Update app.py

app.py

@@ -1016,9 +1016,28 @@ class HuggingFaceChatBot:
 # FIXED APPLICATION WITH UNIQUE WIDGET KEYS
 # ===============================================================================
 
+# ===============================================================================
+# FIXED APPLICATION WITH DYNAMIC UNIQUE KEYS AND SESSION STATE
+# ===============================================================================
+
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import json
+from datetime import datetime
+import os
+import uuid
+
+# Generate unique session ID for this run
+if 'session_id' not in st.session_state:
+    st.session_state.session_id = str(uuid.uuid4())[:8]
+
 def create_huggingface_app():
     """Main Streamlit application optimized for Hugging Face Spaces"""
     
+    # Get unique session ID
+    session_id = st.session_state.session_id
+    
     # Custom CSS for better UI
     st.markdown("""
     <style>
@@ -1040,14 +1059,6 @@ def create_huggingface_app():
     .status-ok { color: #28a745; font-weight: bold; }
     .status-warning { color: #ffc107; font-weight: bold; }
     .status-error { color: #dc3545; font-weight: bold; }
-    .chat-container {
-        border: 1px solid #ddd;
-        border-radius: 10px;
-        padding: 1rem;
-        margin: 1rem 0;
-        max-height: 400px;
-        overflow-y: auto;
-    }
     </style>
     """, unsafe_allow_html=True)
     
@@ -1064,31 +1075,38 @@ def create_huggingface_app():
     # Initialize processor
     if 'hf_processor' not in st.session_state:
         with st.spinner("🔧 Initializing AI Invoice Processor..."):
-            st.session_state.hf_processor = HuggingFaceInvoiceProcessor()
-            st.session_state.hf_chatbot = HuggingFaceChatBot(st.session_state.hf_processor)
-            st.session_state.chat_history = []
+            try:
+                from enhanced_invoice_system_part1 import (
+                    HuggingFaceInvoiceProcessor, HF_CONFIG
+                )
+                st.session_state.hf_processor = HuggingFaceInvoiceProcessor()
+                st.session_state.hf_chatbot = HuggingFaceChatBot(st.session_state.hf_processor)
+                st.session_state.chat_history = []
+                st.success("✅ System initialized successfully!")
+            except Exception as e:
+                st.error(f"❌ Initialization failed: {e}")
+                st.stop()
     
     # Sidebar with system status
     with st.sidebar:
         st.header("🎛️ System Status")
         
-        # Check component status
         processor = st.session_state.hf_processor
         
-        # Document processing
-        if processor.document_processor.processors:
+        # Document processing status
+        if hasattr(processor, 'document_processor') and processor.document_processor.processors:
             st.markdown('<span class="status-ok">✅ Document Processing</span>', unsafe_allow_html=True)
         else:
             st.markdown('<span class="status-error">❌ Document Processing</span>', unsafe_allow_html=True)
         
-        # AI extraction
-        if processor.ai_extractor.use_transformers:
+        # AI extraction status
+        if hasattr(processor, 'ai_extractor') and processor.ai_extractor.use_transformers:
             st.markdown('<span class="status-ok">✅ AI Extraction</span>', unsafe_allow_html=True)
         else:
             st.markdown('<span class="status-warning">⚠️ Regex Extraction</span>', unsafe_allow_html=True)
         
-        # Vector search
-        if processor.vector_store and processor.vector_store.embedding_model:
+        # Vector search status
+        if hasattr(processor, 'vector_store') and processor.vector_store and processor.vector_store.embedding_model:
             st.markdown('<span class="status-ok">✅ Semantic Search</span>', unsafe_allow_html=True)
         else:
             st.markdown('<span class="status-warning">⚠️ Keyword Search Only</span>', unsafe_allow_html=True)
@@ -1102,33 +1120,38 @@ def create_huggingface_app():
             
             st.metric("Total Invoices", total_invoices)
             st.metric("Total Value", f"₹{total_amount:,.2f}")
-            st.metric("Success Rate", f"{processor.processing_stats['successful']}/{processor.processing_stats['total_processed']}")
+            
+            if hasattr(processor, 'processing_stats'):
+                success_rate = f"{processor.processing_stats['successful']}/{processor.processing_stats['total_processed']}"
+                st.metric("Success Rate", success_rate)
             
         except Exception as e:
             st.error(f"Stats error: {e}")
         
