This AI model evaluates anonymized TinkerHub campus lead applications based on predefined criteria and guidelines.
- Automated scoring of applications (1-10 scale)
- Structured feedback generation
- Analysis of both structured and text-based fields
- Alignment with TinkerHub's values and requirements
- Clone this repository
- Install dependencies:
pip install -r requirements.txt
from evaluator import evaluate_application
# Example application
application = {
"role": "Student",
"experience": "Led coding club activities",
"current_year": "Year 2",
"vision_for_campus_community": "Create inclusive tech learning space",
"challenges_and_solutions": "Addressing accessibility issues",
"belief_in_tinkerhub": 5,
"planned_programs": "Monthly hackathons",
"projects_completed": "Built campus portal",
"how_they_know_tinkerhub": "WhatsApp",
"willing_to_travel": "Yes",
"read_wiki": "Yes",
"agreement_to_not_lead_elsewhere": "Yes"
}
# Evaluate the application
result = evaluate_application(application)
print(result)
The evaluator returns a dictionary with three fields:
Your Score (1-10)
: Numerical score based on the scoring guidelinesPositive Feedback
: Brief feedback highlighting strengths (<50 words)Areas of Concern
: Constructive feedback on potential areas for improvement (10-50 words)
The model considers:
- Academic eligibility (Year 1-3)
- Leadership experience
- Community-first mindset
- Project completion
- Program planning
- Alignment with TinkerHub values
- Travel willingness
- Preparation (wiki reading)
- Commitment (agreement to not lead elsewhere)
- 1-3: Does not meet requirements
- 4-6: Meets basic requirements
- 7-8: Strong candidate
- 9-10: Exceptional candidate
- All applications are processed anonymously
- The model is designed to be objective and consistent
- Final selection is made through interviews by TinkerHub Foundation