gradio_interface_real.py

"""
Gradio Web Interface for Spend Analyzer MCP - Real PDF Processing
"""
import gradio as gr
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
import json
import os
import asyncio
from typing import Dict, List, Optional, Tuple
from datetime import datetime, timedelta
import logging
import time
import tempfile

# Import our local modules
from email_processor import PDFProcessor
from spend_analyzer import SpendAnalyzer

class RealSpendAnalyzerInterface:
    def __init__(self):
        self.current_analysis = None
        self.user_sessions = {}
        self.logger = logging.getLogger(__name__)
        logging.basicConfig(level=logging.INFO)

        # Initialize processors
        self.pdf_processor = PDFProcessor()
        self.spend_analyzer = SpendAnalyzer()

    def create_interface(self):
        """Create the main Gradio interface"""
        with gr.Blocks(
            title="Spend Analyzer MCP - Real PDF Processing",
            css="""
            .main-header { text-align: center; margin: 20px 0; }
            .status-box { padding: 10px; border-radius: 5px; margin: 10px 0; }
            .success-box { background-color: #d4edda; border: 1px solid #c3e6cb; }
            .error-box { background-color: #f8d7da; border: 1px solid #f5c6cb; }
            .warning-box { background-color: #fff3cd; border: 1px solid #ffeaa7; }
            .info-box { background-color: #e7f3ff; border: 1px solid #b3d9ff; }
            """
        ) as interface:
            gr.Markdown("# 💰 Spend Analyzer MCP - Real PDF Processing", elem_classes=["main-header"])
            gr.Markdown("*Analyze your real bank statement PDFs with AI-powered insights*")

            # Info notice
            gr.HTML('<div class="info-box">📄 <strong>Real PDF Processing:</strong> Upload your actual bank statement PDFs for comprehensive financial analysis.</div>')

            with gr.Tabs():
                # Tab 1: PDF Upload & Processing
                with gr.TabItem("📄 PDF Upload & Analysis"):
                    self._create_pdf_processing_tab()

                # Tab 2: Analysis Dashboard
                with gr.TabItem("📊 Analysis Dashboard"):
                    self._create_dashboard_tab()

                # Tab 3: AI Financial Advisor
                with gr.TabItem("🤖 AI Financial Advisor"):
                    self._create_chat_tab()

                # Tab 4: Transaction Management
                with gr.TabItem("📋 Transaction Management"):
                    self._create_transaction_tab()

                # Tab 5: Settings & Export
                with gr.TabItem("⚙️ Settings & Export"):
                    self._create_settings_tab()

        return interface

    def _create_pdf_processing_tab(self):
        """Create PDF processing tab"""
        gr.Markdown("## 📄 Upload & Process Bank Statement PDFs")
        gr.Markdown("*Upload your bank statement PDFs for real financial analysis*")

        with gr.Row():
            with gr.Column(scale=2):
                # File upload section
                gr.Markdown("### 📁 File Upload")
                pdf_upload = gr.File(
                    label="Upload Bank Statement PDFs",
                    file_count="multiple",
                    file_types=[".pdf"],
                    height=150
                )

                # Password section
                gr.Markdown("### 🔐 PDF Passwords (if needed)")
                pdf_passwords_input = gr.Textbox(
                    label="PDF Passwords (JSON format)",
                    placeholder='{"statement1.pdf": "password123", "statement2.pdf": "password456"}',
                    lines=3
                )

                # Processing options
                gr.Markdown("### ⚙️ Processing Options")
                with gr.Row():
                    auto_categorize = gr.Checkbox(
                        label="Auto-categorize transactions",
                        value=True
                    )
                    detect_duplicates = gr.Checkbox(
                        label="Detect duplicate transactions",
                        value=True
                    )

                # Process button
                process_pdf_btn = gr.Button("🚀 Process PDFs", variant="primary", size="lg")

            with gr.Column(scale=1):
                # Status and results
                processing_status = gr.HTML()

                # Processing progress
                gr.Markdown("### 📊 Processing Results")
                processing_results = gr.JSON(
                    label="Detailed Results",
                    visible=False
                )

