import streamlit as st
import firebase_admin
from firebase_admin import credentials, db
import pandas as pd
import plotly.express as px
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/'
    })

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 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({
                    '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 []

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

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")

    # Username dropdown
    usernames = fetch_usernames()
    username = st.sidebar.selectbox(
        "Select Username",
        options=usernames if usernames else ["No users found"]
    )

    # 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.warning(f"No transactions found for user: {username} in {selected_month}")

if __name__ == "__main__":
    main()