Spaces:
Running
Running
File size: 9,451 Bytes
7372d1d 7b3d91b 84d3413 3d3e316 eb295f0 5fc1fc9 7372d1d 5fc1fc9 7372d1d c93f217 7372d1d 5fc1fc9 eb295f0 7372d1d 48c9f76 7372d1d 48c9f76 7372d1d 3d3e316 7372d1d 3d3e316 7372d1d 84d3413 7372d1d 3d3e316 a2d8ceb 3d3e316 48c9f76 3d3e316 7372d1d 48c9f76 5fc1fc9 48c9f76 7372d1d 5fc1fc9 da93b97 7372d1d 62ad90c 48c9f76 7372d1d 5fc1fc9 7372d1d eb295f0 48c9f76 da93b97 75aca9c 48c9f76 fcbc6fa cdede07 48c9f76 da93b97 7105268 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 |
import gradio as gr
import pandas as pd
pd.plotting.register_matplotlib_converters()
import matplotlib.pyplot as plt
import seaborn as sns
def greet(subject, course_number):
# modify the inputs
subject = subject.upper()
# import data as csv file
grades = pd.read_csv("gradedist2025.csv")
grades = grades.rename(columns={'Course No.': 'Course_Number',
'Course Title': 'Course_Title',
'Graded Enrollment': 'Enrollment',
'A (%)': 'A',
'A- (%)': 'A-',
'B+ (%)': 'B+',
'B (%)': 'B',
'B- (%)': 'B-',
'C+ (%)': 'C+',
'C (%)': 'C',
'C- (%)': 'C-',
'D+ (%)': 'D+',
'D (%)': 'D',
'D- (%)': 'D-',
'F (%)': 'F'})
grades = grades.sort_values(by='Course_Number')
internal_grades = grades
# user interface
while True:
# filter the csv file by the input subject and course number/title
internal_grades = grades.loc[(grades.Subject == subject) & (grades.Course_Number == course_number)]
internal_grades = internal_grades.sort_values(by='GPA', ascending=False)
# class information label
print('')
print( subject + ' ' + str(internal_grades.iloc[0]['Course_Number']) + ' - ' + internal_grades.iloc[0]['Course_Title'] + ' (' + str(internal_grades.iloc[0]['Credits']) + ' Credits)')
# gpa calculation
print('')
GPA = 0.00
i = 0
while i < internal_grades.GPA.size:
GPA = GPA + internal_grades.iloc[i]['GPA']
i += 1
GPA = GPA / internal_grades.GPA.size
print("Course Average GPA: {:.3}".format(GPA))
gradedistribution = ''
print('')
# print("Grade distribution:")
A = 0
Aminus = 0
Bplus = 0
B = 0
Bminus = 0
Cplus = 0
C = 0
Cminus = 0
Dplus = 0
D = 0
Dminus = 0
F = 0
j = 0
letterchar = ['A', 'A-', 'B+', 'B', 'B-', 'C+', 'C', 'C-', 'D+', 'D', 'D-', 'F']
lettergrades = [A, Aminus, Bplus, B, Bminus, Cplus, C, Cminus, Dplus, D, Dminus, F]
val = []
for letter in (lettergrades):
i = 0
while i < internal_grades.GPA.size:
letter = letter + internal_grades.iloc[i][letterchar[j]]
i += 1
letter = letter / internal_grades.GPA.size
val.append(letter)
gradedistribution = gradedistribution + str("{}: {:.3}%".format(letterchar[j], letter))
gradedistribution = gradedistribution + str(', ')
j += 1
data = {'labels': ['A', 'A-', 'B+', 'B', 'B-', 'C+', 'C', 'C-', 'D+', 'D', 'D-', 'F'],
'values': val}
df = pd.DataFrame(data)
# plt.style.use('dark_background')
sns.set_style("whitegrid")
plt.figure(figsize=(10,10))
plt.pie(df['values'], labels=df['labels'], autopct='%1.1f%%', textprops={'fontsize': 20})
plt.title('Course Average Grade Distribution for ' + subject + ' ' + str(internal_grades.iloc[0]['Course_Number']) + ' - ' + internal_grades.iloc[0]['Course_Title'])
#plt.show()
plot = plt
# sort professors
sortedprofgradedist = ''
print('')
print("Top Professors by GPA:")
profnames = internal_grades.Instructor.unique()
profaveragegpa = []
profdict = {}
for prof in profnames:
internal_prof = internal_grades.loc[internal_grades.Instructor == prof]
GPAinternal = 0.00
i = 0
while i < internal_prof.GPA.size:
GPAinternal = GPAinternal + internal_prof.iloc[i]['GPA']
i += 1
GPAinternal = GPAinternal / internal_prof.GPA.size
profaveragegpa.append(GPAinternal)
profdict[prof] = GPAinternal
# sort profs by highest avg gpa
sortedprofs = {key: val for key, val in sorted(profdict.items(), key = lambda ele: ele[1], reverse = True)}
print(sortedprofs)
profkeys = sortedprofs.keys()
for key in profkeys:
internal_prof = internal_grades.loc[internal_grades.Instructor == key]
enrollment = 0
withdraws = 0
A = 0
Aminus = 0
Bplus = 0
B = 0
Bminus = 0
Cplus = 0
C = 0
Cminus = 0
Dplus = 0
D = 0
Dminus = 0
F = 0
i = 0
while i < internal_prof.GPA.size:
A = A + internal_prof.iloc[i]['A']
Aminus = Aminus + internal_prof.iloc[i]['A-']
Bplus = Bplus + internal_prof.iloc[i]['B+']
B = B + internal_prof.iloc[i]['B']
Bminus = Bminus + internal_prof.iloc[i]['B-']
Cplus = Cplus + internal_prof.iloc[i]['C+']
C = C + internal_prof.iloc[i]['C']
Cminus = Cminus + internal_prof.iloc[i]['C-']
Dplus = Dplus + internal_prof.iloc[i]['D+']
D = D + internal_prof.iloc[i]['D']
Dminus = Dminus + internal_prof.iloc[i]['D-']
F = F + internal_prof.iloc[i]['F']
enrollment = enrollment + internal_prof.iloc[i]['Enrollment']
withdraws = withdraws + internal_prof.iloc[i]['Withdraws']
i += 1
A = A / internal_prof.GPA.size
Aminus = Aminus / internal_prof.GPA.size
Bplus = Bplus / internal_prof.GPA.size
B = B / internal_prof.GPA.size
Bminus = Bminus / internal_prof.GPA.size
Cplus = Cplus / internal_prof.GPA.size
C = C / internal_prof.GPA.size
Cminus = Cminus / internal_prof.GPA.size
Dplus = Dplus / internal_prof.GPA.size
D = D / internal_prof.GPA.size
Dminus = Dminus / internal_prof.GPA.size
F = F / internal_prof.GPA.size
enrollment = enrollment / internal_prof.GPA.size
withdraws = withdraws / internal_prof.GPA.size
sortedprofgradedist = sortedprofgradedist + str('\nProfessor ' + key + ", Avg GPA: {:.3}".format(sortedprofs.get(key)))
if (sortedprofs.get(key) > GPA):
sortedprofgradedist = sortedprofgradedist + str(", Above average")
elif (sortedprofs.get(key) == GPA):
sortedprofgradedist = sortedprofgradedist + str(", Average")
else:
sortedprofgradedist = sortedprofgradedist + str(", Below average")
# print('')
sortedprofgradedist = sortedprofgradedist + str("\nEnrollment: {:.0f}".format(enrollment) + ', Withdraws: {:.0f}'.format(withdraws))
sortedprofgradedist = sortedprofgradedist + str("\nAvg Distribution: ")
letterchar = ['A', 'A-', 'B+', 'B', 'B-', 'C+', 'C', 'C-', 'D+', 'D', 'D-', 'F']
lettergrades = [A, Aminus, Bplus, B, Bminus, Cplus, C, Cminus, Dplus, D, Dminus, F]
j = 0
for letter in lettergrades:
sortedprofgradedist = sortedprofgradedist + str("{}: {:.3}%".format(letterchar[j], letter))
sortedprofgradedist = sortedprofgradedist + str(', ')
j += 1
sortedprofgradedist = sortedprofgradedist + str('\n')
# professors who teach the class
profswhoteach = ''
profswhoteach = profswhoteach + str('\n')
profswhoteach = profswhoteach + str("Professors who teach {}:".format(internal_grades.iloc[0]['Course_Title']))
profswhoteach = profswhoteach + str('\n')
profswhoteach = profswhoteach + str(internal_grades.Instructor.unique())
# enrollment and withdraws
print('')
return (subject + ' ' + str(internal_grades.iloc[0]['Course_Number']) + ' - ' + internal_grades.iloc[0]['Course_Title'] + ' (' + str(internal_grades.iloc[0]['Credits']) + ' Credits)') + ("\nCourse Average GPA: {:.3}".format(GPA)) + profswhoteach, gradedistribution, plot, sortedprofgradedist
# gradio interface
demo = gr.Interface(
fn=greet,
inputs=[gr.Textbox(label="Subject (Eg. ECON, chem)"), gr.Number(label="Course Number (Eg. 2005)")],
outputs=[gr.Textbox(label="Course Information"), gr.Textbox(label="Course Average Grade Distribution", lines=2), gr.Plot(label="Course Average Grade Distribution Plot"), gr.Textbox(label="Average Grade Distributions by Professors", lines=3)],
title="GradeDistVis",
description="📚 Plan your courses like a PRO with GradeDistVis! 🎓✨ Access grade distributions and other key metrics to maximize your GPA!🔥 \n\n For Virginia Tech students,",
article="Feedback Form: https://forms.gle/G4zV25XH8tDYLsRW9",
examples = [["ECON", 2005], ["chem", 1036]],
cache_examples=True,
).queue(default_concurrency_limit=1000)
demo.launch() |