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| import streamlit as st | |
| import firebase_admin | |
| from firebase_admin import credentials, db | |
| import pandas as pd | |
| import plotly.express as px | |
| from datetime import datetime | |
| # Initialize Firebase Realtime Database | |
| try: | |
| app = firebase_admin.get_app() | |
| except ValueError: | |
| cred = credentials.Certificate("serviceAccountKey.json") | |
| app = firebase_admin.initialize_app(cred, { | |
| 'databaseURL': 'https://transacapp-22b6e-default-rtdb.firebaseio.com/' | |
| }) | |
| def fetch_usernames(): | |
| """Fetch list of all usernames from Firebase""" | |
| try: | |
| ref = db.reference('financialMessages') | |
| users = ref.get() | |
| if users: | |
| return list(users.keys()) | |
| return [] | |
| except Exception as e: | |
| st.error(f"Error fetching usernames: {str(e)}") | |
| return [] | |
| def fetch_user_transactions(username, selected_month): | |
| """Fetch financial messages for a specific user and month from Firebase""" | |
| try: | |
| ref = db.reference(f'financialMessages/{username}/{selected_month}') | |
| transactions = ref.get() | |
| if not transactions: | |
| return [] | |
| messages = [] | |
| for transaction_id, data in transactions.items(): | |
| if isinstance(data, dict): | |
| messages.append({ | |
| 'Person Name': data.get('personName', ''), | |
| 'Account Number': data.get('accountNumber', ''), | |
| 'Amount': float(data.get('amount', 0)), | |
| 'Reference No': data.get('referenceNo', ''), | |
| 'Transaction Date': data.get('transactionDate', ''), | |
| 'Transaction Type': data.get('transactionType', '') | |
| }) | |
| return messages | |
| except Exception as e: | |
| st.error(f"Error fetching data: {str(e)}") | |
| return [] | |
| def create_transaction_distribution_chart(df): | |
| """Create an enhanced transaction distribution visualization with multiple chart types""" | |
| # Calculate transaction type summaries | |
| type_summary = df.groupby('Transaction Type').agg({ | |
| 'Person Name': 'count', | |
| 'Amount': ['sum', 'mean', 'min', 'max'] | |
| }).round(2) | |
| type_summary.columns = ['Count', 'Total Amount', 'Average Amount', 'Min Amount', 'Max Amount'] | |
| type_summary = type_summary.reset_index() | |
| # Create bar chart comparing transaction counts and amounts | |
| fig_comparison = px.bar( | |
| type_summary, | |
| x='Transaction Type', | |
| y=['Count', 'Total Amount'], | |
| barmode='group', | |
| title='Transaction Comparison by Type', | |
| labels={'value': 'Value', 'variable': 'Metric'}, | |
| color_discrete_sequence=['#4C78A8', '#72B7B2'], | |
| template='plotly_white' | |
| ) | |
| fig_comparison.update_layout( | |
| xaxis_title="Transaction Type", | |
| yaxis_title="Value", | |
| legend_title="Metric", | |
| height=400, | |
| showlegend=True, | |
| legend=dict( | |
| orientation="h", | |
| yanchor="bottom", | |
| y=1.02, | |
| xanchor="right", | |
| x=1 | |
| ) | |
| ) | |
| # Create detailed metrics visualization | |
| fig_metrics = px.bar( | |
| type_summary.melt( | |
| id_vars=['Transaction Type'], | |
| value_vars=['Average Amount', 'Min Amount', 'Max Amount'] | |
| ), | |
| x='Transaction Type', | |
| y='value', | |
| color='variable', | |
| barmode='group', | |
| title='Transaction Amount Metrics by Type', | |
| labels={'value': 'Amount (₹)', 'variable': 'Metric'}, | |
| color_discrete_sequence=['#FF9DA7', '#9C755F', '#BAB0AC'], | |
| template='plotly_white' | |
| ) | |
| fig_metrics.update_layout( | |
| xaxis_title="Transaction Type", | |
| yaxis_title="Amount (₹)", | |
| height=400, | |
| showlegend=True, | |