         # Processing info
-        st.header("⚙️ Processing Info")
+        st.header("⚙️ System Info")
         st.info(f"""
-        **Limits for Hugging Face Spaces:**
-        • Max file size: {HF_CONFIG['max_file_size_mb']}MB
-        • Max concurrent files: {HF_CONFIG['max_concurrent_files']}
-        • Timeout: {HF_CONFIG['timeout_seconds']}s
+        **Session ID:** {session_id}
+        
+        **Limits:**
+        • Max file size: 10MB
+        • Max concurrent files: 3
+        • Timeout: 30s
         """)
     
-    # Main tabs
-    tab1, tab2, tab3, tab4 = st.tabs([
-        "📤 Upload & Process", 
-        "💬 AI Chat", 
-        "📊 Analytics", 
-        "📋 Data Explorer"
-    ])
+    # Main navigation
+    selected_tab = st.radio(
+        "Choose a section:",
+        ["📤 Upload & Process", "💬 AI Chat", "📊 Analytics", "📋 Data Explorer"],
+        horizontal=True,
+        key=f"main_navigation_{session_id}"
+    )
     
     # -------------------------------------------------------------------------
-    # TAB 1: UPLOAD & PROCESS - FIXED WITH UNIQUE KEY
+    # UPLOAD & PROCESS SECTION
     # -------------------------------------------------------------------------
     
-    with tab1:
+    if selected_tab == "📤 Upload & Process":
         st.header("📤 Upload Invoice Documents")
         
         # Feature highlights
@@ -1158,83 +1181,64 @@ def create_huggingface_app():
             </div>
             """, unsafe_allow_html=True)
         
-        # File upload interface with UNIQUE KEY
+        # File upload interface
         st.markdown("### 📁 Upload Your Invoices")
         
+        # Use timestamp to ensure unique keys
+        timestamp = datetime.now().strftime("%H%M%S")
+        
         uploaded_files = st.file_uploader(
             "Choose invoice files (PDF, TXT supported)",
             type=['pdf', 'txt'],
             accept_multiple_files=True,
-            help=f"Maximum file size: {HF_CONFIG['max_file_size_mb']}MB per file",
-            key="main_file_uploader"  # UNIQUE KEY ADDED
+            help="Maximum file size: 10MB per file",
+            key=f"file_uploader_{session_id}_{timestamp}"
         )
         
         if uploaded_files:
-            # Limit concurrent processing for HF Spaces
-            if len(uploaded_files) > HF_CONFIG['max_concurrent_files']:
-                st.warning(f"⚠️ Too many files selected. Processing first {HF_CONFIG['max_concurrent_files']} files.")
-                uploaded_files = uploaded_files[:HF_CONFIG['max_concurrent_files']]
+            max_files = 3
+            if len(uploaded_files) > max_files:
+                st.warning(f"⚠️ Too many files selected. Processing first {max_files} files.")
+                uploaded_files = uploaded_files[:max_files]
             
             st.info(f"📊 {len(uploaded_files)} files selected")
             
-            if st.button("🚀 Process Files", type="primary", use_container_width=True, key="process_files_btn"):
-                progress_bar = st.progress(0)
-                status_container = st.container()
-                results_container = st.container()
-                
-                successful = 0
-                failed = 0
-                
-                for i, uploaded_file in enumerate(uploaded_files):
-                    progress_bar.progress((i + 1) / len(uploaded_files))
-                    
-                    with status_container:
-                        st.info(f"Processing: {uploaded_file.name}")
-                    
-                    # Process file
-                    result = st.session_state.hf_processor.process_uploaded_file(uploaded_file)
-                    
-                    with results_container:
-                        if result.invoice_number:
-                            successful += 1
-                            with st.expander(f"✅ {uploaded_file.name}", expanded=False, key=f"result_expander_{i}"):
-                                col1, col2 = st.columns(2)
-                                with col1:
-                                    st.write(f"**Invoice #:** {result.invoice_number}")
-                                    st.write(f"**Supplier:** {result.supplier_name}")
-                                    st.write(f"**Amount:** ₹{result.amount:.2f}")
-                                with col2:
-                                    st.write(f"**Date:** {result.date}")
-                                    st.write(f"**Method:** {result.processing_method}")
-                                    st.write(f"**Confidence:** {result.extraction_confidence:.1%}")
-                        else:
-                            failed += 1
-                            st.warning(f"⚠️ Could not extract data from {uploaded_file.name}")
-                
-                # Final status
-                with status_container:
-                    st.success(f"✅ Processing complete! {successful} successful, {failed} failed")
-                
-                if successful > 0:
-                    st.balloons()
+            if st.button("🚀 Process Files", type="primary", key=f"process_btn_{session_id}_{timestamp}"):
+                process_files(uploaded_files, session_id)
     
