web framework, to handle HTTP requests and manage user interactions, and a job database to create a chatbot that either provides a questionnaire (made in React) or salary information from the database. The application uses natural language processing to analyze user queries and provide salary and growth rate information for various job titles. It pulls relevant data from a database that contains detailed salary information for job roles across different regions. By matching user queries with the closest job title and location, the app gives tailored salary insights, including average salaries, growth rates, and other key details.

The app utilizes the following technologies:

  1. SQLite Database for storing detailed job salary information, growth rates, and location data.
  2. Flask for building the web server and handling requests.
  3. Google Gemini AI for natural language understanding and generating responses.
  4. Fuzzy Matching (using Python's difflib) to match user queries with job titles.

Challenges we ran into

We ran into challenges of quickly trying to learn some technologies like the sqlite3 python package, connecting the database to the bot, and some github merge conflicts.

Accomplishments that we're proud of

We're proud that we accomplished a great feat in a short amount of time. We're also proud that we managed to learn some new technologies in a short amount of time.

What we learned

We learned that AI can assist in providing some coding solutions, but it also helps to get feedback from each other. We each other's help, we were able to make decisions faster. We also learned how to use Google gemini API, manage API responses, use conditional rendering, understand how to structure career exploration logic, and how to work as a team.

What's next for The Career Quacker

We can personalize career paths , recommend jobs, visual career maps, and show users where they are and what they want to be:

  1. Personalized Recommendations: Consider integrating personalized career development tips based on the user’s skills and interests. This could involve suggesting courses or certifications to increase their chances of landing a higher-paying job in their field of interest.

  2. Integration with Job Boards: You could integrate the app with job boards or real-time job listings. This way, users can not only see salary data but also find active job postings related to their career interests.

(link to demo if youtube doesnt work: https://drive.google.com/file/d/1jkCIQjGa7YtBSbd0KPEQvripH4gGzXZl/view?usp=sharing) (link db we used: https://drive.google.com/file/d/1NueA52r9kWh1TSqYxv5rWPQ3TTfYFn6X/view?usp=sharing)

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