> ## Documentation Index
> Fetch the complete documentation index at: https://genai.codewithsiva.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Streamlit APP-1

> Student Performance Analytics Dashboard

In this capstone, you will build a complete Streamlit application by
combining all the concepts covered in this chapter. Follow the steps
below and run the application after completing each step.

***

## Step 1: Create the Project

Create a new project folder.

```text theme={null}
student_dashboard/
│
├── app.py
├── students.csv
└── requirements.txt
```

Install Streamlit.

```bash theme={null}
pip install streamlit pandas numpy matplotlib
```

Run the application.

```bash theme={null}
streamlit run app.py
```

***

## Step 2: Create the Basic Application

```python theme={null}
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.")
```

***

## Step 3: Create the Sidebar

```python theme={null}
st.sidebar.title("Filters")

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

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

***

## Step 4: Upload a CSV File

```python theme={null}
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.")
```

***

## Step 5: Display Dataset Information

```python theme={null}
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))
```

***

## Step 6: Create Filters

```python theme={null}
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")
```

***

## Step 7: Filter the Data

```python theme={null}
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)
```

***

## Step 8: Use Session State

```python theme={null}
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"
)
```

***

## Step 9: Organize the Layout

```python theme={null}
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")
```

***

## Step 10: Display Summary Report

```python theme={null}
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))
```

***

## Step 11: Create Charts

```python theme={null}
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)
```

## Grade Distribution

```python theme={null}
fig, ax = plt.subplots()

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

st.pyplot(fig)
```

***

## Step 12: Add Progress Indicators

```python theme={null}
import time

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

st.success("Report Generated Successfully")
```

***

## Step 13: Download the Report

```python theme={null}
csv = filtered_df.to_csv(index=False)

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

***

## Step 14: Final Dashboard Layout

```text theme={null}
------------------------------------------------------
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
```

***

## Sample Dataset (`students.csv`)

```csv theme={null}
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
```

***

## Concepts Covered

* Streamlit application structure
* Streamlit execution flow
* Displaying text and DataFrames
* Sidebar
* User input widgets
* Forms
* Columns and containers
* Tabs
* Session State
* Status messages
* Pandas for CSV handling
* NumPy for basic calculations
* Matplotlib visualizations
* Interactive filtering
* Dashboard development
* Report download