Code Editor for app.py

import streamlit as st
import pandas as pd
import numpy as np

st.title("Student Enrollment Projection Tool")

# Initialize session state for storing projections
if 'projections' not in st.session_state:
    st.session_state.projections = {}

# Sidebar for input parameters
st.sidebar.header("Enrollment Parameters")

# Current year input
current_year = st.sidebar.number_input("Current Year", min_value=2023, max_value=2050, value=2023)

# Student category inputs
st.sidebar.subheader("Current Student Numbers")
current_ug = st.sidebar.number_input("Current Undergraduate (UG) Students", min_value=0, value=1000)
current_tpg = st.sidebar.number_input("Current Taught Postgraduate (TPG) Students", min_value=0, value=200)
current_rpg = st.sidebar.number_input("Current Research Postgraduate (RPG) Students", min_value=0, value=100)

# Growth rate inputs
st.sidebar.subheader("Expected Annual Growth Rate (%)")
ug_growth = st.sidebar.slider("UG Growth Rate", -20.0, 20.0, 5.0, 0.1)
tpg_growth = st.sidebar.slider("TPG Growth Rate", -20.0, 20.0, 3.0, 0.1)
rpg_growth = st.sidebar.slider("RPG Growth Rate", -20.0, 20.0, 2.0, 0.1)

# Projection years
projection_years = st.sidebar.slider("Number of Years to Project", 1, 10, 5)

# Calculate projections
def calculate_projections(initial, growth_rate, years):
    projections = []
    current = initial
    for _ in range(years + 1):
        projections.append(round(current))
        current *= (1 + growth_rate/100)
    return projections

# Generate year labels
years = list(range(current_year, current_year + projection_years + 1))

# Calculate projections for each category
ug_projections = calculate_projections(current_ug, ug_growth, projection_years)
tpg_projections = calculate_projections(current_tpg, tpg_growth, projection_years)
rpg_projections = calculate_projections(current_rpg, rpg_growth, projection_years)
total_projections = [ug + tpg + rpg for ug, tpg, rpg in zip(ug_projections, tpg_projections, rpg_projections)]

# Create DataFrame for display
df_projections = pd.DataFrame({
    'Year': years,
    'Undergraduate': ug_projections,
    'Taught Postgraduate': tpg_projections,
    'Research Postgraduate': rpg_projections,
    'Total': total_projections
})

# Display results
st.header("Enrollment Projections")

# Display the projection table
st.subheader("Projected Student Numbers")
st.table(df_projections)

# Create a line chart
st.subheader("Projection Trends")
chart_data = df_projections.melt('Year', var_name='Category', value_name='Students')
st.line_chart(data=df_projections.set_index('Year'))

# Summary statistics
st.subheader("Summary")
final_year = current_year + projection_years
total_growth = {
    'UG': ((ug_projections[-1] - ug_projections[0]) / ug_projections[0] * 100),
    'TPG': ((tpg_projections[-1] - tpg_projections[0]) / tpg_projections[0] * 100),
    'RPG': ((rpg_projections[-1] - rpg_projections[0]) / rpg_projections[0] * 100),
    'Total': ((total_projections[-1] - total_projections[0]) / total_projections[0] * 100)
}

st.write(f"By {final_year}:")
col1, col2, col3, col4 = st.columns(4)
with col1:
    st.metric("UG Growth", f"{total_growth['UG']:.1f}%")
with col2:
    st.metric("TPG Growth", f"{total_growth['TPG']:.1f}%")
with col3:
    st.metric("RPG Growth", f"{total_growth['RPG']:.1f}%")
with col4:
    st.metric("Total Growth", f"{total_growth['Total']:.1f}%")

# Download the projections as CSV
csv = df_projections.to_csv(index=False)
st.download_button(
    label="Download Projections as CSV",
    data=csv,
    file_name=f"student_projections_{current_year}-{final_year}.csv",
    mime="text/csv",
)