     # -------------------------------------------------------------------------
-    # TAB 2: AI CHAT - FIXED LAYOUT WITH UNIQUE KEYS
+    # AI CHAT SECTION
     # -------------------------------------------------------------------------
     
-    with tab2:
+    elif selected_tab == "💬 AI Chat":
         st.header("💬 AI Chat Interface")
         
-        # Display chat history first
+        # Display chat history
         if st.session_state.chat_history:
             st.markdown("### 💬 Chat History")
-            chat_container = st.container()
-            with chat_container:
-                for i, message in enumerate(st.session_state.chat_history):
-                    with st.chat_message(message["role"]):
-                        st.markdown(message["content"])
+            for i, message in enumerate(st.session_state.chat_history):
+                with st.chat_message(message["role"]):
+                    st.markdown(message["content"])
+        
+        # Chat input area
+        st.markdown("### ✍️ Ask a Question")
+        
+        col1, col2 = st.columns([4, 1])
+        
+        with col1:
+            user_input = st.text_input(
+                "Type your question:",
+                placeholder="e.g., 'show me total spending'",
+                key=f"chat_input_{session_id}"
+            )
+        
+        with col2:
+            ask_btn = st.button("🚀 Ask", type="primary", key=f"ask_btn_{session_id}")
+        
+        if ask_btn and user_input:
+            handle_chat_query(user_input)
         
-        # Suggested queries when no chat history
+        # Suggested queries
         if not st.session_state.chat_history:
             st.markdown("### 💡 Try These Queries")
             
@@ -1242,85 +1246,39 @@ def create_huggingface_app():
             
             with col1:
                 st.markdown("**📊 Basic Queries:**")
-                queries = [
+                basic_queries = [
                     "Show me a summary of all invoices",
                     "How much have we spent in total?",
                     "Who are our top suppliers?",
                     "Find invoices with high amounts"
                 ]
-                for i, query in enumerate(queries):
-                    if st.button(query, key=f"basic_query_{i}", use_container_width=True):
-                        st.session_state.chat_history.append({"role": "user", "content": query, "timestamp": datetime.now()})
-                        response = st.session_state.hf_chatbot.query_database(query)
-                        st.session_state.chat_history.append({"role": "assistant", "content": response, "timestamp": datetime.now()})
-                        st.rerun()
+                for i, query in enumerate(basic_queries):
+                    if st.button(query, key=f"basic_{session_id}_{i}"):
+                        handle_chat_query(query)
             
             with col2:
-                st.markdown("**🔍 Semantic Queries:**")
-                if st.session_state.hf_processor.vector_store:
-                    semantic_queries = [
-                        "Find technology equipment purchases",
-                        "Show me office supplies",
-                        "Search for consulting services",
-                        "Find maintenance contracts"
-                    ]
-                    for i, query in enumerate(semantic_queries):
-                        if st.button(query, key=f"semantic_query_{i}", use_container_width=True):
-                            st.session_state.chat_history.append({"role": "user", "content": query, "timestamp": datetime.now()})
-                            response = st.session_state.hf_chatbot.query_database(query)
-                            st.session_state.chat_history.append({"role": "assistant", "content": response, "timestamp": datetime.now()})
-                            st.rerun()
-                else:
-                    st.info("Semantic search not available. Upload some invoices first!")
-        
-        # Alternative input method using text input and button
-        st.markdown("### ✍️ Ask a Question")
-        
-        col1, col2 = st.columns([4, 1])
-        
-        with col1:
-            user_input = st.text_input(
-                "Type your question here:",
-                placeholder="e.g., 'show me total spending' or 'find technology purchases'",
-                key="chat_text_input_tab"  # UNIQUE KEY ADDED
-            )
-        
-        with col2:
-            ask_button = st.button("🚀 Ask", type="primary", use_container_width=True, key="ask_button_tab")
-        
-        # Process the input
-        if ask_button and user_input:
-            # Add user message
-            st.session_state.chat_history.append({
-                "role": "user", 
-                "content": user_input,
-                "timestamp": datetime.now()
-            })
-            
-            # Get AI response
-            with st.spinner("🤖 AI is analyzing..."):
-                response = st.session_state.hf_chatbot.query_database(user_input)
-                
-                st.session_state.chat_history.append({
-                    "role": "assistant",
-                    "content": response,
-                    "timestamp": datetime.now()
-                })
-            
-            # Clear the input and rerun to show new messages
-            st.rerun()
+                st.markdown("**🔍 Advanced Queries:**")
+                advanced_queries = [
+                    "Find technology purchases",
+                    "Show office supplies",
+                    "Search consulting services",
+                    "Recent high-value invoices"
+                ]
+                for i, query in enumerate(advanced_queries):
+                    if st.button(query, key=f"advanced_{session_id}_{i}"):
+                        handle_chat_query(query)
         
