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()