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| import streamlit as st | |
| import firebase_admin | |
| from firebase_admin import credentials, auth, db | |
| import pandas as pd | |
| import plotly.express as px | |
| # Initialize Firebase | |
| try: | |
| if not firebase_admin._apps: | |
| cred = credentials.Certificate("serviceAccountKey.json") | |
| firebase_admin.initialize_app(cred, { | |
| 'databaseURL': 'https://transacapp-22b6e-default-rtdb.firebaseio.com/' | |
| }) | |
| except Exception as e: | |
| st.error(f"Error initializing Firebase: {str(e)}") | |
| st.stop() | |
| def login(): | |
| st.title("📊 Financial Dashboard Login") | |
| with st.form("login_form"): | |
| email = st.text_input("Email") | |
| password = st.text_input("Password", type="password") | |
| submit_button = st.form_submit_button("Login") | |
| if submit_button: | |
| try: | |
| user = auth.get_user_by_email(email) | |
| st.session_state['user'] = user | |
| st.success("Logged in successfully!") | |
| st.rerun() # Use st.rerun() instead of st.experimental_rerun() | |
| except Exception as e: | |
| st.error("Invalid credentials. Please try again.") | |
| return submit_button | |
| return False | |
| def get_user_transactions(uid): | |
| try: | |
| transactions = [] | |
| # Get all financial messages for the user | |
| ref = db.reference(f'financialMessages/{uid}') | |
| financial_data = ref.get() | |
| if financial_data: | |
| for month, month_data in financial_data.items(): | |
| if isinstance(month_data, dict): | |
| for trans_id, trans_data in month_data.items(): | |
| if isinstance(trans_data, dict): | |
| trans_data['month'] = month | |
| # Convert amount to float if it's a string | |
| if 'amount' in trans_data: | |
| try: | |
| trans_data['amount'] = float(str(trans_data['amount']).replace(',', '')) | |
| except ValueError: | |
| trans_data['amount'] = 0.0 | |
| transactions.append(trans_data) | |
| df = pd.DataFrame(transactions) | |
| return df if not df.empty else pd.DataFrame(columns=[ | |
| 'accountNumber', 'amount', 'personName', 'referenceNo', | |
| 'transactionDate', 'transactionType', 'month' | |
| ]) | |
| except Exception as e: | |
| st.error(f"Error fetching transactions: {str(e)}") | |
| return pd.DataFrame() | |
| def main(): | |
| st.set_page_config(page_title="Financial Dashboard", page_icon="💰", layout="wide") | |
| if 'user' not in st.session_state: | |
| if not login(): | |
| return | |
| st.title("💰 Financial Dashboard") | |
| # Add logout button | |
| if st.sidebar.button("Logout"): | |
| st.session_state.pop('user', None) | |
| st.rerun() # Updated line | |
| # Get user data | |
| user = st.session_state['user'] | |
| df = get_user_transactions(user.uid) | |
| if df.empty: | |
| st.warning("No transactions found for this account.") | |
| return | |
| # Date filters | |
| st.sidebar.header("📅 Filters") | |
| months = sorted(df['month'].unique()) | |
| selected_months = st.sidebar.multiselect("Select Months", months, default=months) | |
| # Transaction type filter with "All" option | |
| transaction_types = sorted(df['transactionType'].unique()) | |
| transaction_types.insert(0, "All") # Add "All" option at the beginning | |
| selected_types = st.sidebar.multiselect("Transaction Types", transaction_types, default=transaction_types[1:]) | |
| # Filter DataFrame to only include selected months | |
| filtered_by_month_df = df[df['month'].isin(selected_months)] | |
| # Date filter based on the filtered DataFrame | |
| filtered_by_month_df['transactionDate'] = pd.to_datetime(filtered_by_month_df['transactionDate']) # Ensure transactionDate is in datetime format | |
| dates = filtered_by_month_df['transactionDate'].dt.date.unique() # Get unique dates from filtered data | |
| selected_dates = st.sidebar.multiselect("Select Dates", options=dates, default=dates) | |
| # Final data filtering based on selected months, types, and dates | |
| if "All" in selected_types: | |
| filtered_df = filtered_by_month_df[ | |
| filtered_by_month_df['transactionDate'].dt.date.isin(selected_dates) | |
| ] | |
| else: | |
| filtered_df = filtered_by_month_df[ | |
| (filtered_by_month_df['transactionType'].isin(selected_types)) & | |
| (filtered_by_month_df['transactionDate'].dt.date.isin(selected_dates)) | |
| ] | |
| # Dashboard layout | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.metric("Total Transactions", len(filtered_df)) | |
| with col2: | |
| debited = filtered_df[filtered_df['transactionType'] == 'debited']['amount'].sum() | |
| credited = filtered_df[filtered_df['transactionType'] == 'credited']['amount'].sum() | |
| net_amount = credited - debited | |
| st.metric("Net Amount", f"₹{net_amount:,.2f}") | |
| with col3: | |
| avg_amount = filtered_df['amount'].mean() | |
| st.metric("Average Transaction", f"₹{avg_amount:,.2f}") | |
| # Visualizations | |
| st.subheader("📊 Transaction Analysis") | |
| # Create two columns for charts | |
| chart_col1, chart_col2 = st.columns(2) | |
| with chart_col1: | |
| # Monthly transaction summary | |
| monthly_summary = filtered_df.groupby(['month', 'transactionType']).agg({ | |
| 'amount': 'sum' | |
| }).reset_index() | |
| fig1 = px.bar(monthly_summary, x='month', y='amount', color='transactionType', | |
| title='Monthly Transaction Volume by Type', | |
| labels={'amount': 'Amount (₹)', 'month': 'Month'}, | |
| barmode='group') | |
| st.plotly_chart(fig1, use_container_width=True) | |
| with chart_col2: | |
| # Transaction type distribution | |
| fig2 = px.pie(filtered_df, values='amount', names='transactionType', | |
| title='Transaction Amount Distribution by Type') | |
| st.plotly_chart(fig2, use_container_width=True) | |
| # Transaction Trends | |
| fig3 = px.line(monthly_summary, x='month', y='amount', color='transactionType', | |
| title='Transaction Trends Over Time', | |
| labels={'amount': 'Amount (₹)', 'month': 'Month'}) | |
| st.plotly_chart(fig3, use_container_width=True) | |
| # Detailed transaction table | |
| st.subheader("📝 Recent Transactions") | |
| # Format the dataframe for display | |
| display_df = filtered_df.copy() | |
| display_df['amount'] = display_df['amount'].apply(lambda x: f"₹{x:,.2f}") | |
| display_df = display_df.sort_values('transactionDate', ascending=False) | |
| # Display as a styled dataframe | |
| st.dataframe( | |
| display_df, | |
| column_config={ | |
| "transactionDate": "Date", | |
| "transactionType": "Type", | |
| "amount": "Amount", | |
| "personName": "Person", | |
| "referenceNo": "Reference", | |
| "accountNumber": "Account" | |
| }, | |
| hide_index=True | |
| ) | |
| # Add download button for CSV | |
| if st.button("Download Transaction Data"): | |
| csv = filtered_df.to_csv(index=False) | |
| st.download_button( | |
| label="Download CSV", | |
| data=csv, | |
| file_name="transactions.csv", | |
| mime="text/csv" | |
| ) | |
| if __name__ == "__main__": | |
| main() | |