app.py

import streamlit as st
import tempfile
import os
import json
from typing import List, Dict, Any, Optional, Tuple
import traceback

# Import our modules
from src.document_processor import DocumentProcessor
from src.llm_extractor import LLMExtractor
from src.graph_builder import GraphBuilder
from src.visualizer import GraphVisualizer
from config.settings import Config

# Page config
st.set_page_config(
    page_title="Knowledge Graph Extraction",
    page_icon="🕸️",
    layout="wide"
)

# Initialize components
@st.cache_resource
def initialize_components():
    config = Config()
    doc_processor = DocumentProcessor()
    llm_extractor = LLMExtractor()
    graph_builder = GraphBuilder()
    visualizer = GraphVisualizer()
    return config, doc_processor, llm_extractor, graph_builder, visualizer

config, doc_processor, llm_extractor, graph_builder, visualizer = initialize_components()

def process_uploaded_files(uploaded_files, api_key, batch_mode, layout_type, 
                          show_labels, show_edge_labels, min_importance, entity_types_filter):
    """Process uploaded files and extract knowledge graph."""
    
    try:
        # Update API key
        if api_key.strip():
            config.OPENROUTER_API_KEY = api_key.strip()
            llm_extractor.config.OPENROUTER_API_KEY = api_key.strip()
            llm_extractor.headers["Authorization"] = f"Bearer {api_key.strip()}"
        
        if not config.OPENROUTER_API_KEY:
            st.error("❌ OpenRouter API key is required")
            return None
        
        if not uploaded_files:
            st.error("❌ Please upload at least one file")
            return None
        
        progress_bar = st.progress(0)
        status_text = st.empty()
        
        status_text.text("Loading documents...")
        progress_bar.progress(0.1)
        
        # Save uploaded files to temporary location
        file_paths = []
        for uploaded_file in uploaded_files:
            # Create temporary file
            with tempfile.NamedTemporaryFile(delete=False, suffix=f"_{uploaded_file.name}") as tmp_file:
                tmp_file.write(uploaded_file.getvalue())
                file_paths.append(tmp_file.name)
        
        # Process documents
        doc_results = doc_processor.process_documents(file_paths, batch_mode)
        
        # Clean up temporary files
        for file_path in file_paths:
            try:
                os.unlink(file_path)
            except:
                pass
        
        # Check for errors
        failed_files = [r for r in doc_results if r['status'] == 'error']
        if failed_files:
            error_msg = "Failed to process files:\n" + "\n".join([f"- {r['file_path']}: {r['error']}" for r in failed_files])
            if len(failed_files) == len(doc_results):
                st.error(f"❌ {error_msg}")
                return None
        
        status_text.text("Extracting entities and relationships...")
        progress_bar.progress(0.3)
        
        # Extract entities and relationships
        all_entities = []
        all_relationships = []
        extraction_errors = []
        
        for doc_result in doc_results:
            if doc_result['status'] == 'success':
                extraction_result = llm_extractor.process_chunks(doc_result['chunks'])
                
                if extraction_result.get('errors'):
                    extraction_errors.extend(extraction_result['errors'])
                
                all_entities.extend(extraction_result.get('entities', []))
                all_relationships.extend(extraction_result.get('relationships', []))
        
        if not all_entities:
            error_msg = "No entities extracted from documents"
            if extraction_errors:
                error_msg += f"\nExtraction errors: {'; '.join(extraction_errors[:3])}"
            st.error(f"❌ {error_msg}")
            return None
        
        status_text.text("Building knowledge graph...")
        progress_bar.progress(0.6)
        
        # Build graph
        graph = graph_builder.build_graph(all_entities, all_relationships)
        
        if not graph.nodes():
            st.error("❌ No valid knowledge graph could be built")
            return None
        
        status_text.text("Applying filters...")
        progress_bar.progress(0.7)
        
        # Apply filters
        filtered_graph = graph
        if entity_types_filter:
            filtered_graph = graph_builder.filter_graph(
                entity_types=entity_types_filter,
                min_importance=min_importance
            )
        elif min_importance > 0:
            filtered_graph = graph_builder.filter_graph(min_importance=min_importance)
        
        if not filtered_graph.nodes():
            st.error("❌ No entities remain after applying filters")
            return None
        
        status_text.text("Generating visualizations...")
        progress_bar.progress(0.8)
        
        # Generate graph visualization
        graph_image_path = visualizer.visualize_graph(
            filtered_graph,
            layout_type=layout_type,
            show_labels=show_labels,
            show_edge_labels=show_edge_labels
        )
        
        # Get statistics
        stats = graph_builder.get_graph_statistics()
        stats_summary = visualizer.create_statistics_summary(filtered_graph, stats)
        
        # Get entity list
        entity_list = visualizer.create_entity_list(filtered_graph)
        
        # Get central nodes
        central_nodes = graph_builder.get_central_nodes()
        central_nodes_text = "## Most Central Entities\n\n"
        for i, (node, score) in enumerate(central_nodes, 1):
            central_nodes_text += f"{i}. **{node}** (centrality: {score:.3f})\n"
        
        status_text.text("Complete!")
        progress_bar.progress(1.0)
        