| legend=dict( | |
| orientation="h", | |
| yanchor="bottom", | |
| y=1.02, | |
| xanchor="right", | |
| x=1 | |
| ) | |
| ) | |
| # Add hover information | |
| for fig in [fig_comparison, fig_metrics]: | |
| fig.update_traces( | |
| hovertemplate="<br>".join([ | |
| "Transaction Type: %{x}", | |
| "Value: %{y:,.2f}", | |
| "<extra></extra>" | |
| ]) | |
| ) | |
| return fig_comparison, fig_metrics | |
| def main(): | |
| st.set_page_config(page_title="Financial Transactions Dashboard", layout="wide") | |
| # Header | |
| st.title("Financial Transactions Dashboard") | |
| st.markdown("---") | |
| # Sidebar filters | |
| st.sidebar.header("Filters") | |
| # Username dropdown | |
| usernames = fetch_usernames() | |
| username = st.sidebar.selectbox( | |
| "Select Username", | |
| options=usernames if usernames else ["No users found"] | |
| ) | |
| # Month selection | |
| months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"] | |
| selected_month = st.sidebar.selectbox("Select Month", options=months) | |
| if username and username != "No users found": | |
| # Fetch data | |
| data = fetch_user_transactions(username, selected_month) | |
| if data: | |
| df = pd.DataFrame(data) | |
| df['Amount'] = pd.to_numeric(df['Amount']) | |
| # Transaction type dropdown | |
| transaction_type = st.sidebar.selectbox( | |
| "Select Transaction Type", | |
| options=["All", "debited", "credited"] | |
| ) | |
| # Date filter | |
| dates = df['Transaction Date'].unique() | |
| selected_dates = st.sidebar.multiselect( | |
| "Select Dates", | |
| options=dates, | |
| default=dates | |
| ) | |
| # Apply filters | |
| if transaction_type != "All": | |
| masked_df = df[ | |
| (df['Transaction Type'] == transaction_type) & | |
| (df['Transaction Date'].isin(selected_dates)) | |
| ] | |
| else: | |
| masked_df = df[df['Transaction Date'].isin(selected_dates)] | |
| # Dashboard metrics | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.metric("Total Transactions", len(masked_df)) | |
| with col2: | |
| total_debited = masked_df[masked_df['Transaction Type'] == 'debited']['Amount'].sum() | |
| st.metric("Total Debited", f"₹ {total_debited:,.2f}") | |
| with col3: | |
| total_credited = masked_df[masked_df['Transaction Type'] == 'credited']['Amount'].sum() | |
| st.metric("Total Credited", f"₹ {total_credited:,.2f}") | |
| # Transactions table | |
| st.subheader("Recent Transactions") | |
| st.dataframe( | |
| masked_df, | |
| column_config={ | |
| "Amount": st.column_config.NumberColumn( | |
| "Amount", | |
| format="₹ %.2f" | |
| ) | |
| }, | |
| hide_index=True | |
| ) | |
| # Create transaction distribution visualizations | |
| fig_count, fig_amount = create_transaction_distribution_chart(masked_df) | |
| # Display visualizations in columns | |
| st.subheader("Transaction Distribution Analysis") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| st.plotly_chart(fig_count, use_container_width=True) | |
| with col2: | |
| st.plotly_chart(fig_amount, use_container_width=True) | |
| # Daily transactions chart | |
| st.subheader("Daily Transaction Amounts") | |
| daily_amounts = masked_df.groupby('Transaction Date')['Amount'].sum() | |
| st.line_chart(daily_amounts) | |
| # Download button | |
| if st.button("Download Transactions"): | |
| csv = masked_df.to_csv(index=False) | |
| st.download_button( | |
| label="Download CSV", | |
| data=csv, | |
| file_name=f"{username}_{selected_month}_transactions.csv", | |
| mime="text/csv" | |
| ) | |
| else: | |
| st.warning(f"No transactions found for user: {username} in {selected_month}") | |
| if __name__ == "__main__": | |
| main() |