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import streamlit as st
import pandas as pd
import numpy as np
from cryptography.fernet import Fernet
import os
import io
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
# Set page configuration
st.set_page_config(page_title="Student Grade Lookup", page_icon="📚", layout="centered")
# Custom CSS to improve the app's appearance
st.markdown("""
<style>
.reportview-container {
background: #f0f2f6
}
.big-font {
font-size:20px !important;
font-weight: bold;
}
.stAlert > div {
padding-top: 15px;
padding-bottom: 15px;
}
</style>
""", unsafe_allow_html=True)
# Load and decrypt the CSV file
@st.cache_data
def load_data():
encryption_key = os.getenv('ENCRYPTION_KEY')
if not encryption_key:
st.error("Encryption key not found in environment variables.")
return pd.DataFrame()
f = Fernet(encryption_key.encode())
try:
with open('encrypted_grades.csv', 'rb') as file:
encrypted_data = file.read()
decrypted_data = f.decrypt(encrypted_data)
return pd.read_csv(io.StringIO(decrypted_data.decode()))
except Exception as e:
st.error(f"Error decrypting file: {str(e)}")
return pd.DataFrame()
# Main function to run the Streamlit app
def main():
st.title('📚 Student Grade Lookup')
st.markdown("---")
# Load the data
df = load_data()
# Create two columns
col1, col2 = st.columns([2, 1])
with col1:
# Create an input field for the student ID
student_id = st.text_input('Enter Student ID:', placeholder="e.g., C21101100")
with col2:
st.write("")
st.write("")
search_button = st.button('Search', use_container_width=True)
if student_id and search_button:
# Search for the student in the dataframe
student = df[df['ID'].astype(str) == student_id]
if not student.empty:
st.markdown("---")
st.subheader('Student Information:')
# Display student information in a more structured way
col1, col2 = st.columns(2)
with col1:
st.markdown("<p class='big-font'>Name</p>", unsafe_allow_html=True)
st.write(f"{student['NAME'].values[0]}")
st.markdown("<p class='big-font'>ID</p>", unsafe_allow_html=True)
st.write(f"{student_id}")
with col2:
st.markdown("<p class='big-font'>Grade</p>", unsafe_allow_html=True)
# Check if grade is NA
grade = student['GRADE'].values[0]
if pd.isna(grade):
grade_display = "N/A"
else:
grade_display = f"{grade:.2f}"
st.write(grade_display)
st.markdown("<p class='big-font'>Remarks</p>", unsafe_allow_html=True)
remarks = student['REMARKS'].values[0]
if remarks == "Passed":
st.success(remarks)
elif remarks == "Conditional":
st.warning(remarks)
else:
st.write(remarks)
else:
st.error('Student ID not found. Please try again.')
# Display some statistics about the class
st.markdown("---")
st.subheader("Class Statistics")
col1, col2, col3 = st.columns(3)
with col1:
st.metric("Total Students", len(df))
with col2:
avg_grade = df['GRADE'].mean()
st.metric("Average Grade", f"{avg_grade:.2f}")
with col3:
passing_rate = (df['REMARKS'] == 'Passed').mean() * 100
st.metric("Passing Rate", f"{passing_rate:.1f}%")
if __name__ == '__main__':
main()
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