Streamlit APP-1

student_dashboard/
│
├── app.py
├── students.csv
└── requirements.txt

pip install streamlit pandas numpy matplotlib

streamlit run app.py

import streamlit as st

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

st.title("📊 Student Performance Analytics Dashboard")
st.write("Upload a CSV file to analyze student performance.")

st.sidebar.title("Filters")

department = st.sidebar.selectbox(
    "Department",
    ["All", "CSE", "ECE", "MECH"]
)

semester = st.sidebar.selectbox(
    "Semester",
    ["All", 1, 2, 3, 4]
)

import pandas as pd

uploaded_file = st.file_uploader(
    "Upload Student CSV",
    type="csv"
)

if uploaded_file:
    df = pd.read_csv(uploaded_file)

    st.success("File uploaded successfully.")
    st.dataframe(df)
else:
    st.info("Please upload a CSV file.")

if uploaded_file:

    col1, col2, col3 = st.columns(3)

    col1.metric("Rows", len(df))
    col2.metric("Columns", len(df.columns))
    col3.metric("Average Marks", round(df["Marks"].mean(), 2))

if uploaded_file:

    with st.sidebar.form("filter_form"):

        dept = st.selectbox(
            "Department",
            ["All"] + sorted(df["Department"].unique().tolist())
        )

        subject = st.selectbox(
            "Subject",
            ["All"] + sorted(df["Subject"].unique().tolist())
        )

        search = st.text_input("Search Student")

        submit = st.form_submit_button("Apply Filters")

filtered_df = df.copy()

if dept != "All":
    filtered_df = filtered_df[
        filtered_df["Department"] == dept
    ]

if subject != "All":
    filtered_df = filtered_df[
        filtered_df["Subject"] == subject
    ]

if search:
    filtered_df = filtered_df[
        filtered_df["Name"].str.contains(search, case=False)
    ]

st.dataframe(filtered_df)

if "visits" not in st.session_state:
    st.session_state.visits = 0

st.session_state.visits += 1

st.sidebar.write(
    f"Dashboard Opened: {st.session_state.visits} times"
)

tab1, tab2, tab3 = st.tabs(
    [
        "Dataset",
        "Reports",
        "Charts"
    ]
)

with tab1:
    st.dataframe(filtered_df)

with tab2:
    st.write("Summary Report")

with tab3:
    st.write("Visualizations")

col1, col2, col3, col4 = st.columns(4)

col1.metric("Students", len(filtered_df))
col2.metric("Highest", filtered_df["Marks"].max())
col3.metric("Lowest", filtered_df["Marks"].min())
col4.metric("Average", round(filtered_df["Marks"].mean(), 2))

import matplotlib.pyplot as plt

fig, ax = plt.subplots()

filtered_df.groupby("Department")["Marks"].mean().plot(
    kind="bar",
    ax=ax
)

ax.set_ylabel("Average Marks")

st.pyplot(fig)

fig, ax = plt.subplots()

filtered_df["Marks"].plot(
    kind="hist",
    bins=10,
    ax=ax
)

st.pyplot(fig)

import time

with st.spinner("Generating report..."):
    time.sleep(2)

st.success("Report Generated Successfully")

csv = filtered_df.to_csv(index=False)

st.download_button(
    label="Download Filtered CSV",
    data=csv,
    file_name="students_report.csv",
    mime="text/csv"
)

------------------------------------------------------
Student Performance Analytics Dashboard
------------------------------------------------------

Sidebar
    • Upload CSV
    • Department Filter
    • Subject Filter
    • Search Student

KPIs
Students | Average | Highest | Lowest

Tabs
• Dataset
• Reports
• Charts

Charts
• Average Marks by Department
• Grade Distribution
• Subject-wise Performance

Download Report

RollNo,Name,Department,Semester,Subject,Marks
101,Sai Kiran,CSE,4,Python,88
102,Sravani Reddy,CSE,4,Python,75
103,Venkatesh Kumar,ECE,4,Python,91
104,Harika Devi,CSE,4,AI,82
105,Naveen Kumar,ECE,4,AI,95
106,Keerthana,MECH,3,Python,69
107,Sandeep Reddy,CSE,3,AI,90
108,Lakshmi Priya,ECE,2,Maths,74
109,Rohith Varma,CSE,4,Maths,86
110,Bhavya Sri,MECH,2,Physics,72