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YouEyeSea displays grade data sourced from UIC’s Office of Institutional Research. To run a local instance with real data, you need to download the official CSVs, place them in the right directory, run the seed script, and then generate the search cache files.

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Get the CSV files

UIC publishes semester-by-semester grade distribution data at: https://oir.uic.edu/data/student-data/grade-distribution/ Download the CSV files for whichever semesters you want to include. Each file covers one semester of grade data.

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Place the files

Put each CSV in the prisma/grade_distribution_data/ directory: 
prisma/
└── grade_distribution_data/
    ├── fall_2023.csv
    ├── spring_2024.csv
    └── fall_2024.csv
The seed script uses the file basename (the filename without the extension) as the semester identifier stored in the database. For example, a file named fall_2024.csv produces records with semester = "fall_2024". Before re-seeding a semester, the script deletes all existing CourseInstance rows for that semester, so re-running it on the same file is safe and idempotent.

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CSV column format

The seed script expects the following columns in each file. These match the headers used in UIC’s official exports:
ColumnDescription
DEPT_CDDepartment code (integer)
DEPT_NAMEDepartment name
CRS_NBRCourse number
CRS_SUBJ_CDCourse subject code (e.g. CS, MATH)
CRS_TITLECourse title
Primary_InstructorInstructor full name
Grade_RegsTotal students registered for a grade
ACount of A grades
BCount of B grades
CCount of C grades
DCount of D grades
FCount of F grades
ADVAdvanced standing credit
CRCredit
DFRDeferred
IIncomplete
NGNo grade
NRNot reported
OOther
PRProgress
SSatisfactory
UUnsatisfactory
WWithdrawn
Rows missing a Primary_Instructor, DEPT_CD, CRS_SUBJ_CD, or CRS_NBR are skipped automatically.

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How the seed script works

The seed script at prisma/seed.ts uses PapaParse to parse each CSV and then upserts records into four Prisma models:
  1. Professor — upserted by name. Each unique Primary_Instructor value becomes one professor record.
  2. Department — upserted by DEPT_CD. Each unique department code becomes one department record.
  3. Course — upserted by the composite key (CRS_SUBJ_CD, CRS_NBR). Each unique subject+number pair becomes one course record linked to its department.
  4. CourseInstance — bulk-inserted at the end. Each row in the CSV (after deduplication of professors, departments, and courses) becomes one course instance record, carrying all 17 grade counts, the total_students value from Grade_Regs, and the semester identifier derived from the filename.
The script deletes existing CourseInstance rows for the same semester before inserting, which lets you re-run it safely after correcting a CSV.

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Run the seed script

npx prisma db seed
Prisma reads the seed command from prisma.config.ts, which runs tsx prisma/seed.ts.
Seeding a large semester CSV (several thousand rows) can take a few minutes. The script logs each completed row to the console so you can track progress.

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Generate the search cache

After seeding, run the cache generator to produce the static TypeScript files that power the fuzzy search: 
npm run generate-cache
This queries the database and writes three files to the project root:
FileContents
courseList.tsxArray of { subject, number, title, professor } objects for every distinct course+professor combination
professorList.tsxMap of professor ID → name
subjectList.tsxArray of distinct subject codes
Re-run npm run generate-cache any time you seed new semesters so that the search index reflects your latest data.

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Next steps

With data loaded, start the development server to see it in the UI:

Development workflow

Run the local dev server and build the app for production.

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