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import streamlit as st
import pandas as pd
import firebase_admin
from firebase_admin import credentials, firestore
import matplotlib.pyplot as plt
import seaborn as sns
# Path to Firebase credentials file
cred = credentials.Certificate('firebase_credentials/your-credentials-file.json')
firebase_admin.initialize_app(cred)
# Connect to Firestore
db = firestore.client()
# Collection name for expenses
expenses_ref = db.collection('expenses')
# Function to add an expense to Firebase
def add_expense(date, category, description, amount):
expenses_ref.add({
'Date': date,
'Category': category,
'Description': description,
'Amount': amount
})
# Initialize the UI
st.title("Daily Expense Tracker")
# Add expense form
with st.form(key="expense_form"):
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")
submit_button = st.form_submit_button(label="Add Expense")
if submit_button:
add_expense(date, category, description, amount)
st.success(f"Added expense: {description} - ${amount:.2f}")
# Retrieve expenses from Firebase
expenses_query = expenses_ref.stream()
# Store expenses in a DataFrame
expenses_data = []
for expense in expenses_query:
expenses_data.append(expense.to_dict())
if expenses_data:
expenses_df = pd.DataFrame(expenses_data)
else:
expenses_df = pd.DataFrame(columns=['Date', 'Category', 'Description', 'Amount'])
# Display expenses list
st.subheader("Expenses List")
st.write(expenses_df)
# Generate summary by category
category_summary = expenses_df.groupby('Category')['Amount'].sum().reset_index()
st.subheader("Category Summary")
st.write(category_summary)
# Visualize the expenses by category (Bar Chart)
st.subheader("Expense Visualization (Bar Chart)")
fig, ax = plt.subplots(figsize=(10, 6))
sns.barplot(x='Category', y='Amount', data=category_summary, ax=ax)
ax.set_title('Total Expenses by Category')
ax.set_xlabel('Category')
ax.set_ylabel('Amount Spent ($)')
ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
st.pyplot(fig)
# Pie chart for category-wise expenses
st.subheader("Expense Distribution (Pie Chart)")
fig2, ax2 = plt.subplots(figsize=(8, 8))
category_summary.set_index('Category')['Amount'].plot(kind='pie', autopct='%1.1f%%', legend=False, ax=ax2)
ax2.set_title('Expense Distribution by Category')
ax2.set_ylabel('') # Hide ylabel
st.pyplot(fig2)
# Option to save the expenses to a CSV file
st.download_button(
label="Download Expenses as CSV",
data=expenses_df.to_csv(index=False),
file_name="expenses.csv",
mime="text/csv"
)
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