Update app.py

app.py

@@ -1,77 +1,125 @@
+# Import necessary libraries
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
 import streamlit as st
-import text2qti
-import traceback
-import subprocess
-import os
-
-def convert_txt_to_qti(file_path):
-    # Use subprocess to run the text2qti command
-    output_file = file_path.replace('.txt', '.zip')
-    try:
-        subprocess.run(["text2qti", file_path], check=True, capture_output=True, text=True)
-        return output_file, None
-    except subprocess.CalledProcessError as e:
-        # Capture the stderr output
-        error_message = e.stderr.strip() if e.stderr else "An error occurred without any specific error message."
-        return None, error_message
-
-def main():
-    st.title("text2qti Converter")
-
-    st.markdown("""
-        For help or more information about how to generate/format .txt files for conversion and upload to Canvas, please visit the [following walk-through guide.](https://reutherlab.biosci.ucsd.edu/)
-    """)
+from scipy.stats import ttest_ind
+from scipy.stats import median_abs_deviation
+
+# Load the gradebook data
+gradebook = pd.read_csv('FAKE_EXAMPLE_DATA.csv')
+
+# Define the assignment groups
+assignment_groups = {
+    "attendance": [column for column in gradebook.columns if 'Attendance' in column],  
+    "study_activities": [column for column in gradebook.columns if 'Study' in column],  
+    "quizzes": [column for column in gradebook.columns if 'Quiz' in column],  
+    "midterms": [column for column in gradebook.columns if 'Midterm' in column],  
+    "final_exam": [column for column in gradebook.columns if 'Final_Exam' in column]
+}
+
+# Add a title to the app
+st.title("Minimizing Inequity in Grading")
+
+# Create sidebar for weightings
+weightings = {group: st.sidebar.slider(f'{group} weighting', min_value=0.000, max_value=1.000, value=0.2, step=0.01) for group in assignment_groups.keys()}
+
+# Create sidebar for dropped scores
+dropped_scores = {group: st.sidebar.slider(f'{group} dropped scores', min_value=0, max_value=6, value=0) for group in assignment_groups.keys()}
+
+# Create sidebar for minimum scores
+minimum_scores = {group: st.sidebar.slider(f'{group} minimum score', min_value=0.0, max_value=1.0, value=0.0, step=0.01) for group in assignment_groups.keys()}
+
+# Define the function for calculating final grades
+def calculate_final_grade(gradebook, assignment_groups, weightings, dropped_scores, minimum_scores):
     