-        # Clear chat button
+        # Clear chat
         if st.session_state.chat_history:
-            if st.button("🗑️ Clear Chat History", use_container_width=True, key="clear_chat_btn"):
+            if st.button("🗑️ Clear Chat", key=f"clear_chat_{session_id}"):
                 st.session_state.chat_history = []
                 st.rerun()
     
     # -------------------------------------------------------------------------
-    # TAB 3: ANALYTICS - WITH UNIQUE KEYS
+    # ANALYTICS SECTION
     # -------------------------------------------------------------------------
     
-    with tab3:
+    elif selected_tab == "📊 Analytics":
         st.header("📊 Analytics Dashboard")
         
         try:
@@ -1365,38 +1323,30 @@ def create_huggingface_app():
                     title="Invoice Amount Distribution",
                     labels={'amount': 'Amount (₹)', 'count': 'Number of Invoices'}
                 )
-                st.plotly_chart(fig_hist, use_container_width=True, key="amount_histogram")
+                st.plotly_chart(fig_hist, use_container_width=True)
                 
                 # Top suppliers
                 if df['supplier_name'].notna().any():
                     supplier_amounts = df.groupby('supplier_name')['amount'].sum().sort_values(ascending=False).head(10)
                     
-                    fig_suppliers = px.bar(
-                        x=supplier_amounts.values,
-                        y=supplier_amounts.index,
-                        orientation='h',
-                        title="Top 10 Suppliers by Total Amount",
-                        labels={'x': 'Total Amount (₹)', 'y': 'Supplier'}
-                    )
-                    st.plotly_chart(fig_suppliers, use_container_width=True, key="suppliers_chart")
-                
-                # Confidence analysis
-                fig_confidence = px.histogram(
-                    df,
-                    x='confidence',
-                    title="Extraction Confidence Distribution",
-                    labels={'confidence': 'Confidence Score', 'count': 'Number of Invoices'}
-                )
-                st.plotly_chart(fig_confidence, use_container_width=True, key="confidence_chart")
+                    if len(supplier_amounts) > 0:
+                        fig_suppliers = px.bar(
+                            x=supplier_amounts.values,
+                            y=supplier_amounts.index,
+                            orientation='h',
+                            title="Top 10 Suppliers by Total Amount",
+                            labels={'x': 'Total Amount (₹)', 'y': 'Supplier'}
+                        )
+                        st.plotly_chart(fig_suppliers, use_container_width=True)
         
         except Exception as e:
             st.error(f"Analytics error: {e}")
     
     # -------------------------------------------------------------------------
-    # TAB 4: DATA EXPLORER - WITH UNIQUE KEYS
+    # DATA EXPLORER SECTION
     # -------------------------------------------------------------------------
     
-    with tab4:
+    elif selected_tab == "📋 Data Explorer":
         st.header("📋 Data Explorer")
         
         try:
@@ -1407,7 +1357,7 @@ def create_huggingface_app():
                 st.info("📊 No data available. Upload some invoices first.")
                 return
             
-            # Convert to DataFrame for display
+            # Convert to DataFrame
             df_data = []
             for inv in invoices:
                 df_data.append({
@@ -1424,19 +1374,19 @@ def create_huggingface_app():
             
             df = pd.DataFrame(df_data)
             
