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# evaluation.py
import streamlit as st
import pandas as pd
from app5_selectbox.database_con import cursor, db_connection
from app5_selectbox.app5_selectbox_func import display_table, generate_unique_4
from app5_selectbox.evaluation_analysis import eval_analysis
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.express as px
import plotly.graph_objs as go
# Function to perform analytics on instructors
def analyze_instructors(cursor):
try:
# Execute the SQL query to fetch the evaluation data
cursor.execute("SELECT * FROM evaluation")
evaluation_data = cursor.fetchall()
if not evaluation_data:
st.warning("No evaluation data found.")
else:
# Create a DataFrame from the fetched data and set column names
column_names = [i[0].replace("_"," ") for i in cursor.description]
# for i in range(len(column_names)):
# column_names[i] = column_names[i].replace("_"," ")
# st.write(column_names)
# .replace("_"," ")
df = pd.DataFrame(evaluation_data, columns=column_names)
# # Group data by instructor and calculate average scores per criteria
# instructor_avg_scores = df.groupby("inst_id").agg({
# "score_criteria_1": "mean",
# "score_criteria_2": "mean",
# "score_criteria_3": "mean"
# }).reset_index()
# Get the column names from the cursor description
criteria_columns = [f"score_criteria_{i}" for i in range(10)]
column_names = [column[0].replace("_"," ") for column in cursor.description][4:14]
# Print the column names
instructor_avg_scores = df.groupby("inst id")[column_names].mean().reset_index()
# Join with instructor data to get their names
cursor.execute("SELECT inst_id, inst_name FROM instructor")
instructor_data = cursor.fetchall()
instructor_df = pd.DataFrame(instructor_data, columns=["inst id", "instructor name"])
instructor_avg_scores = instructor_avg_scores.merge(instructor_df, on="inst id", how="left")
# Create a select box to filter by instructor
selected_instructor = st.selectbox("Select Instructor", instructor_avg_scores["instructor name"].unique())
# Filter data based on the selected instructor
filtered_data = df[df["inst id"] == instructor_avg_scores[instructor_avg_scores["instructor name"] == selected_instructor]["inst id"].values[0]]
# st.write(filtered_data[filtered_data.columns[4:15]])
# st.write(selected_instructor)
selected_instructor_comments = list(filtered_data["comments"])
# st.write(selected_instructor_comments) #get all comments fro the instructor
st.subheader(f"Evaluated by: {len(selected_instructor_comments)} students")
# Join with the subj_inst and subject tables to get subject names
cursor.execute("""
SELECT subj_inst.subj_inst_id, subject.sub_name
FROM subj_inst
INNER JOIN subject
ON subj_inst.sub_id_code = subject.sub_id_code
""")
subject_data = cursor.fetchall()
subject_df = pd.DataFrame(subject_data, columns=["subj inst id", "sub name"])
filtered_data = filtered_data.merge(subject_df, on="subj inst id", how="left")
# # Group data by subject and calculate average scores per criteria
# subject_avg_scores = filtered_data.groupby("sub_name").agg({
# "score_criteria_1": "mean",
# "score_criteria_2": "mean",
# "score_criteria_3": "mean"
# }).reset_index()
# criteria_columns = [f"score_criteria_{i}" for i in range(10)]
subject_avg_scores = filtered_data.groupby("sub name")[column_names].mean().reset_index()
# # Calculate the total average score for each subject
# subject_avg_scores["total_average"] = subject_avg_scores[["score_criteria_1", "score_criteria_2", "score_criteria_3"]].mean(axis=1)
# criteria_columns = [f"score_criteria_{i}" for i in range(10)]
subject_avg_scores["total average"] = subject_avg_scores[column_names].mean(axis=1)
# Create a grouped bar chart for average scores per criteria by subject
fig = go.Figure()
# for criterion, label in [("score_criteria_1", "Criteria 1"), ("score_criteria_2", "Criteria 2"), ("score_criteria_3", "Criteria 3")]:
# fig.add_trace(go.Bar(
# x=subject_avg_scores["sub_name"],
# y=subject_avg_scores[criterion],
# name=label,
# ))
criteria_labels = [(f"{column_names[i]}", f"{column_names[i]}".replace("_", " ")) for i in range(10)]
for criterion, label in criteria_labels:
fig.add_trace(go.Bar(
x=subject_avg_scores["sub name"],
y=subject_avg_scores[criterion],
name=label,
))
# Add the total average score above the bars
fig.add_trace(go.Scatter(
x=subject_avg_scores["sub name"],
y=subject_avg_scores["total average"],
mode="markers+text",
text=round(subject_avg_scores["total average"],2),
textposition="top center",
textfont=dict(size=14),
marker=dict(size=10, color="black"),
name="Total Average",
))
# Display the overall average of all subjects
overall_average = subject_avg_scores["total average"].mean()
# st.write(f"Overall Average Score (All Subjects): {overall_average:.2f}")
fig.update_layout(
barmode="group",
title=f"Average Scores per Criteria by Subject for Instructor: {selected_instructor}",
xaxis_title=f"Overall Average Score (All Subjects): {overall_average:.2f}",
yaxis_title="Average Score",
)
st.plotly_chart(fig)
return selected_instructor, selected_instructor_comments
except Exception as e:
st.error(f"An error occurred during data analytics: {str(e)}")
