Student.py

import streamlit as st
import pandas as pd
from streamlit_option_menu import option_menu
# for emoji you can use https://getemoji.com/

st.set_page_config(page_title="Student Result Analysis", layout="wide")

st.title("🎓 Student Result Analysis System")

with st.sidebar:

    st.header("📂 Upload Dataset")
    uploaded_file = st.file_uploader("Upload CSV File", type=["csv"])

    st.markdown("---")

    selected = option_menu(
        menu_title="📌 Menu",
        options=[
            "Raw Data",
            "Student Result",
            "Topper",
            "Search Student",
            "Subject Analysis",
            "Pass / Fail",
            "Pivot Table"
        ],
        icons=[
            "table",
            "bar-chart",
            "trophy",
            "search",
            "book",
            "check-circle",
            "grid"
        ],
        menu_icon="menu-button-wide",
        default_index=0
    )

if uploaded_file is None:
    st.warning("Please upload a CSV file to continue.")
    st.stop()

df = pd.read_csv(uploaded_file)

if selected == "Raw Data":
    st.subheader("📊 Raw Data")
    st.dataframe(df)


elif selected == "Student Result":
    total = df.groupby("Name")["Marks"].sum()
    avg = df.groupby("Name")["Marks"].mean()

    result = pd.DataFrame({
    "Total": total,
    "Average": avg
    }).reset_index()

    st.dataframe(
        result,
        use_container_width=True,
        column_config={
            "Name": st.column_config.TextColumn("Student Name", width="medium"),
            "Total": st.column_config.NumberColumn("Total Marks", width="small"),
            "Average": st.column_config.NumberColumn("Average Marks", width="small")
        }
    )

    # result = pd.DataFrame({
    #     "Total": total,
    #     "Average": avg
    # })

    # st.subheader("📈 Student Performance Summary")
    # st.dataframe(result)


elif selected == "Topper":
    total = df.groupby("Name")["Marks"].sum().sort_values(ascending=False)

    n = st.number_input(
        "How many toppers do you want?",
        min_value=1,
        max_value=len(total),
        value=1
    )

    st.subheader(f"🏆 Top {n} Students")
    st.dataframe(total.head(n))

elif selected == "Search Student":
    st.subheader("🔍 Search Student")

    search_name = st.text_input("Enter Student Name")

    if search_name:

        filtered_df = df[df["Name"].str.lower() == search_name.lower()]

        if not filtered_df.empty:
            st.success(f"Showing results for {search_name}")
            st.dataframe(filtered_df)

            total = filtered_df["Marks"].sum()
            avg = filtered_df["Marks"].mean()

            st.write(f"Total Marks: {total}")
            st.write(f"Average Marks: {avg}")

        else:
            st.error("Student not found!")

elif selected == "Subject Analysis":
    subject_avg = df.groupby("Subject")["Marks"].mean()

    st.subheader("📘 Subject Wise Average")
    st.dataframe(subject_avg)


elif selected == "Pass / Fail":
    pass_marks = st.slider("Select Passing Marks", 0, 100, 40)

    df["Result"] = df["Marks"].apply(
        lambda x: "Pass" if x >= pass_marks else "Fail"
    )

    st.subheader("📌 Result")
    st.dataframe(df)

    st.subheader("📊 Summary")
    st.write(df["Result"].value_counts())
  


elif selected == "Pivot Table":
    pivot = df.pivot_table(
        values="Marks",
        index="Name",
        columns="Subject"
    )

    st.subheader("📑 Student vs Subject")
    st.dataframe(pivot)