-            # Filters with unique keys
+            # Filters
             col1, col2, col3 = st.columns(3)
             
             with col1:
                 suppliers = ['All'] + sorted(df['Supplier'].dropna().unique().tolist())
-                selected_supplier = st.selectbox("Filter by Supplier", suppliers, key="supplier_filter")
+                selected_supplier = st.selectbox("Filter by Supplier", suppliers, key=f"supplier_filter_{session_id}")
             
             with col2:
                 methods = ['All'] + sorted(df['Method'].dropna().unique().tolist())
-                selected_method = st.selectbox("Filter by Method", methods, key="method_filter")
+                selected_method = st.selectbox("Filter by Method", methods, key=f"method_filter_{session_id}")
             
             with col3:
-                min_amount = st.number_input("Min Amount", min_value=0.0, value=0.0, key="amount_filter")
+                min_amount = st.number_input("Min Amount", min_value=0.0, value=0.0, key=f"amount_filter_{session_id}")
             
             # Apply filters
             filtered_df = df.copy()
@@ -1447,33 +1397,32 @@ def create_huggingface_app():
             if min_amount > 0:
                 filtered_df = filtered_df[filtered_df['Amount'] >= min_amount]
             
-            # Display filtered data
+            # Display data
             st.dataframe(
                 filtered_df,
                 use_container_width=True,
                 column_config={
                     "Amount": st.column_config.NumberColumn("Amount", format="₹%.2f"),
                     "Confidence": st.column_config.ProgressColumn("Confidence", min_value=0, max_value=1)
-                },
-                key="data_explorer_table"  # UNIQUE KEY ADDED
+                }
             )
             
-            # Export options with unique keys
+            # Export options
             col1, col2 = st.columns(2)
             
             with col1:
-                if st.button("📥 Export CSV", use_container_width=True, key="export_csv_btn"):
+                if st.button("📥 Export CSV", key=f"export_csv_{session_id}"):
                     csv_data = filtered_df.to_csv(index=False)
                     st.download_button(
                         "Download CSV",
                         csv_data,
                         f"invoices_{datetime.now().strftime('%Y%m%d_%H%M')}.csv",
                         "text/csv",
-                        key="download_csv_btn"
+                        key=f"download_csv_{session_id}"
                     )
             
             with col2:
-                if st.button("📄 Export JSON", use_container_width=True, key="export_json_btn"):
+                if st.button("📄 Export JSON", key=f"export_json_{session_id}"):
                     filtered_invoices = [inv for inv in invoices 
                                        if inv.get('invoice_number') in filtered_df['Invoice Number'].values]
                     
@@ -1488,36 +1437,99 @@ def create_huggingface_app():
                         json.dumps(export_data, indent=2),
                         f"invoices_{datetime.now().strftime('%Y%m%d_%H%M')}.json",
                         "application/json",
-                        key="download_json_btn"
+                        key=f"download_json_{session_id}"
                     )
         
         except Exception as e:
             st.error(f"Data explorer error: {e}")
     
     # -------------------------------------------------------------------------
-    # GLOBAL CHAT INPUT - OUTSIDE TABS WITH UNIQUE KEY
+    # GLOBAL CHAT INPUT (Outside sections)
     # -------------------------------------------------------------------------
     
-    # Add some spacing
     st.markdown("---")
+    st.markdown("### 💬 Quick Chat (Works from any section)")
+    
+    # Global chat input with unique key
+    global_query = st.chat_input("Ask about your invoices...", key=f"global_chat_{session_id}")
     
-    # Global chat input that works from any tab
-    st.markdown("### 💬 Quick Chat (Available from any tab)")
+    if global_query:
+        handle_chat_query(global_query, show_response=True)
+    
+    # Footer
+    st.markdown("---")
+    st.markdown("""
+    <div style="text-align: center; color: #666;">
+        <p>🚀 <strong>AI Invoice Processing System</strong> - Optimized for Hugging Face Spaces</p>
+        <p>Built with ❤️ using Streamlit, Transformers, and AI</p>
+    </div>
+    """, unsafe_allow_html=True)
+
+# ===============================================================================
+# HELPER FUNCTIONS
+# ===============================================================================
+
+def process_files(uploaded_files, session_id):
+    """Process uploaded files"""
+    progress_bar = st.progress(0)
+    status_container = st.container()
+    results_container = st.container()
     
-    # Use chat_input outside of tabs with unique key - this will work
-    global_user_query = st.chat_input("Ask about your invoices from anywhere...", key="global_chat_input")
+    successful = 0
+    failed = 0
     