# try:
# # Execute the SQL query to fetch the evaluation data
# cursor.execute("SELECT * FROM evaluation")
# evaluation_data = cursor.fetchall()
# if not evaluation_data:
# st.warning("No evaluation data found.")
# else:
# # Create a DataFrame from the fetched data and set column names
# column_names = [i[0] for i in cursor.description]
# df = pd.DataFrame(evaluation_data, columns=column_names)
# # Group data by instructor and calculate average scores per criteria
# instructor_avg_scores = df.groupby("inst_id").agg({
# "score_criteria_1": "mean",
# "score_criteria_2": "mean",
# "score_criteria_3": "mean"
# }).reset_index()
# # Join with instructor data to get their names
# cursor.execute("SELECT inst_id, inst_name FROM instructor")
# instructor_data = cursor.fetchall()
# instructor_df = pd.DataFrame(instructor_data, columns=["inst_id", "instructor_name"])
# instructor_avg_scores = instructor_avg_scores.merge(instructor_df, on="inst_id", how="left")
# # Join with subj_inst and subject tables to get subject names
# cursor.execute("SELECT si.subj_inst_id, s.sub_name FROM subj_inst si INNER JOIN subject s ON si.sub_id_code = s.sub_id_code")
# subject_data = cursor.fetchall()
# subject_df = pd.DataFrame(subject_data, columns=["subj_inst_id", "sub_name"])
# df = df.merge(subject_df, on="subj_inst_id", how="left")
# # Create a select box to filter by instructor and subject
# selected_instructor = st.selectbox("Select Instructor", instructor_avg_scores["instructor_name"].unique())
# selected_subjects = df[df["inst_id"] == instructor_avg_scores[instructor_avg_scores["instructor_name"] == selected_instructor]["inst_id"].values[0]]["sub_name"].unique()
# selected_subject = st.selectbox("Select Subject", selected_subjects)
# # Filter data based on the selected instructor and subject
# filtered_data = df[(df["inst_id"] == instructor_avg_scores[instructor_avg_scores["instructor_name"] == selected_instructor]["inst_id"].values[0]) &
# (df["sub_name"] == selected_subject)]
# # Create a bar chart for average scores per criteria
# fig = px.bar(instructor_avg_scores, x="instructor_name",
# y=["score_criteria_1", "score_criteria_2", "score_criteria_3"],
# labels={"value": "Average Score", "variable": "Criteria"},
# title="Average Scores per Criteria by Instructor")
# st.plotly_chart(fig)
# # Group data by subject instructor and calculate average scores
# subject_avg_scores = filtered_data.groupby("sub_name").agg({
# "score_criteria_1": "mean",
# "score_criteria_2": "mean",
# "score_criteria_3": "mean"
# }).reset_index()
# # Create a bar chart for average scores per criteria for the selected subject
# fig = px.bar(subject_avg_scores, x="sub_name",
# y=["score_criteria_1", "score_criteria_2", "score_criteria_3"],
# labels={"value": "Average Score", "variable": "Criteria"},
# title=f"Average Scores per Criteria for Subject {selected_subject}")
# st.plotly_chart(fig)
# except Exception as e:
# st.error(f"An error occurred during data analytics: {str(e)}")
def evaluation(cursor, table_name):
try:
# Execute the SQL query to fetch the evaluation data
cursor.execute("SELECT * FROM evaluation")
evaluation_data = cursor.fetchall()
if not evaluation_data:
st.warning("No evaluation data found.")
else:
# Create a DataFrame from the fetched data and set column names
column_names = [i[0] for i in cursor.description]
df = pd.DataFrame(evaluation_data, columns=column_names)
# # Display the table with centered text
# st.header(f"{table_name} Table")
# st.dataframe(df.style.set_properties(**{'text-align': 'center'}))
analyze_instructors_results = analyze_instructors(cursor)
if st.button("Analyze comments"):
# st.write(analyze_instructors_results[0], analyze_instructors_results[1])
eval_analysis(analyze_instructors_results[0], analyze_instructors_results[1])
except Exception as e:
st.error(f"An error occurred while fetching evaluation data: {str(e)}")
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