Update app.py

app.py

@@ -1,56 +1,245 @@
 import streamlit as st
-import requests
+import firebase_admin
+from firebase_admin import credentials, db
+import pandas as pd
+import plotly.express as px
+from datetime import datetime
 
-# Firebase Auth REST API to login
-def firebase_login(email, password):
+# 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 fetch_usernames():
+    """Fetch list of all usernames from Firebase"""
     try:
-        api_key = "AIzaSyBEL99hu6ja9LjNABpBRL1QtHCAVSh1umQ"  # Your Firebase Web API Key
-        url = f"https://identitytoolkit.googleapis.com/v1/accounts:signInWithPassword?key={api_key}"
-        payload = {"email": email, "password": password, "returnSecureToken": True}
-        response = requests.post(url, data=payload)
-
-        if response.status_code == 200:
-            user_data = response.json()
-            return user_data['localId'], user_data['idToken']
-        else:
-            return None, None
+        ref = db.reference('financialMessages')
+        users = ref.get()
+        if users:
+            return list(users.keys())
+        return []
     except Exception as e:
-        st.error(f"Error logging in: {str(e)}")
-        return None, None
-
-def main():
-    st.set_page_config(page_title="Login", layout="centered")
+        st.error(f"Error fetching usernames: {str(e)}")
+        return []
 
-    # Check login state
-    if "logged_in" not in st.session_state:
-        st.session_state.logged_in = False
-
-    # Check if already logged in
-    if st.session_state.logged_in:
-        st.write("You are already logged in.")
-        st.write("Redirecting to your dashboard...")
+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 []
 
-        # Redirect by setting query parameter
-        st.query_params.page = "app2"
-        return
+        messages = []
+        for transaction_id, data in transactions.items():
+            if isinstance(data, dict):
+                messages.append({
+                    'Person Name': data.get('personName', ''),
+                    '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 []
 
-    # Login form if not logged in
-    email = st.text_input("Email")
-    password = st.text_input("Password", type="password")
+def create_transaction_distribution_chart(df):
+    """Create an enhanced transaction distribution visualization with multiple chart types"""
+    # Calculate transaction type summaries
+    type_summary = df.groupby('Transaction Type').agg({
+        'Person Name': 'count',
+        'Amount': ['sum', 'mean', 'min', 'max']
+    }).round(2)
+    
+    type_summary.columns = ['Count', 'Total Amount', 'Average Amount', 'Min Amount', 'Max Amount']
+    type_summary = type_summary.reset_index()
+    
+    # Create bar chart comparing transaction counts and amounts
+    fig_comparison = px.bar(
+        type_summary,
+        x='Transaction Type',
+        y=['Count', 'Total Amount'],
+        barmode='group',
+        title='Transaction Comparison by Type',
+        labels={'value': 'Value', 'variable': 'Metric'},
+        color_discrete_sequence=['#4C78A8', '#72B7B2'],
+        template='plotly_white'
+    )
+    
+    fig_comparison.update_layout(
+        xaxis_title="Transaction Type",
+        yaxis_title="Value",
+        legend_title="Metric",
+        height=400,
+        showlegend=True,
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="right",
+            x=1
+        )
+    )
+    
+    # Create detailed metrics visualization
+    fig_metrics = px.bar(
+        type_summary.melt(
+            id_vars=['Transaction Type'],
+            value_vars=['Average Amount', 'Min Amount', 'Max Amount']
+        ),
+        x='Transaction Type',
+        y='value',
+        color='variable',
+        barmode='group',
+        title='Transaction Amount Metrics by Type',
+        labels={'value': 'Amount (₹)', 'variable': 'Metric'},
+        color_discrete_sequence=['#FF9DA7', '#9C755F', '#BAB0AC'],
+        template='plotly_white'
+    )
+    
+    fig_metrics.update_layout(
+        xaxis_title="Transaction Type",
+        yaxis_title="Amount (₹)",
+        height=400,
+        showlegend=True,
+        legend=dict(
+            orientation="h",
+            yanchor="bottom",
+            y=1.02,
+            xanchor="right",
+            x=1
+        )
+    )
+    
+    # Add hover information
+    for fig in [fig_comparison, fig_metrics]:
+        fig.update_traces(
+            hovertemplate="<br>".join([
+                "Transaction Type: %{x}",
+                "Value: %{y:,.2f}",
+                "<extra></extra>"
+            ])
+        )
+    
+    return fig_comparison, fig_metrics
 
-    if st.button("Login"):
-        user_id, id_token = firebase_login(email, password)
-        if user_id:
-            st.session_state.logged_in = True
-            st.session_state.email = email
-            st.session_state.user_id = user_id
+def main():
+    st.set_page_config(page_title="Financial Transactions Dashboard", layout="wide")
+    
+    # Header
+    st.title("Financial Transactions Dashboard")
+    st.markdown("---")
+    
+    # Sidebar filters
+    st.sidebar.header("Filters")
 
-            # Set query parameter to redirect to app2.py
-            st.query_params.page = "app2"
+    # Username dropdown
+    usernames = fetch_usernames()
+    username = st.sidebar.selectbox(
+        "Select Username",
+        options=usernames if usernames else ["No users found"]
+    )
 
-            st.success("Login successful! Redirecting to your dashboard...")
+    # Month selection
+    months = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
+    selected_month = st.sidebar.selectbox("Select Month", options=months)
+    
+    if username and username != "No users found":
+        # Fetch data
+        data = fetch_user_transactions(username, selected_month)
+        
+        if data:
+            df = pd.DataFrame(data)
+            df['Amount'] = pd.to_numeric(df['Amount'])
+            
+            # Transaction type dropdown
+            transaction_type = st.sidebar.selectbox(
+                "Select Transaction Type",
+                options=["All", "debited", "credited"]
+            )
+            
+            # Date filter
+            dates = df['Transaction Date'].unique()
+            selected_dates = st.sidebar.multiselect(
+                "Select Dates",
+                options=dates,
+                default=dates
+            )
+            
+            # Apply filters
+            if transaction_type != "All":
+                masked_df = df[
+                    (df['Transaction Type'] == transaction_type) &
+                    (df['Transaction Date'].isin(selected_dates))
+                ]
+            else:
+                masked_df = df[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
+            )
+            
+            # 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)
+                st.download_button(
+                    label="Download CSV",
+                    data=csv,
+                    file_name=f"{username}_{selected_month}_transactions.csv",
+                    mime="text/csv"
+                )
         else:
-            st.error("Login failed. Please check your credentials.")
+            st.warning(f"No transactions found for user: {username} in {selected_month}")
 
 if __name__ == "__main__":
-    main()
+    main()