Update app.py

app.py

@@ -3,123 +3,102 @@ import firebase_admin
 from firebase_admin import credentials, db
 import pandas as pd
 import plotly.express as px
-import plotly.graph_objects as go
 from datetime import datetime
 
-# [Previous initialization code remains the same until the main() function]
+# 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 create_trend_chart(df):
-    """Create a line chart showing transaction trends"""
-    # Group by date and transaction type
-    daily_trends = df.pivot_table(
-        index='Transaction Date',
-        columns='Transaction Type',
-        values='Amount',
-        aggfunc='sum'
-    ).fillna(0)
-    
-    fig = go.Figure()
-    
-    if 'credited' in daily_trends.columns:
-        fig.add_trace(go.Scatter(
-            x=daily_trends.index,
-            y=daily_trends['credited'],
-            name='Credited',
-            line=dict(color='#00CC96', width=3),
-            fill='tonexty'
-        ))
-    
-    if 'debited' in daily_trends.columns:
-        fig.add_trace(go.Scatter(
-            x=daily_trends.index,
-            y=daily_trends['debited'],
-            name='Debited',
-            line=dict(color='#EF553B', width=3),
-            fill='tonexty'
-        ))
-    
-    fig.update_layout(
-        title='Daily Transaction Trends',
-        xaxis_title='Date',
-        yaxis_title='Amount (₹)',
-        hovermode='x unified',
-        showlegend=True,
-        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
-        height=400
-    )
-    
-    return fig
+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 create_distribution_chart(df):
-    """Create a pie chart for transaction distribution"""
+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({
+                    '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 create_transaction_distribution_chart(df):
+    """Create an enhanced transaction distribution visualization"""
+    # Calculate transaction type counts and amounts
     type_summary = df.groupby('Transaction Type').agg({
+        'Transaction ID': 'count',
         'Amount': 'sum'
     }).reset_index()
     
-    fig = px.pie(
+    type_summary.columns = ['Transaction Type', 'Count', 'Total Amount']
+    
+    # Create pie chart for transaction counts
+    fig_count = px.pie(
         type_summary,
-        values='Amount',
+        values='Count',
         names='Transaction Type',
-        title='Transaction Amount Distribution',
+        title='Transaction Distribution by Count',
         color='Transaction Type',
         color_discrete_map={'credited': '#00CC96', 'debited': '#EF553B'},
         hole=0.4
     )
     
-    fig.update_traces(
-        textposition='inside',
-        textinfo='percent+value',
-        pull=[0.1, 0.1],
-        marker=dict(line=dict(color='#FFFFFF', width=2))
-    )
-    
-    fig.update_layout(
-        showlegend=True,
-        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
-        height=400
-    )
-    
-    return fig
-
-def create_daily_comparison(df):
-    """Create a bar chart comparing daily credited vs debited amounts"""
-    daily_comparison = df.pivot_table(
-        index='Transaction Date',
-        columns='Transaction Type',
-        values='Amount',
-        aggfunc='sum'
-    ).fillna(0)
-    
-    fig = go.Figure()
-    
-    if 'credited' in daily_comparison.columns:
-        fig.add_trace(go.Bar(
-            name='Credited',
-            x=daily_comparison.index,
-            y=daily_comparison['credited'],
-            marker_color='#00CC96'
-        ))
-    
-    if 'debited' in daily_comparison.columns:
-        fig.add_trace(go.Bar(
-            name='Debited',
-            x=daily_comparison.index,
-            y=daily_comparison['debited'],
-            marker_color='#EF553B'
-        ))
-    
-    fig.update_layout(
-        title='Daily Transaction Comparison',
-        xaxis_title='Date',
-        yaxis_title='Amount (₹)',
-        barmode='group',
-        hovermode='x unified',
-        showlegend=True,
-        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
-        height=400
+    # Create pie chart for transaction amounts
+    fig_amount = px.pie(
+        type_summary,
+        values='Total Amount',
+        names='Transaction Type',
+        title='Transaction Distribution by Amount',
+        color='Transaction Type',
+        color_discrete_map={'credited': '#00CC96', 'debited': '#EF553B'},
+        hole=0.4
     )
     
-    return fig
+    # Update layout for better appearance
+    for fig in [fig_count, fig_amount]:
+        fig.update_traces(
+            textposition='inside',
+            textinfo='percent+label',
+            pull=[0.1, 0.1],
+            marker=dict(line=dict(color='#FFFFFF', width=2))
+        )
+        fig.update_layout(
+            showlegend=True,
+            legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
+            height=400
+        )
+    
+    return fig_count, fig_amount
 
 def main():
     st.set_page_config(page_title="Financial Transactions Dashboard", layout="wide")
@@ -174,7 +153,6 @@ def main():
                 masked_df = df[df['Transaction Date'].isin(selected_dates)]
             
             # Dashboard metrics
-            st.subheader("Transaction Summary")
             col1, col2, col3 = st.columns(3)
             
             with col1:
@@ -188,27 +166,7 @@ def main():
                 total_credited = masked_df[masked_df['Transaction Type'] == 'credited']['Amount'].sum()
                 st.metric("Total Credited", f"₹ {total_credited:,.2f}")
             
-            # Visualizations Section
-            st.markdown("---")
-            st.subheader("Transaction Analysis")
-            
-            # Create three columns for visualizations
-            col1, col2, col3 = st.columns(3)
-            
-            with col1:
-                trend_fig = create_trend_chart(masked_df)
-                st.plotly_chart(trend_fig, use_container_width=True)
-            
-            with col2:
-                dist_fig = create_distribution_chart(masked_df)
-                st.plotly_chart(dist_fig, use_container_width=True)
-            
-            with col3:
-                comp_fig = create_daily_comparison(masked_df)
-                st.plotly_chart(comp_fig, use_container_width=True)
-            
             # Transactions table
-            st.markdown("---")
             st.subheader("Recent Transactions")
             st.dataframe(
                 masked_df,
@@ -221,6 +179,24 @@ def main():
                 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)