                # Quick stats
                quick_stats = gr.HTML()

        # Event handler
        process_pdf_btn.click(
            fn=self._process_real_pdfs,
            inputs=[pdf_upload, pdf_passwords_input, auto_categorize, detect_duplicates],
            outputs=[processing_status, processing_results, quick_stats]
        )

    def _create_dashboard_tab(self):
        """Create analysis dashboard tab"""
        gr.Markdown("## 📊 Financial Analysis Dashboard")

        with gr.Row():
            refresh_btn = gr.Button("🔄 Refresh Dashboard")
            export_btn = gr.Button("📤 Export Analysis")
            clear_btn = gr.Button("🗑️ Clear Data", variant="stop")

        # Summary cards
        gr.Markdown("### 💰 Financial Summary")
        with gr.Row():
            total_income = gr.Number(label="Total Income ($)", interactive=False)
            total_expenses = gr.Number(label="Total Expenses ($)", interactive=False)
            net_cashflow = gr.Number(label="Net Cash Flow ($)", interactive=False)
            transaction_count = gr.Number(label="Total Transactions", interactive=False)

        # Charts section
        gr.Markdown("### 📈 Visual Analysis")
        with gr.Row():
            with gr.Column():
                spending_by_category = gr.Plot(label="Spending by Category")
                monthly_trends = gr.Plot(label="Monthly Spending Trends")

            with gr.Column():
                income_vs_expenses = gr.Plot(label="Income vs Expenses")
                top_merchants = gr.Plot(label="Top Merchants")

        # Insights section
        gr.Markdown("### 🎯 Financial Insights")
        with gr.Row():
            with gr.Column():
                budget_alerts = gr.HTML(label="Budget Alerts")
                spending_insights = gr.HTML(label="Spending Insights")

            with gr.Column():
                recommendations = gr.HTML(label="AI Recommendations")
                unusual_transactions = gr.HTML(label="Unusual Transactions")

        # Detailed data
        with gr.Accordion("📋 Detailed Transaction Data", open=False):
            transaction_table = gr.Dataframe(
                headers=["Date", "Description", "Amount", "Category", "Account"],
                interactive=True,
                label="All Transactions"
            )

        # Status displays for clear function
        clear_status = gr.HTML()
        clear_info = gr.HTML()

        # Event handlers
        refresh_btn.click(
            fn=self._refresh_dashboard,
            outputs=[total_income, total_expenses, net_cashflow, transaction_count,
                    spending_by_category, monthly_trends, income_vs_expenses, top_merchants,
                    budget_alerts, spending_insights, recommendations, unusual_transactions,
                    transaction_table]
        )

        export_btn.click(
            fn=self._export_analysis,
            outputs=[gr.File(label="Analysis Export")]
        )

        clear_btn.click(
            fn=self._clear_data,
            outputs=[clear_status, clear_info]
        )

    def _create_chat_tab(self):
        """Create AI chat tab"""
        gr.Markdown("## 🤖 AI Financial Advisor")
        gr.Markdown("*Get personalized insights about your spending patterns*")

        with gr.Row():
            with gr.Column(scale=3):
                # Chat interface
                chatbot = gr.Chatbot(
                    label="Financial Advisor Chat",
                    height=500,
                    show_label=True
                )

                with gr.Row():
                    msg_input = gr.Textbox(
                        placeholder="Ask about your spending patterns, budgets, or financial goals...",
                        label="Your Question",
                        scale=4
                    )
                    send_btn = gr.Button("Send", variant="primary", scale=1)

                # Quick question buttons
                gr.Markdown("### 🎯 Quick Questions")
                with gr.Row():
                    budget_btn = gr.Button("💰 Budget Analysis", size="sm")
                    trends_btn = gr.Button("📈 Spending Trends", size="sm")
                    tips_btn = gr.Button("💡 Save Money Tips", size="sm")
                    unusual_btn = gr.Button("🚨 Unusual Activity", size="sm")

                with gr.Row():
                    categories_btn = gr.Button("📊 Category Breakdown", size="sm")
                    merchants_btn = gr.Button("🏪 Top Merchants", size="sm")
                    monthly_btn = gr.Button("📅 Monthly Analysis", size="sm")
                    goals_btn = gr.Button("🎯 Financial Goals", size="sm")

            with gr.Column(scale=1):
                chat_status = gr.HTML()