        # Success message
        success_msg = f"✅ Successfully processed {len([r for r in doc_results if r['status'] == 'success'])} document(s)"
        if failed_files:
            success_msg += f"\n⚠️ {len(failed_files)} file(s) failed to process"
        if extraction_errors:
            success_msg += f"\n⚠️ {len(extraction_errors)} extraction error(s) occurred"
        
        return {
            'success_msg': success_msg,
            'graph_image_path': graph_image_path,
            'stats_summary': stats_summary,
            'entity_list': entity_list,
            'central_nodes_text': central_nodes_text,
            'graph': filtered_graph
        }
        
    except Exception as e:
        st.error(f"❌ Error: {str(e)}")
        st.error(f"Full traceback:\n{traceback.format_exc()}")
        return None

# Main app
def main():
    st.title("🕸️ Knowledge Graph Extraction")
    st.markdown("""
    Upload documents and extract knowledge graphs using LLMs via OpenRouter.
    Supports PDF, TXT, DOCX, and JSON files.
    """)
    
    # Sidebar for configuration
    with st.sidebar:
        st.header("📁 Document Upload")
        uploaded_files = st.file_uploader(
            "Choose files",
            type=['pdf', 'txt', 'docx', 'json'],
            accept_multiple_files=True
        )
        
        batch_mode = st.checkbox(
            "Batch Processing Mode",
            value=False,
            help="Process multiple files together"
        )
        
        st.header("🔑 API Configuration")
        api_key = st.text_input(
            "OpenRouter API Key",
            type="password",
            placeholder="Enter your OpenRouter API key",
            help="Get your key at openrouter.ai"
        )
        
        st.header("🎛️ Visualization Settings")
        layout_type = st.selectbox(
            "Layout Algorithm",
            options=visualizer.get_layout_options(),
            index=0
        )
        
        show_labels = st.checkbox("Show Node Labels", value=True)
        show_edge_labels = st.checkbox("Show Edge Labels", value=False)
        
        st.header("🔍 Filtering Options")
        min_importance = st.slider(
            "Minimum Entity Importance",
            min_value=0.0,
            max_value=1.0,
            value=0.3,
            step=0.1
        )
        
        entity_types_filter = st.multiselect(
            "Entity Types Filter",
            options=[],
            help="Filter will be populated after processing"
        )
        
        process_button = st.button("🚀 Extract Knowledge Graph", type="primary")
    
    # Main content area
    if process_button and uploaded_files:
        with st.spinner("Processing..."):
            result = process_uploaded_files(
                uploaded_files, api_key, batch_mode, layout_type,
                show_labels, show_edge_labels, min_importance, entity_types_filter
            )
        
        if result:
            # Store results in session state
            st.session_state['result'] = result
            
            # Display success message
            st.success(result['success_msg'])
            
            # Create tabs for results
            tab1, tab2, tab3, tab4 = st.tabs(["📈 Graph Visualization", "📋 Statistics", "📝 Entities", "🎯 Central Nodes"])
            
            with tab1:
                if result['graph_image_path'] and os.path.exists(result['graph_image_path']):
                    st.image(result['graph_image_path'], caption="Knowledge Graph", use_column_width=True)
                else:
                    st.error("Failed to generate graph visualization")
            
            with tab2:
                st.markdown(result['stats_summary'])
            
            with tab3:
                st.markdown(result['entity_list'])
            
            with tab4:
                st.markdown(result['central_nodes_text'])
            
            # Export options
            st.header("💾 Export Options")
            col1, col2 = st.columns(2)
            
            with col1:
                export_format = st.selectbox(
                    "Export Format",
                    options=["json", "graphml", "gexf"],
                    index=0
                )
            
            with col2:
                if st.button("📥 Export Graph"):
                    try:
                        export_data = graph_builder.export_graph(export_format)
                        st.text_area("Export Data", value=export_data, height=300)
                        
                        # Download button
                        st.download_button(
                            label=f"Download {export_format.upper()} file",
                            data=export_data,
                            file_name=f"knowledge_graph.{export_format}",
                            mime="application/octet-stream"
                        )
                    except Exception as e:
                        st.error(f"Export failed: {str(e)}")
    
    elif process_button and not uploaded_files:
        st.warning("Please upload at least one file before processing.")
    
    # Instructions
    st.header("📚 Instructions")
    
    with st.expander("How to use this app"):
        st.markdown("""
        1. **Upload Documents**: Select one or more files (PDF, TXT, DOCX, JSON) using the file uploader in the sidebar
        2. **Enter API Key**: Get a free API key from [OpenRouter](https://openrouter.ai) and enter it in the sidebar
        3. **Configure Settings**: Adjust visualization and filtering options in the sidebar
        4. **Extract Graph**: Click the "Extract Knowledge Graph" button and wait for processing
        5. **Explore Results**: View the graph, statistics, and entity details in the tabs
        6. **Export**: Download the graph data in various formats
        """)
    
    with st.expander("Features"):
        st.markdown("""
        - **Multi-format Support**: PDF, TXT, DOCX, JSON files
        - **Batch Processing**: Process multiple documents together
        - **Smart Extraction**: Uses LLM to identify important entities and relationships
        - **Interactive Filtering**: Filter by entity type and importance
        - **Multiple Layouts**: Various graph layout algorithms
        - **Export Options**: JSON, GraphML, GEXF formats
        - **Free Models**: Uses cost-effective OpenRouter models
        """)
    
    with st.expander("Notes"):
        st.markdown("""
        - File size limit: 10MB per file
        - Free OpenRouter models are used to minimize costs
        - Processing time depends on document size and complexity
        """)

if __name__ == "__main__":
    main()