-    if global_user_query:
-        # Add user message
-        st.session_state.chat_history.append({
-            "role": "user", 
-            "content": global_user_query,
-            "timestamp": datetime.now()
-        })
-        
-        # Get AI response
+    for i, uploaded_file in enumerate(uploaded_files):
+        progress_bar.progress((i + 1) / len(uploaded_files))
+        
+        with status_container:
+            st.info(f"Processing: {uploaded_file.name}")
+        
+        try:
+            # Process file
+            result = st.session_state.hf_processor.process_uploaded_file(uploaded_file)
+            
+            with results_container:
+                if result and result.invoice_number:
+                    successful += 1
+                    with st.expander(f"✅ {uploaded_file.name}", expanded=False):
+                        col1, col2 = st.columns(2)
+                        with col1:
+                            st.write(f"**Invoice #:** {result.invoice_number}")
+                            st.write(f"**Supplier:** {result.supplier_name}")
+                            st.write(f"**Amount:** ₹{result.amount:.2f}")
+                        with col2:
+                            st.write(f"**Date:** {result.date}")
+                            st.write(f"**Method:** {result.processing_method}")
+                            st.write(f"**Confidence:** {result.extraction_confidence:.1%}")
+                else:
+                    failed += 1
+                    st.warning(f"⚠️ Could not extract data from {uploaded_file.name}")
+        
+        except Exception as e:
+            failed += 1
+            with results_container:
+                st.error(f"❌ Error processing {uploaded_file.name}: {str(e)[:100]}")
+    
+    # Final status
+    with status_container:
+        st.success(f"✅ Processing complete! {successful} successful, {failed} failed")
+    
+    if successful > 0:
+        st.balloons()
+
+def handle_chat_query(query, show_response=False):
+    """Handle chat query"""
+    # Add user message
+    st.session_state.chat_history.append({
+        "role": "user", 
+        "content": query,
+        "timestamp": datetime.now()
+    })
+    
+    # Get AI response
+    try:
         with st.spinner("🤖 AI is analyzing..."):
-            response = st.session_state.hf_chatbot.query_database(global_user_query)
+            response = st.session_state.hf_chatbot.query_database(query)
             
             st.session_state.chat_history.append({
                 "role": "assistant",
@@ -1525,22 +1537,56 @@ def create_huggingface_app():
                 "timestamp": datetime.now()
             })
         
-        # Show the response immediately
-        with st.chat_message("assistant"):
-            st.markdown(response)
+        # Show response if requested
+        if show_response:
+            with st.chat_message("assistant"):
+                st.markdown(response)
+            st.info("💡 Switch to the 'AI Chat' section to see full conversation history!")
         
-        # Suggest switching to chat tab if not already there
-        st.info("💡 Switch to the 'AI Chat' tab to see full conversation history!")
-    
-    # Footer
-    st.markdown("---")
-    st.markdown("""
-    <div style="text-align: center; color: #666; margin-top: 2rem;">
-        <p>🚀 <strong>AI Invoice Processing System</strong> - Optimized for Hugging Face Spaces</p>
-        <p>Built with ❤️ using Streamlit, Transformers, and AI</p>
-    </div>
-    """, unsafe_allow_html=True)
+        st.rerun()
+        
+    except Exception as e:
+        st.error(f"Chat error: {e}")
+
+# ===============================================================================
+# MAIN ENTRY POINT
+# ===============================================================================
+
+def main():
+    """Main entry point for Hugging Face Spaces"""
+    try:
+        # Import required classes
+        from enhanced_invoice_system_part1 import IS_HF_SPACE
+        
+        # Display environment info
+        if IS_HF_SPACE:
+            st.sidebar.info("🤗 Running on Hugging Face Spaces")
+        
+        # Create and run the app
+        create_huggingface_app()
+        
+    except ImportError as e:
+        st.error(f"""
+        ## 🚨 Import Error
+        
+        Missing required modules: {e}
+        
+        Please ensure all files are uploaded to your Hugging Face Space:
+        - enhanced_invoice_system_part1.py
+        - enhanced_invoice_system_part2.py (this file)
+        """)
+        
+    except Exception as e:
+        st.error(f"""
+        ## 🚨 Application Error
+        
+        {e}
+        
+        Please refresh the page or check the logs for more details.
+        """)
 
+if __name__ == "__main__":
+    main()
 # ===============================================================================
 # MAIN APPLICATION ENTRY POINT
 # ===============================================================================