streamlit_app.py

import streamlit as st
from cryptography.fernet import Fernet
import pickle
import pandas as pd
import os
def load_credentials():
    with open("key.key", "rb") as key_file:
        key = key_file.read()
    cipher_suite = Fernet(key)
    with open('credentials_encrypted.pkl', 'rb') as f:
        encrypted_credentials = pickle.load(f)
    return cipher_suite, encrypted_credentials
def login():
    st.sidebar.title("Login")
    cipher_suite, credentials = load_credentials()
    username = st.sidebar.text_input("Username")
    password = st.sidebar.text_input("Password", type='password')
    if username and password:
        if username in credentials:
            decrypted_password = cipher_suite.decrypt(credentials[username].encode()).decode()
            if password == decrypted_password:
                st.session_state.logged_in = True
                st.sidebar.success("Login successful!")
                return True
            else:
                st.sidebar.error("Incorrect password")
        else:
            st.sidebar.error("Username not found")
    return False
BASE_DIR = os.path.dirname(__file__)
DATA_DIR = os.path.join(BASE_DIR, 'Dataset')
def _read_csv(name: str):
    return pd.read_csv(os.path.join(DATA_DIR, name))
def main():
    st.set_page_config(page_title="LMS Dashboard", layout="wide")
    if 'logged_in' not in st.session_state or not st.session_state.logged_in:
        if not login():
            return
    st.title("LMS Dashboard")
    assessments_df = _read_csv('assessments.csv')
    courses_df = _read_csv('courses.csv')
    student_assessment_df = _read_csv('studentAssessment.csv')
    student_info_df = _read_csv('studentInfo.csv')
    student_registration_df = _read_csv('studentRegistration.csv')
    student_vle_df = _read_csv('studentVle.csv')
    vle_df = _read_csv('vle.csv')
    st.sidebar.title("Navigation")
    menu_option = st.sidebar.radio("Go to", ["Student Info", "Assessments", "Courses", "VLE Interactions", "Model Analysis"])
    if menu_option == "Student Info":
        st.header("Student Information")
        student_id = st.selectbox("Select a Student ID to view details:", student_info_df['id_student'].unique())
        student_info = student_info_df[student_info_df['id_student'] == student_id]
        student_registration = student_registration_df[student_registration_df['id_student'] == student_id]
        if not student_info.empty:
            st.subheader("Student Profile")
            col1, col2 = st.columns([1, 3])
            with col1:
                st.image("https://i.imgur.com/NPnaIRmb.jpg", caption="Sample Profile Photo", width=150)
            with col2:
                st.write("### Personal Details")
                st.write(f"**Student ID:** {student_info['id_student'].values[0]}")
                st.write(f"**Gender:** {student_info['gender'].values[0]}")
                st.write(f"**Region:** {student_info['region'].values[0]}")
                st.write(f"**Age Band:** {student_info['age_band'].values[0]}")
                st.write(f"**IMD Band (Socio-Economic Status):** {student_info['imd_band'].values[0]}")
                st.write(f"**Highest Education:** {student_info['highest_education'].values[0]}")
                st.write(f"**Disability:** {'Yes' if student_info['disability'].values[0] == 'Y' else 'No'}")
            st.subheader("Registration Details")
            st.dataframe(student_registration)
        else:
            st.warning("No data available for the selected student ID.")
    elif menu_option == "Assessments":
        st.header("Assessments Overview")
        st.dataframe(assessments_df.head())
        module = st.selectbox("Select a module:", assessments_df['code_module'].unique())
        presentation = st.selectbox("Select a presentation:", assessments_df[assessments_df['code_module'] == module]['code_presentation'].unique())
        filtered_assessments = assessments_df[(assessments_df['code_module'] == module) & (assessments_df['code_presentation'] == presentation)]
        st.subheader(f"Assessments for {module} ({presentation})")
        st.dataframe(filtered_assessments)
    elif menu_option == "Courses":
        st.header("Course Information")
        st.dataframe(courses_df)
        selected_course = st.selectbox("Choose a course module:", courses_df['code_module'].unique())
        course_data = courses_df[courses_df['code_module'] == selected_course]
        if not course_data.empty:
            st.subheader(f"Course Details: {selected_course}")
            st.dataframe(course_data)
    elif menu_option == "VLE Interactions":
        st.header("VLE (Virtual Learning Environment) Interactions")
        vle_student_id = st.selectbox("Select Student ID for VLE data:", student_vle_df['id_student'].unique())
        if vle_student_id:
            student_vle_data = student_vle_df[student_vle_df['id_student'] == vle_student_id]
            if not student_vle_data.empty:
                st.subheader(f"VLE Data for Student ID: {vle_student_id}")
                st.dataframe(student_vle_data)
                vle_clicks_summary = student_vle_data.groupby('code_module')['sum_click'].sum().reset_index()
                st.subheader("Total Clicks per Module")
                st.bar_chart(vle_clicks_summary.set_index('code_module')['sum_click'])
                student_vle_data = student_vle_data.merge(vle_df[['id_site', 'code_module', 'activity_type']], on=['id_site', 'code_module'])
                resource_clicks = student_vle_data.groupby('activity_type')['sum_click'].sum().sort_values(ascending=False)
                st.subheader("Resource Type Engagement (Click Totals)")
                st.bar_chart(resource_clicks)
            else:
                st.warning("No VLE data found for that student ID.")
        st.subheader("Overall VLE Usage")
        overall_vle_summary = student_vle_df.groupby('id_site')['sum_click'].sum().reset_index()
        top_vle_resources = overall_vle_summary.sort_values(by='sum_click', ascending=False).head(10)
        st.subheader("Top VLE Resources by Clicks (Overall)")
        st.bar_chart(top_vle_resources.set_index('id_site')['sum_click'])
    elif menu_option == "Model Analysis":
        st.header("Model Analysis")
        st.subheader("Confusion Matrix Analysis")
        st.image(os.path.join(BASE_DIR, 'confusion_matrix_analysis.png'), caption="Confusion Matrix Analysis")
        st.subheader("Prediction Distribution")
        st.image(os.path.join(BASE_DIR, 'prediction_distribution.png'), caption="Prediction Distribution")
        st.subheader("Feature Importance")
        st.image(os.path.join(BASE_DIR, 'feature_importance.png'), caption="Feature Importance")
        st.subheader("Confusion Matrix - Random Forest")
        st.image(os.path.join(BASE_DIR, 'confusion_matrix_Random Forest.png'), caption="Confusion Matrix - Random Forest Model")
        st.subheader("Confusion Matrix - XGBoost")
        st.image(os.path.join(BASE_DIR, 'confusion_matrix_XGBoost.png'), caption="Confusion Matrix - XGBoost Model")
if __name__ == "__main__":
    main()