app.py

import streamlit as st
import firebase_admin
from firebase_admin import credentials, db
import pandas as pd
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/'  # Add your Firebase Realtime Database URL here
    })

def fetch_user_transactions(username):
    """Fetch financial messages for a specific user from Firebase Realtime Database"""
    try:
        # Reference to the financial messages path for the specific user
        ref = db.reference(f'financialMessages/{username}/Apr')
        # Get all transactions
        transactions = ref.get()
        
        if not transactions:
            return []

        messages = []
        # Convert the data to a list of dictionaries
        for transaction_id, data in transactions.items():
            if isinstance(data, dict):  # Ensure we're only processing dictionary data
                messages.append({
                    'Transaction ID': transaction_id,
                    '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 main():
    st.set_page_config(page_title="Financial Transactions Dashboard", layout="wide")
    
    # Header
    st.title("Financial Transactions Dashboard")
    st.markdown("---")
    
    # User input
    username = st.text_input("Enter Username (e.g., Akshay Chame)", "Akshay Chame")
    
    if username:
        # Format username to match database structure (replace spaces with actual format)
        formatted_username = username.strip()
        
        # Fetch data
        data = fetch_user_transactions(formatted_username)
        
        if data:
            df = pd.DataFrame(data)
            
            # Convert amount to numeric
            df['Amount'] = pd.to_numeric(df['Amount'])
            
            # Sidebar filters
            st.sidebar.header("Filters")
            
            # Transaction type filter
            transaction_types = st.sidebar.multiselect(
                "Select Transaction Type",
                options=df['Transaction Type'].unique(),
                default=df['Transaction Type'].unique()
            )
            
            # Date range filter
            dates = df['Transaction Date'].unique()
            selected_dates = st.sidebar.multiselect(
                "Select Dates",
                options=dates,
                default=dates
            )
            
            # Apply filters
            masked_df = df[
                (df['Transaction Type'].isin(transaction_types)) &
                (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
            )
            
            # Charts
            col1, col2 = st.columns(2)
            
            with col1:
                st.subheader("Transaction Type Distribution")
                type_counts = masked_df['Transaction Type'].value_counts()
                st.bar_chart(type_counts)
            
            with col2:
                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}_transactions.csv",
                    mime="text/csv"
                )
        else:
            st.warning(f"No transactions found for user: {username}")

if __name__ == "__main__":
    main()