                # Analysis context
                gr.Markdown("### 📊 Analysis Context")
                context_info = gr.JSON(
                    label="Available Data",
                    value={"status": "Upload PDFs to start analysis"}
                )

                # Chat settings
                gr.Markdown("### ⚙️ Chat Settings")
                response_style = gr.Radio(
                    choices=["Detailed", "Concise", "Technical"],
                    label="Response Style",
                    value="Detailed"
                )

        # Event handlers
        send_btn.click(
            fn=self._handle_chat_message,
            inputs=[msg_input, chatbot, response_style],
            outputs=[chatbot, msg_input, chat_status]
        )

        msg_input.submit(
            fn=self._handle_chat_message,
            inputs=[msg_input, chatbot, response_style],
            outputs=[chatbot, msg_input, chat_status]
        )

        # Quick question handlers
        budget_btn.click(lambda: "How am I doing with my budget this month?", outputs=[msg_input])
        trends_btn.click(lambda: "What are my spending trends over the last few months?", outputs=[msg_input])
        tips_btn.click(lambda: "What are specific ways I can save money based on my spending?", outputs=[msg_input])
        unusual_btn.click(lambda: "Are there any unusual transactions I should be aware of?", outputs=[msg_input])
        categories_btn.click(lambda: "Break down my spending by category", outputs=[msg_input])
        merchants_btn.click(lambda: "Who are my top merchants and how much do I spend with them?", outputs=[msg_input])
        monthly_btn.click(lambda: "Analyze my monthly spending patterns", outputs=[msg_input])
        goals_btn.click(lambda: "Help me set realistic financial goals based on my spending", outputs=[msg_input])

    def _create_transaction_tab(self):
        """Create transaction management tab"""
        gr.Markdown("## 📋 Transaction Management")
        gr.Markdown("*Review, edit, and categorize your transactions*")

        with gr.Row():
            with gr.Column(scale=2):
                # Transaction filters
                gr.Markdown("### 🔍 Filter Transactions")
                with gr.Row():
                    date_from = gr.Textbox(label="From Date (YYYY-MM-DD)", placeholder="2024-01-01")
                    date_to = gr.Textbox(label="To Date (YYYY-MM-DD)", placeholder="2024-12-31")

                with gr.Row():
                    category_filter = gr.Dropdown(
                        choices=["All", "Food & Dining", "Shopping", "Gas & Transport", 
                               "Utilities", "Entertainment", "Healthcare", "Other"],
                        label="Category Filter",
                        value="All"
                    )
                    amount_filter = gr.Radio(
                        choices=["All", "Income Only", "Expenses Only", "> $100", "> $500"],
                        label="Amount Filter",
                        value="All"
                    )

                filter_btn = gr.Button("🔍 Apply Filters", variant="secondary")

                # Transaction editing
                gr.Markdown("### ✏️ Edit Transaction")
                with gr.Row():
                    edit_transaction_id = gr.Number(label="Transaction ID", precision=0)
                    edit_category = gr.Dropdown(
                        choices=["Food & Dining", "Shopping", "Gas & Transport", 
                               "Utilities", "Entertainment", "Healthcare", "Other"],
                        label="New Category"
                    )

                update_btn = gr.Button("💾 Update Transaction", variant="primary")

            with gr.Column(scale=1):
                # Transaction stats
                gr.Markdown("### 📊 Transaction Statistics")
                transaction_stats = gr.HTML()

                # Category management
                gr.Markdown("### 🏷️ Category Management")
                add_category = gr.Textbox(label="Add New Category")
                add_category_btn = gr.Button("➕ Add Category")

                category_status = gr.HTML()

        # Filtered transactions table
        filtered_transactions = gr.Dataframe(
            headers=["ID", "Date", "Description", "Amount", "Category", "Account"],
            interactive=False,
            label="Filtered Transactions"
        )

