Good Job!

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  Good Job!

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  App's main page

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  Behind the scenes

Inspiration

Job searching can be challenging, especially when skills don’t perfectly match job requirements. Studies show that men apply even when they meet 60% of requirements, while women often wait until they meet nearly 100%. This app aims to reduce this confidence gap, promote equal opportunities, and encourage women to upskill.

What it does

The app analyses job seeker skills against job descriptions, providing a match percentage and AI-driven encouragement to boost confidence.

How we built it

We used Streamlit with Python for frontend and backend development, integrating AI for skill matching and analysis.

Challenges we ran into

Streamlit’s simplicity restricted advanced features, impacting user experience—such as the inability to remove displayed text after actions. Prompt engineering was also challenging, with responses being sometimes unpredictable or inconsistently formatted.

Accomplishments that we're proud of

We successfully built a system that supports file uploads and deletions, processes job descriptions, and provides a matching score and analysis using Gemini Flash.

What we learned

We gained experience using Streamlit for frontend development, setting AI model parameters to control output quality, and refining prompt engineering for more consistent and accurate responses.

What's next for 'Good Job'!

Future plans include enhanced security, including scraping CVs to remove personal data, using cloud storage for secure data handling, adding features such as conversational chat and history and matching comparison with another role, and further prompt engineering to improve response stability.

Built With

• gemini
• python
• streamlit