app.py

import streamlit as st
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Initialize the DataFrame in the session state if it doesn't exist
if 'expenses_df' not in st.session_state:
    columns = ['Date', 'Category', 'Description', 'Amount']
    st.session_state.expenses_df = pd.DataFrame(columns=columns)

# Streamlit app layout
st.title('Expense Tracker')

# Add new expense entry
st.subheader("Add New Expense")

date = st.date_input("Date")
category = st.selectbox("Category", ['Food', 'Transport', 'Entertainment', 'Other'])
description = st.text_input("Description")
amount = st.number_input("Amount", min_value=0.0, format="%.2f")

if st.button("Add Expense"):
    new_expense = pd.DataFrame([[date, category, description, amount]], columns=st.session_state.expenses_df.columns)
    st.session_state.expenses_df = pd.concat([st.session_state.expenses_df, new_expense], ignore_index=True)
    st.success(f"Added expense: {description} - ${amount:.2f}")

# Display all expenses
st.subheader("Expenses List")
st.write(st.session_state.expenses_df)

# Ensure 'Amount' is numeric (convert if necessary)
st.session_state.expenses_df['Amount'] = pd.to_numeric(st.session_state.expenses_df['Amount'], errors='coerce')

# Show category summary
st.subheader("Category Summary")
category_summary = st.session_state.expenses_df.groupby('Category')['Amount'].sum().reset_index()
st.write(category_summary)

# Generate individual bar and pie charts per Category (grouped by Description)
st.subheader("Bar Charts and Pie Charts per Category")

# Get all unique categories
unique_categories = st.session_state.expenses_df['Category'].unique()

# Loop through each unique category to plot bar and pie charts by description
for category in unique_categories:
    category_df = st.session_state.expenses_df[st.session_state.expenses_df['Category'] == category]

    # Bar chart: Expense distribution by description for this category
    fig, ax = plt.subplots(figsize=(10, 6))
    sns.barplot(x='Description', y='Amount', data=category_df, ax=ax)
    ax.set_title(f'Total Expenses for Category: {category}')
    ax.set_xlabel('Description')
    ax.set_ylabel('Amount Spent ($)')
    ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
    st.pyplot(fig)

    # Pie chart: Expense distribution by description for this category
    fig, ax = plt.subplots(figsize=(8, 8))
    description_summary = category_df.groupby('Description')['Amount'].sum().reset_index()
    description_summary.set_index('Description')['Amount'].plot(kind='pie', autopct='%1.1f%%', legend=False, ax=ax)
    ax.set_title(f'Expense Distribution for Category: {category}')
    ax.set_ylabel('')  # Hide ylabel
    st.pyplot(fig)

# Create overall bar and pie chart for all categories

# Bar chart for total expenses by category
st.subheader("Overall Expenses by Category")
fig, ax = plt.subplots(figsize=(10, 6))
sns.barplot(x='Category', y='Amount', data=category_summary, ax=ax)
ax.set_title('Total Expenses by Category (Overall)')
ax.set_xlabel('Category')
ax.set_ylabel('Amount Spent ($)')
ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
st.pyplot(fig)

# Pie chart for expense distribution by category
st.subheader("Overall Expense Distribution")
fig, ax = plt.subplots(figsize=(8, 8))
category_summary.set_index('Category')['Amount'].plot(kind='pie', autopct='%1.1f%%', legend=False, ax=ax)
ax.set_title('Expense Distribution by Category (Overall)')
ax.set_ylabel('')  # Hide ylabel
st.pyplot(fig)

# Save the expenses to a CSV file
st.download_button(
    label="Download Expenses as CSV",
    data=st.session_state.expenses_df.to_csv(index=False),
    file_name="expenses.csv",
    mime="text/csv"
)