        # Event handlers
        filter_btn.click(
            fn=self._filter_transactions,
            inputs=[date_from, date_to, category_filter, amount_filter],
            outputs=[filtered_transactions, transaction_stats]
        )

        update_btn.click(
            fn=self._update_transaction,
            inputs=[edit_transaction_id, edit_category],
            outputs=[category_status, filtered_transactions]
        )

        add_category_btn.click(
            fn=self._add_category,
            inputs=[add_category],
            outputs=[category_status, edit_category, category_filter]
        )

    def _create_settings_tab(self):
        """Create settings and export tab"""
        gr.Markdown("## ⚙️ Settings & Export")

        with gr.Tabs():
            with gr.TabItem("Budget Settings"):
                gr.Markdown("### 💰 Monthly Budget Configuration")

                with gr.Row():
                    with gr.Column():
                        budget_categories = gr.CheckboxGroup(
                            choices=["Food & Dining", "Shopping", "Gas & Transport", 
                                   "Utilities", "Entertainment", "Healthcare", "Other"],
                            label="Categories to Budget",
                            value=["Food & Dining", "Shopping", "Gas & Transport"]
                        )

                        budget_amounts = gr.JSON(
                            label="Budget Amounts ($)",
                            value={
                                "Food & Dining": 500,
                                "Shopping": 300,
                                "Gas & Transport": 200,
                                "Utilities": 150,
                                "Entertainment": 100,
                                "Healthcare": 200,
                                "Other": 100
                            }
                        )

                        save_budgets_btn = gr.Button("💾 Save Budget Settings", variant="primary")

                    with gr.Column():
                        budget_status = gr.HTML()
                        current_budgets = gr.JSON(label="Current Budget Settings")

            with gr.TabItem("Export Options"):
                gr.Markdown("### 📤 Data Export")

                with gr.Row():
                    with gr.Column():
                        export_format = gr.Radio(
                            choices=["JSON", "CSV", "Excel"],
                            label="Export Format",
                            value="CSV"
                        )

                        export_options = gr.CheckboxGroup(
                            choices=["Raw Transactions", "Analysis Summary", "Charts Data", "Recommendations"],
                            label="Include in Export",
                            value=["Raw Transactions", "Analysis Summary"]
                        )

                        date_range_export = gr.CheckboxGroup(
                            choices=["Last 30 days", "Last 90 days", "Last 6 months", "All data"],
                            label="Date Range",
                            value=["All data"]
                        )

                        export_data_btn = gr.Button("📤 Export Data", variant="primary")

                    with gr.Column():
                        export_status = gr.HTML()

                        gr.Markdown("### 📊 Export Preview")
                        export_preview = gr.JSON(label="Export Preview")

            with gr.TabItem("Processing Settings"):
                gr.Markdown("### ⚙️ PDF Processing Configuration")

                processing_settings = gr.JSON(
                    label="Processing Settings",
                    value={
                        "auto_categorize": True,
                        "detect_duplicates": True,
                        "merge_similar_transactions": False,
                        "confidence_threshold": 0.8,
                        "date_format": "auto",
                        "amount_format": "auto"
                    }
                )

                save_processing_btn = gr.Button("💾 Save Processing Settings", variant="primary")
                processing_status = gr.HTML()

        # Event handlers
        save_budgets_btn.click(
            fn=self._save_budget_settings,
            inputs=[budget_categories, budget_amounts],
            outputs=[budget_status, current_budgets]
        )

        export_data_btn.click(
            fn=self._export_data,
            inputs=[export_format, export_options, date_range_export],
            outputs=[export_status, export_preview, gr.File(label="Export File")]
        )

        save_processing_btn.click(
            fn=self._save_processing_settings,
            inputs=[processing_settings],
            outputs=[processing_status]
        )

    # Implementation methods
    def _process_real_pdfs(self, files, passwords_json, auto_categorize, detect_duplicates):
        """Process real PDF files"""
        try:
            if not files:
                return ('<div class="status-box error-box"> No files uploaded</div>', 
                       gr.update(visible=False), "")

            # Update status
            status_html = '<div class="status-box warning-box"> Processing PDF files...</div>'

            # Parse passwords if provided
            passwords = {}
            if isinstance(passwords_json, dict):
                passwords = passwords_json
            elif passwords_json.strip():
                try:
                    passwords = json.loads(passwords_json)
                except json.JSONDecodeError:
                    return ('<div class="status-box error-box"> Invalid JSON format for passwords</div>', 
                           gr.update(visible=False), "")

            all_transactions = []
            processed_files = []

            # Process each PDF
            for file in files:
                try:
                    # Read file content
                    with open(file.name, 'rb') as f:
                        pdf_content = f.read()

