Create app2.py

app2.py

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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
+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()