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app.py

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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()

encrypted_grades.csv

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+gAAAAABnCqYeNNU_F-av2AD6lolJiVFxBvK7Q1udCgyrR3m_ioDyTDE2SebzLgdBdvFiJRWXFtszDaOtrZh25LfdD5ruHV57qRpNQKO3D7XtiVz3xMdnsTgE14Ex9ibNk8lfTR-vYsdhnwlRBAGuXgilrwErEwD-BrnfGGgh__sUoFsRN_V2dnU0TGjgUBKDTZV0EK6nV5D05YXz5FVNJtdsXVgzV0th3L2HE6qCtX9J6IIDSGpHpnZIh82zjeR2BlqPBFC9rHEmxn-O8dK1nHad9Q-sJF8bed5oi17j4w_4omhCjG44TbF-beM-aXJrQMeDFJmNoDA3JrkgdDVzcYpy2ImE4RSisqqRR19Wzl92mW4HAY-QPAopa3wHaEow3ujQey3LNujtN2nnpi5PbKqixs96nniDe3if3esXKqB9unKLsXDowaP98_6VPgbMP07XY8-rSdrZGhSCGCuxLhtQXww_YcC_ogXinXmscZpB18AZ6rYMKicBcaT1BT_TwqaXe-LSfwvE75RVOlVqDUEr_h7BufgwPK8M5zYIU1AitM_WQsvDY7gSzXKbaQUV_aLzvxc6WmwZtEfWdPipua2qqJJaWZrSbsd0GiLc_ZWurcvX4H9O2g1yz3xB03jhQvvX8cTUyNxAXIPDhaaBsYVcgYL3Q7-ZmUenRFAHj3fC01zuTctmHwDw9Y_hZtou0mbRmnHD4wrycUPrHwUEHe6jJ16ZDbO4aUBPIH0iMG8hBi-6rJcLoUtS5nfmpyuqJqznN-1J9IGgLU2UTROVZXnrzKNEMq-wl6ygAmbgwvcGvcx7yNGp0oBi3szUYbw2LoeTWaUDDhu43fUKsbBV3QXOGrE1G5Ooi4Jvat6gWyKSni03xzEo-COq4rKIAZNjN_E08zFNDTRiPbDPg_rugySHaBRksq5jC1lqbBlly2Cvdz7uJiJYeYsCfzv9FUj2HmdgX2BQipYUr6am9r0MbgJqEXmmNpLOY2nsG65bGsRsJt0cw4qvNcexAN2QyBhyD1QEVG_r1J2hlkovCPtfpmgEpZzz4jKPlDOVO53CIlmxPmvuRMJPMrW3WUYVOsrTTOQIlLZTmF7ZcwrLyQjAimjFPcCnsIqclyULZwfhMn3QgRHLXTyZImQhHWfuo-_d74_KGf1hOQ4Sor-yAckbI8-YB_Gy-en2hgrlBdr0MhnfvY86tZzFe-XtAZOKnXERaOCbV9crI2c95onYpuMH_yhyfo7ITj_suKiUD_nmmNcpNlxsTzz8vLSoMMbEXaO7_IoFt9gg4eN7940CfgDcniyAB8xhScypzuMXCS4EnUs3oRsWqON8zIsEwLR0wTPulxYIXNeDTfUrh7ClZSKSjV0PZ8m3AO4MuHIIc5oKLfwR_gaoLmqel_aCqegoECaUe2_xBS5TyYGK1zq7re6QP98wCOHSqvl9Xn2Ho_bSRoFbtX6HHmstWelxz5jaRJQZWTLOBqF7Oa9wO9feqVaFQL2O0Ew3yU9Y49Ho5iUpgi94Wb0TPRv5IgJ5MYaPEwPOxP1U5SGXS2RZoWfJ5qClYdMz6jZjAV_GIfwjcOdJPFk4NcR2NHJHcbr44dWkTHxARf9Nda4qPPe5f64fJtg7GTZ75j0NQO6XTEBgO7tQ4qJEhpASkhZ1l8irpm20XdsDPy8ErF1JcAEOMyJwyV3Z9eBRVQdPaRkPqf0qUJZYvJIbRTg_TZx7oCjMygS0d82dwRWirS5rw-NzJM6RNdLYFydDYfoQEsr-kQdOjuSHN-JLAmkvLNlvsA73jxKDDtZFhZFilvp6_FMPYyl-uSD7j02w_l9kuO7fKH5U1kMemarqylZ5fnexRgkARDYnUdtSdFddcrpknh75UFYkeMKbdmU0a0xzJNIqMC6Hhhf7rtFfH9w5aK2mQXw8teRq0hv5HSnH0j_HVyNdH8WgNkgqsIM36OkcjqwKa_SaXUA2IhInqH7FeZBNzUEAEmhTb8616bjQNsF2NByZgXauCk24os2LDXHseDmBW99-sNC_FDCSZhU6fSY98oEH-zylTGsRW50xw_Vr7FPhj3bW1tvy_mvZ7XnGGFeGiLUKX-AJgvgRA3Q87vf7SwnqdMtvsYbka0O2yOxSdxBXRAvyD15_m7r9iWl7-ufX1N0Lw191w1xVNUjgJEzYC7x5ZkbkrTaW-UToVcTyV3OglfXR24cTs9bi0bjZQVvFvNpz8lYMgrG4hfYVjxrLrUVM-MNVY-cd3G1tyDX10bmt0kl98ZPnAbKuil8mCH5ayJDektrm5ZX8pP9XoR3Hgh06ItRZo_jmfGg_HcMASibYdxcTUZ49fU-HGgDEXReQcMTNyJpRO0T4ehpfzYZHSzE8dz7b28-mtfg-bOYwlaK_n8w8s0In_61ssH9xn-CkhUty4nansJE3m7p4Ar2JiFSJut_k8938wV6gCni5ZyVCi48f29qg5CGcn6K3cKc8bkCc8kgBoMRVUSQQ9IXpwKl1hV49GbST3Xd9a9sUbo2DvDjrXwnGkTeBki3vlkkJ6EqzbY8gwBIhQtjTTZlFioofHGZ1mqy6J49VZCgmNpkBh6p4swXCk4-t9VMLY3HtTs7MChIhbLyho_W7WLUTUcWIdVF5JOiMJeHDFhQA5xJO

fernet_key.txt

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+3vfbK_1k3_c2qxBdz244VphBiEx0db3cG_kg0If4Ois=