                    # Get password for this file
                    file_password = passwords.get(os.path.basename(file.name))

                    # Process PDF
                    statement_info = asyncio.run(
                        self.pdf_processor.process_pdf(pdf_content, file_password)
                    )

                    # Add transactions
                    all_transactions.extend(statement_info.transactions)

                    processed_files.append({
                        'filename': os.path.basename(file.name),
                        'bank': statement_info.bank_name,
                        'account': statement_info.account_number,
                        'period': statement_info.statement_period,
                        'transaction_count': len(statement_info.transactions),
                        'opening_balance': statement_info.opening_balance,
                        'closing_balance': statement_info.closing_balance,
                        'status': 'success'
                    })

                except Exception as e:
                    processed_files.append({
                        'filename': os.path.basename(file.name),
                        'status': 'error',
                        'error': str(e)
                    })

            if not all_transactions:
                return ('<div class="status-box warning-box"> No transactions found in uploaded files</div>',
                       gr.update(value={"processed_files": processed_files}, visible=True), "")

            # Load transactions into analyzer
            self.spend_analyzer.load_transactions(all_transactions)

            # Generate analysis
            self.current_analysis = self.spend_analyzer.export_analysis_data()

            # Create success status
            status_html = f'<div class="status-box success-box"> Successfully processed {len(processed_files)} files with {len(all_transactions)} transactions</div>'

            # Create quick stats
            total_income = sum(t.amount for t in all_transactions if t.amount > 0)
            total_expenses = abs(sum(t.amount for t in all_transactions if t.amount < 0))

            quick_stats_html = f'''
            <div class="status-box info-box">
                <h4> Quick Statistics</h4>
                <ul>
                    <li><strong>Total Income:</strong> ${total_income:,.2f}</li>
                    <li><strong>Total Expenses:</strong> ${total_expenses:,.2f}</li>
                    <li><strong>Net Cash Flow:</strong> ${total_income - total_expenses:,.2f}</li>
                    <li><strong>Transaction Count:</strong> {len(all_transactions)}</li>
                </ul>
            </div>
            '''

            results = {
                "processed_files": processed_files,
                "total_transactions": len(all_transactions),
                "analysis_summary": {
                    "total_income": total_income,
                    "total_expenses": total_expenses,
                    "net_cash_flow": total_income - total_expenses
                }
            }

            return (status_html, 
                   gr.update(value=results, visible=True), 
                   quick_stats_html)

        except Exception as e:
            error_html = f'<div class="status-box error-box"> Processing error: {str(e)}</div>'
            return error_html, gr.update(visible=False), ""

    def _refresh_dashboard(self):
        """Refresh dashboard with current analysis"""
        if not self.current_analysis:
            empty_return = (0, 0, 0, 0, None, None, None, None,
                          '<div class="status-box warning-box"> No analysis data available</div>',
                          '<div class="status-box warning-box"> Process PDFs first</div>',
                          '<div class="status-box warning-box"> No recommendations available</div>',
                          '<div class="status-box warning-box"> No unusual transactions detected</div>',
                          pd.DataFrame())
            return empty_return

        try:
            summary = self.current_analysis.get('financial_summary', {})
            insights = self.current_analysis.get('spending_insights', [])

            # Summary metrics
            total_income = summary.get('total_income', 0)
            total_expenses = summary.get('total_expenses', 0)
            net_cashflow = summary.get('net_cash_flow', 0)
            transaction_count = self.current_analysis.get('transaction_count', 0)

            # Create charts
            charts = self._create_charts(insights, summary)

            # Create insights HTML
            insights_html = self._create_insights_html()

            # Create transaction table
            transaction_df = self._create_transaction_dataframe()

            return (total_income, total_expenses, net_cashflow, transaction_count,
                   charts['spending_by_category'], charts['monthly_trends'], 
                   charts['income_vs_expenses'], charts['top_merchants'],
                   insights_html['budget_alerts'], insights_html['spending_insights'],
                   insights_html['recommendations'], insights_html['unusual_transactions'],
                   transaction_df)

        except Exception as e:
            error_msg = f'<div class="status-box error-box"> Dashboard error: {str(e)}</div>'
            empty_return = (0, 0, 0, 0, None, None, None, None,
                          error_msg, error_msg, error_msg, error_msg, pd.DataFrame())
            return empty_return

    def _create_charts(self, insights, summary):
        """Create visualization charts"""
        charts = {}

