Update app.py

app.py

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+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()