courses.py

from typing import List, Dict, Set
import pandas as pd

# Load the Excel file
file_path = 'data/DePaul-University-Courses.xlsx'
df = pd.read_excel(file_path)

# Display the first few rows to understand its structure
df.head()


# Simplified prerequisite parser
# Assuming prerequisites are separated by commas for OR, and spaces for AND
def parse_prerequisites(prerequisites: str) -> List[Set[str]]:
    if pd.isna(prerequisites):
        return []

    # Splitting by commas first to handle OR conditions
    or_conditions = prerequisites.split(',')
    parsed = [set(and_condition.split()) for and_condition in or_conditions]
    return parsed


# Create a mapping of courses to their parsed prerequisites
course_prerequisites = {}
for _, row in df.iterrows():
    course_code = row['Course']
    prerequisites = row['PREREQUISITES']
    course_prerequisites[course_code] = parse_prerequisites(prerequisites)


# Define a function to determine eligible courses
def get_eligible_courses(passed_courses: List[str]) -> List[str]:
    eligible_courses = []
    passed_set = set(passed_courses)

    for course, prerequisites in course_prerequisites.items():
        if not prerequisites:  # No prerequisites means the course is eligible
            eligible_courses.append(course)
        else:
            # Check if all OR conditions are met (at least one set of AND conditions)
            if any(all(prereq in passed_set for prereq in and_conditions) for and_conditions in prerequisites):
                eligible_courses.append(course)

    # Remove courses that the student has already passed
    return [course for course in eligible_courses if course not in passed_set]


# Example usage

passed_courses_example = ['CSC211', 'MAT130', 'SE450']
eligible_courses = get_eligible_courses(passed_courses_example)

print(eligible_courses)