app2.py

import streamlit as st
import firebase_admin
from firebase_admin import credentials, auth, db
import pandas as pd
import plotly.express as px
from datetime import datetime

# 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.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
    # 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
    transaction_types = sorted(df['transactionType'].unique())
    selected_types = st.sidebar.multiselect("Transaction Types", transaction_types, default=transaction_types)
    
    # Filter data
    filtered_df = df[
        (df['month'].isin(selected_months)) &
        (df['transactionType'].isin(selected_types))
    ]
    
    # 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()