SavvyAI

A student asking “what should I do next?” needs more than a list of job titles. The answer depends on what they are good at, what they want, and which specific skills are missing.

1. 01

   Profile the learner from three directions: stated aspirations, an aptitude test, and their own résumé.

2. 02

   Score aptitude from a graded question bank rather than a single fixed quiz, so the assessment can move with the learner.

3. 03

   Turn the profile into recommendations an LLM can explain, then attach concrete courses to the gaps it finds.

4. 04

   Keep the output usable: a roadmap, a generated résumé, and a profile the learner can return to.

• app.py

  The Flask application: authentication, profile, aptitude flow, chatbot, roadmap and community routes.

• dataset/GAQ/

  The aptitude question bank, filed as Easy, Medium and Hard documents so difficulty can be varied.

• dataset/NPTEL_Course_details/

  Course records used to answer a skill gap with something specific to study.

• resume_parser.py

  Reads an uploaded PDF résumé and extracts education, skills and experience into the profile.

• resume_generator.py

  Writes the profile back out as a formatted résumé, with a choice of templates.

• course_finder.py

  Matches recommended skills against the course dataset.

• templates/

  The screens: aptitude, questions, results, roadmap, suggestions, learning, chatbot, profile and community.