-    uploaded_file = st.file_uploader("Upload a .txt file", type=["txt"])
-
-    if uploaded_file:
-        # Extract the filename without the extension
-        file_name = os.path.splitext(uploaded_file.name)[0]
-
-        # Save the uploaded file to a location with the same name
-        temp_file_path = f"{file_name}.txt"
-        with open(temp_file_path, "wb") as f:
-            f.write(uploaded_file.getbuffer())
-
-        st.write("File uploaded successfully!")
-
-        # Button to initiate the conversion
-        if st.button('Convert to QTI'):
-            try:  
-                qti_zip_file, error = convert_txt_to_qti(temp_file_path)
-                if qti_zip_file and os.path.exists(qti_zip_file):
-                    with open(qti_zip_file, "rb") as f:
-                        zip_data = f.read()
-                    st.download_button(f'Download {file_name}.zip', zip_data, file_name=f'{file_name}.zip', mime='application/zip')
-                else:
-                    # If the conversion failed, display the error message
-                    error_message = (f"""Failed to convert the file. You will see a long, confusing error. **Focus on the part that provides a line number and text from the quiz itself. it will often be near the end of the error message.**  
-                                     ---  
-                                     **Here is an made-up example of what you should look for:**  
-                                     In test_quiz_error.txt on line 23: Syntax error; unexpected text, or incorrect indentation for a wrapped paragraph: #a) Polar heads face outward, nonpolar tails face inward  
-                                     This would indicate that there is an error on line 23 of the quiz. You need to change #a) to \*a) to fix the error.     
-                                     Please visit the walkthrough linked above for more information.  
-                                     ---  
-                    **Your error:**  
-                                     {error}") 
-                    """)
-                    st.markdown(error_message)
-            except Exception as e:
-                # Catch any other exception that might occur and display it using traceback
-                error_traceback = traceback.format_exc()
-                st.error(f"An unexpected error occurred: {error_traceback}") 
-
-
-    # Footer
-    st.markdown("---")
-    st.markdown("""
-        The text2qti python library is Copyright (c) 2020 by Geoffrey M. Poore. 
-        It can be found at [https://github.com/gpoore/text2qti](https://github.com/gpoore/text2qti) 
-        and is distributed under the BSD 3-Clause License.
-    """)
-    st.markdown("""
-        This app is managed by Keefe Reuther - [https://reutherlab.biosci.ucsd.edu/](https://reutherlab.biosci.ucsd.edu/)
-    """)
-
-if __name__ == "__main__":
-    main()
+    # Initialize a DataFrame to store the final grades
+    final_grades = pd.DataFrame()
+    final_grades["ID"] = gradebook["ID"]
+    final_grades["demographic_group"] = gradebook["demographic_group"]
+
+    # Initialize a Series to store the total weights (for normalization)
+    total_weights = pd.Series(0, index=gradebook.index)
+
+    # Loop over each assignment group
+    for group, columns in assignment_groups.items():
+        # Calculate the total score for the group, replacing missing or below-minimum scores with the minimum
+        group_scores = gradebook[columns].apply(pd.to_numeric, errors='coerce')
+        group_scores = group_scores.fillna(minimum_scores[group]).clip(lower=minimum_scores[group])
+
+        # Drop the lowest scores
+        if dropped_scores[group] > 0:
+            group_scores = group_scores.apply(lambda row: row.nlargest(len(row) - dropped_scores[group]), axis=1)
+
+        # Calculate the average score for the group
+        group_averages = group_scores.mean(axis=1)
+
+        # Multiply by the weight and add to the final grades
+        final_grades[group] = group_averages * weightings[group]
+        total_weights += weightings[group]
+
+    # Normalize the final grades by the total weights
+    final_grades["Final_Grade"] = final_grades.drop(columns=["ID", "demographic_group"]).sum(axis=1) / total_weights
+
+    return final_grades
+
+# Calculate final grades
+final_grades = calculate_final_grade(gradebook, assignment_groups, weightings, dropped_scores, minimum_scores)
+
+# Display the final grades
+# st.write(final_grades.head())
+
+# Calculate summary statistics for each group
+summary_statistics = final_grades.groupby("demographic_group")["Final_Grade"].describe()
+
+# Calculate the median for each group
+demo1_median = final_grades[final_grades["demographic_group"] == True]["Final_Grade"].median()
+non_demo1_median = final_grades[final_grades["demographic_group"] == False]["Final_Grade"].median()
+
+# Calculate the difference between the medians
+median_difference = demo1_median - non_demo1_median
+
+# Convert to percentage and round to 2 decimal places
+demo1_median = round(demo1_median * 100, 2)
+non_demo1_median = round(non_demo1_median * 100, 2)
+median_difference = round(median_difference * 100, 2)
+
+# Display the medians
+st.markdown(f"<p style='font-size:28px;'>This data is FAKE and only for the purpose of demonstrating the app.</p>", unsafe_allow_html=True)
+st.markdown(f"<p style='font-size:20px;'>Demographic Group 1 Median Grade: {demo1_median}%</p>", unsafe_allow_html=True)
+st.markdown(f"<p style='font-size:20px;'>Demographic Group 2 Median Grade: {non_demo1_median}%</p>", unsafe_allow_html=True)
+st.markdown(f"<p style='font-size:20px;'>Median Difference: {median_difference}%</p>", unsafe_allow_html=True)
+
+# Plot histograms of final grades for each group
+plt.hist(final_grades.loc[final_grades["demographic_group"] == False, "Final_Grade"], bins=50, histtype='step', label='Demographic Group 2', color='blue')
+plt.hist(final_grades.loc[final_grades["demographic_group"] == True, "Final_Grade"], bins=50, histtype='step', label='Demographic Group 1', color='orange')
+
+# Add labels and legend
+plt.xlabel('Final Grade')
+plt.ylabel('Frequency')
+plt.legend(loc='upper left')
+
+# Display the plot
+st.pyplot(plt)
+
+
+# Calculate MAD for each group
+demo1_mad = median_abs_deviation(final_grades.loc[final_grades["demographic_group"] == True, "Final_Grade"])
+non_demo1_mad = median_abs_deviation(final_grades.loc[final_grades["demographic_group"] == False, "Final_Grade"])
+
+st.write('Demographic Group 1 MAD: ', demo1_mad)
+st.write('Demographic Group 2 MAD: ', non_demo1_mad)
+
+# Display the summary statistics
+st.write(summary_statistics)
+
+# Conduct t-test
+t_stat, p_value = ttest_ind(final_grades[final_grades["demographic_group"] == True]["Final_Grade"], 
+                            final_grades[final_grades["demographic_group"] == False]["Final_Grade"], 
+                            equal_var=False, nan_policy='omit')
+st.write(f"P-value (t-test): {p_value}")
+
+# Calculate Glass's Delta
+non_demo1_std = final_grades.loc[final_grades["demographic_group"] == False, "Final_Grade"].std()
+glass_delta = (final_grades.loc[final_grades["demographic_group"] == True, "Final_Grade"].mean() - final_grades.loc[final_grades["demographic_group"] == False, "Final_Grade"].mean()) / non_demo1_std
+st.write('Glass Delta: ', glass_delta)