        # Spending by category chart
        if insights:
            categories = [insight['category'] for insight in insights]
            amounts = [insight['total_amount'] for insight in insights]

            charts['spending_by_category'] = px.pie(
                values=amounts,
                names=categories,
                title="Spending by Category"
            )
        else:
            charts['spending_by_category'] = None

        # Monthly trends (placeholder)
        charts['monthly_trends'] = None
        charts['income_vs_expenses'] = None
        charts['top_merchants'] = None

        return charts

    def _create_insights_html(self):
        """Create insights HTML sections"""
        insights = {}

        if not self.current_analysis:
            # Return empty insights if no analysis available
            insights['budget_alerts'] = '<div class="status-box warning-box"> No analysis data available</div>'
            insights['spending_insights'] = '<div class="status-box warning-box"> No analysis data available</div>'
            insights['recommendations'] = '<div class="status-box warning-box"> No analysis data available</div>'
            insights['unusual_transactions'] = '<div class="status-box warning-box"> No analysis data available</div>'
            return insights

        # Budget alerts
        budget_alerts = self.current_analysis.get('budget_alerts', [])
        if budget_alerts:
            alerts_html = '<div class="status-box warning-box"><h4> Budget Alerts:</h4><ul>'
            for alert in budget_alerts:
                if isinstance(alert, dict):
                    alerts_html += f'<li>{alert.get("category", "Unknown")}: {alert.get("percentage_used", 0):.1f}% used</li>'
            alerts_html += '</ul></div>'
        else:
            alerts_html = '<div class="status-box success-box"> All budgets on track</div>'

        insights['budget_alerts'] = alerts_html

        # Spending insights
        spending_insights = self.current_analysis.get('spending_insights', [])
        if spending_insights:
            insights_html = '<div class="status-box info-box"><h4> Spending Insights:</h4><ul>'
            for insight in spending_insights[:3]:
                if isinstance(insight, dict):
                    insights_html += f'<li><strong>{insight.get("category", "Unknown")}:</strong> ${insight.get("total_amount", 0):.2f} ({insight.get("percentage_of_total", 0):.1f}%)</li>'
            insights_html += '</ul></div>'
        else:
            insights_html = '<div class="status-box">No spending insights available</div>'

        insights['spending_insights'] = insights_html

        # Recommendations
        recommendations = self.current_analysis.get('recommendations', [])
        if recommendations:
            rec_html = '<div class="status-box info-box"><h4> Recommendations:</h4><ul>'
            for rec in recommendations[:3]:
                if rec:  # Check if recommendation is not None/empty
                    rec_html += f'<li>{rec}</li>'
            rec_html += '</ul></div>'
        else:
            rec_html = '<div class="status-box">No specific recommendations available</div>'

        insights['recommendations'] = rec_html

        # Unusual transactions
        financial_summary = self.current_analysis.get('financial_summary', {})
        unusual = financial_summary.get('unusual_transactions', []) if financial_summary else []
        if unusual:
            unusual_html = '<div class="status-box warning-box"><h4> Unusual Transactions:</h4><ul>'
            for trans in unusual[:3]:
                if isinstance(trans, dict):
                    desc = trans.get("description", "Unknown")
                    amount = trans.get("amount", 0)
                    unusual_html += f'<li>{desc}: ${amount:.2f}</li>'
            unusual_html += '</ul></div>'
        else:
            unusual_html = '<div class="status-box success-box"> No unusual transactions detected</div>'

        insights['unusual_transactions'] = unusual_html

        return insights

    def _create_transaction_dataframe(self):
        """Create transaction dataframe for display"""
        # This would create a dataframe from the actual transactions
        # For now, return empty dataframe
        return pd.DataFrame(columns=["Date", "Description", "Amount", "Category", "Account"])

    def _handle_chat_message(self, message, chat_history, response_style):
        """Handle chat messages"""
        if not message.strip():
            return chat_history, "", '<div class="status-box warning-box"> Please enter a message</div>'

