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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,175 @@ | ||
| 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, course_title): | ||
| # modify the inputs | ||
| subject = subject.upper() | ||
|
|
||
| # import data as csv file | ||
| grades = pd.read_csv("gradedist.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 | ||
| if ((course_number != '') and (course_title == '')): | ||
| internal_grades = grades.loc[(grades.Subject == subject) & (grades.Course_Number == course_number)] | ||
| else: | ||
| internal_grades = grades.loc[(grades.Subject == subject) & (grades.Course_Title == course_title)] | ||
| 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("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('\n') | ||
| j += 1 | ||
| data = {'labels': ['A', 'A-', 'B+', 'B', 'B-', 'C+', 'C', 'C-', 'D+', 'D', 'D-', 'F'], | ||
| 'values': val} | ||
| df = pd.DataFrame(data) | ||
| sns.set_style("whitegrid") | ||
| plt.figure(figsize=(10,10)) | ||
| plt.pie(df['values'], labels=df['labels'], autopct='%1.1f%%') | ||
| plt.title('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:") | ||
| k = 1 | ||
| for k in range(internal_grades.Instructor.size): | ||
| j = k - 1 | ||
| internal_found = False | ||
| while (j >= 0): | ||
| if (internal_grades.iloc[k]['Instructor'] == internal_grades.iloc[j]['Instructor']): | ||
| internal_found = True | ||
| j -= 1 | ||
| if (internal_found != True): | ||
| sortedprofgradedist = sortedprofgradedist + str("\nProfessor " + internal_grades.iloc[k]['Instructor'] + ', GPA: ' + str(internal_grades.iloc[k]['GPA']) + ',') | ||
| if (internal_grades.iloc[k]['GPA'] > GPA): | ||
| sortedprofgradedist = sortedprofgradedist + str(" Above average") | ||
| elif (internal_grades.iloc[k]['GPA'] == GPA): | ||
| sortedprofgradedist = sortedprofgradedist + str(" Average") | ||
| else: | ||
| sortedprofgradedist = sortedprofgradedist + str(" Below average") | ||
| sortedprofgradedist = sortedprofgradedist + str("\nEnrollment: {}".format(internal_grades.iloc[k]['Enrollment']) + ', Withdraws: {}'.format(internal_grades.iloc[k]['Withdraws'])) | ||
| sortedprofgradedist = sortedprofgradedist + str("\nGrade 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] | ||
| # vals = [] | ||
| for letter in (lettergrades): | ||
| i = 0 | ||
| while i < internal_grades.A.size: | ||
| letter = letter + internal_grades.iloc[k][letterchar[j]] | ||
| i += 1 | ||
| letter = letter / internal_grades.A.size | ||
| sortedprofgradedist = sortedprofgradedist + str("\n{}: {:.3}%".format(letterchar[j], letter)) | ||
| # vals.append(letter) | ||
| j += 1 | ||
| sortedprofgradedist = sortedprofgradedist + str('\n') | ||
| # data = {'labels': ['A', 'A-', 'B+', 'B', 'B-', 'C+', 'C', 'C-', 'D+', 'D', 'D-', 'F'], | ||
| # 'values': vals} | ||
| # df = pd.DataFrame(data) | ||
| # sns.set_style("whitegrid") | ||
| # plt.figure(figsize=(10,10)) | ||
| # plt.pie(df['values'], labels=df['labels'], autopct='%1.1f%%') | ||
| # plt.title('Best Grade Distribution for ' + "Professor " + internal_grades.iloc[k]['Instructor'] + "'s class") | ||
| # plt.show() | ||
| k += 1 | ||
|
|
||
| # 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)') + ("\nAverage GPA: {:.3}".format(GPA)) + profswhoteach, gradedistribution, plot, sortedprofgradedist | ||
|
|
||
| # gradio interface | ||
| demo = gr.Interface( | ||
| fn=greet, | ||
| inputs=["text", "number", "text"], | ||
| outputs=[gr.Textbox(label="Course Information"), gr.Textbox(label="Average Grade Distribution", lines=3), gr.Plot(label="Plot"), gr.Textbox(label="Grade Distributions by Professors", lines=3)], | ||
| title="GradeDistVis", | ||
| description="By Gautam Soni, for Virginia Tech students: \n\n A free tool to help you plan your courses based on grade distribution and other metrics", | ||
| article="My github: https://github.com/gsoni1/ My Linkedin: https://www.linkedin.com/in/gsoni16/", | ||
| ) | ||
|
|
||
| demo.launch() |