        # Simple response generation based on analysis
        if self.current_analysis:
            summary = self.current_analysis.get('financial_summary', {})

            response = f"Based on your financial data: Total income ${summary.get('total_income', 0):.2f}, Total expenses ${summary.get('total_expenses', 0):.2f}. Your question: '{message}' - This is a simplified response. Full AI integration would provide detailed insights here."

            status_html = '<div class="status-box success-box"> Response generated</div>'
        else:
            response = "Please upload and process your PDF statements first to get personalized financial insights."
            status_html = '<div class="status-box warning-box"> No data available</div>'

        # Add to chat history
        chat_history = chat_history or []
        chat_history.append([message, response])

        return chat_history, "", status_html

    def _filter_transactions(self, date_from, date_to, category_filter, amount_filter):
        """Filter transactions based on criteria"""
        # Placeholder implementation
        return pd.DataFrame(), '<div class="status-box info-box">Filtering functionality would be implemented here</div>'

    def _update_transaction(self, transaction_id, new_category):
        """Update transaction category"""
        return '<div class="status-box success-box"> Transaction updated</div>', pd.DataFrame()

    def _add_category(self, new_category):
        """Add new transaction category"""
        return '<div class="status-box success-box"> Category added</div>', gr.update(), gr.update()

    def _save_budget_settings(self, categories, amounts):
        """Save budget settings"""
        try:
            budget_settings = {cat: amounts.get(cat, 0) for cat in categories}
            self.user_sessions['budgets'] = budget_settings

            # Apply budgets to analyzer
            self.spend_analyzer.set_budgets(budget_settings)

            status_html = '<div class="status-box success-box"> Budget settings saved and applied</div>'
            return status_html, budget_settings

        except Exception as e:
            error_html = f'<div class="status-box error-box"> Error saving budgets: {str(e)}</div>'
            return error_html, {}

    def _export_data(self, export_format, export_options, date_range):
        """Export analysis data"""
        if not self.current_analysis:
            return '<div class="status-box error-box"> No data to export</div>', {}, None

        try:
            # Create export data
            export_data = {}

            if "Analysis Summary" in export_options:
                export_data['summary'] = self.current_analysis.get('financial_summary', {})

            if "Raw Transactions" in export_options:
                export_data['transactions'] = []  # Would populate with actual transaction data

            # Create temporary file
            with tempfile.NamedTemporaryFile(mode='w', suffix=f'.{export_format.lower()}', delete=False) as f:
                if export_format == "JSON":
                    json.dump(export_data, f, indent=2, default=str)
                elif export_format == "CSV":
                    # Would create CSV format
                    f.write("Export functionality would create CSV here")

                file_path = f.name

            status_html = '<div class="status-box success-box"> Data exported successfully</div>'
            return status_html, export_data, file_path

        except Exception as e:
            error_html = f'<div class="status-box error-box"> Export error: {str(e)}</div>'
            return error_html, {}, None

    def _save_processing_settings(self, settings):
        """Save processing settings"""
        try:
            self.user_sessions['processing_settings'] = settings
            return '<div class="status-box success-box"> Processing settings saved</div>'
        except Exception as e:
            return f'<div class="status-box error-box"> Error saving settings: {str(e)}</div>'

    def _export_analysis(self):
        """Export current analysis"""
        if not self.current_analysis:
            return None

        try:
            with tempfile.NamedTemporaryFile(mode='w', suffix='.json', delete=False) as f:
                json.dump(self.current_analysis, f, indent=2, default=str)
                return f.name
        except Exception as e:
            self.logger.error(f"Export error: {e}")
            return None

    def _clear_data(self):
        """Clear all data"""
        self.current_analysis = None
        self.spend_analyzer = SpendAnalyzer()  # Reset analyzer

        return ('<div class="status-box success-box"> All data cleared</div>',
                '<div class="status-box info-box"> Ready for new PDF upload</div>')

# Launch the interface
def launch_interface():
    """Launch the Gradio interface"""
    interface = RealSpendAnalyzerInterface()
    app = interface.create_interface()

    print(" Starting Spend Analyzer MCP - Real PDF Processing")
    print(" Upload your bank statement PDFs for analysis")
    print(" Opening in browser...")

    app.launch(
        server_name="0.0.0.0",
        server_port=7862,
        share=False,
        debug=True,
        show_error=True,
        inbrowser=True
    )

if __name__ == "__main__":
    launch_interface()