app.py

"""
Main Streamlit Application - GEO SEO AI Optimizer
Entry point for the application with UI components
"""

import streamlit as st
import os
import tempfile
import json
from typing import Dict, Any, List
import time  # Add this if not present

# Import our custom modules
from utils.parser import PDFParser, TextParser, WebpageParser
from utils.scorer import GEOScorer
from utils.optimizer import ContentOptimizer
from utils.chunker import VectorChunker
from utils.export import ResultExporter

# Import LangChain components
from langchain_groq import ChatGroq
from langchain_community.embeddings import HuggingFaceEmbeddings

class GEOSEOApp:
    """Main application class that orchestrates all components"""
    
    def __init__(self):
        self.setup_config()
        self.setup_models()
        self.setup_parsers()
        self.setup_components()
    
    def setup_config(self):
        """Initialize configuration and API keys"""
        self.groq_api_key = os.getenv("GROQ_API_KEY", "your-groq-api-key")
        self.hf_api_key = os.getenv("HUGGINGFACE_API_KEY", "your-huggingface-api-key")
        
        # Create data directory if it doesn't exist
        os.makedirs("data/uploaded_files", exist_ok=True)
    
    def setup_models(self):
        """Initialize LLM and embedding models"""
        self.llm = ChatGroq(
            api_key=self.groq_api_key,
            model_name="llama3-8b-8192",
            temperature=0.1
        )
        
        # Updated embeddings initialization without the `device` parameter
        self.embeddings = HuggingFaceEmbeddings(
            model_name="sentence-transformers/all-MiniLM-L6-v2",
            model_kwargs={"device": "cpu"},
            cache_folder="./hf_cache",
        )
    
    def setup_parsers(self):
        """Initialize content parsers"""
        self.pdf_parser = PDFParser()
        self.text_parser = TextParser()
        self.webpage_parser = WebpageParser()
    
    def setup_components(self):
        """Initialize processing components"""
        self.geo_scorer = GEOScorer(self.llm)
        self.content_optimizer = ContentOptimizer(self.llm)
        self.vector_chunker = VectorChunker(self.embeddings)
        self.result_exporter = ResultExporter()
    
    def run(self):
        """Main application runner"""
        st.set_page_config(
            page_title="GEO SEO AI Optimizer", 
            page_icon="🚀", 
            layout="wide"
        )
        
        st.title("🚀 GEO SEO AI Optimizer")
        st.markdown("*Optimize your content for AI search engines and LLM systems*")
        
        # Sidebar
        self.render_sidebar()
        
        # Main tabs
        tab1, tab2, tab3 = st.tabs([
            "🌐 Website GEO Analysis",
            "🔧 Content Enhancement", 
            "📄 Document Q&A", 
        ])
        
        with tab1:
            self.render_website_analysis_tab()
        
        with tab2:
            self.render_content_enhancement_tab()
        
        with tab3:
            self.render_document_qa_tab()
    
    def render_sidebar(self):
        """Render sidebar with information and controls"""
        st.sidebar.title("🛠️ GEO Tools")
        st.sidebar.markdown("- 📄 Document Q&A with RAG")
        st.sidebar.markdown("- 🔧 Content Enhancement")
        st.sidebar.markdown("- 🌐 Website GEO Analysis")
        st.sidebar.markdown("- 📊 AI-First SEO Scoring")
        
        st.sidebar.markdown("---")
        st.sidebar.markdown("### 🔧 Configuration")
        st.sidebar.markdown("Set your API keys:")
        st.sidebar.code("export GROQ_API_KEY='your-key'")
        
        st.sidebar.markdown("---")
        st.sidebar.markdown("### 📖 GEO Metrics")
        st.sidebar.markdown("**AI Search Visibility**: How likely AI engines will surface your content")
        st.sidebar.markdown("**Query Intent Matching**: How well content matches user queries")
        st.sidebar.markdown("**Conversational Readiness**: Suitability for AI chat responses")
        st.sidebar.markdown("**Citation Worthiness**: Probability of being cited by AI")
        
        st.sidebar.markdown("---")
        st.sidebar.markdown("### ℹ️ Components")
        st.sidebar.markdown("- **Parser**: Extract content from various sources")
        st.sidebar.markdown("- **Scorer**: Analyze GEO performance")
        st.sidebar.markdown("- **Optimizer**: Enhance content for AI")
        st.sidebar.markdown("- **Chunker**: Create vector embeddings")
        st.sidebar.markdown("- **Exporter**: Generate reports")
    
    def render_document_qa_tab(self):
        """Render Document Q&A tab"""
        st.header("📄 Document Question Answering")
        st.markdown("Upload documents or paste text to ask questions using RAG.")
        
        # File upload
        uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
        
        # Text input
        pasted_text = st.text_area("Or paste text directly:", height=150)
        
        # Question input
        user_query = st.text_input("Ask a question about the content:")
        
        # Submit button
        if st.button("🔍 Ask Question", key="qa_submit"):
            if not user_query.strip():
                st.warning("Please enter a question.")
                return
            
            try:
                # Parse content
                documents = []
                
                if uploaded_file:
                    with st.spinner("Processing PDF..."):
                        # Save uploaded file temporarily
                        temp_path = self.save_uploaded_file(uploaded_file)
                        documents = self.pdf_parser.parse(temp_path)
                        os.unlink(temp_path)  # Clean up
                
                elif pasted_text.strip():
                    with st.spinner("Processing text..."):
                        documents = self.text_parser.parse(pasted_text)
                
                else:
                    st.warning("Please upload a PDF or paste some text.")
                    return
                
                # Create vector store and answer question
                with st.spinner("Creating embeddings and searching..."):
                    qa_chain = self.vector_chunker.create_qa_chain(documents, self.llm)
                    result = qa_chain({"query": user_query})
                
                # Display results
                st.markdown("### 💬 Answer")
                st.write(result["result"])
                
                # Show sources
                with st.expander("📄 Source Documents"):
                    for i, doc in enumerate(result.get("source_documents", [])):
                        st.write(f"**Source {i+1}:**")
                        content = doc.page_content
                        st.write(content[:500] + "..." if len(content) > 500 else content)
                        if hasattr(doc, 'metadata') and doc.metadata:
                            st.write(f"*Metadata: {doc.metadata}*")
                        st.write("---")
            
            except Exception as e:
                st.error(f"An error occurred: {str(e)}")
    
    def render_content_enhancement_tab(self):
        """Render Content Enhancement tab with optimization type selector"""
        st.header("🔧 Content Enhancement")
        st.markdown("Analyze and optimize your content for better AI/LLM performance.")
        
        # Content input
        input_text = st.text_area(
            "Enter content to analyze and enhance:", 
            height=200, 
            key="enhancement_input"
        )
        
        # Optimization type selector
        st.markdown("### ⚙️ Optimization Settings")
        col1, col2 = st.columns(2)
        
        with col1:
            optimization_type = st.selectbox(
                "Select Optimization Type:",
                options=[
                    "standard", 
                    "seo", 
                    # "competitive", 
                    # "voice_search", 
                    # "batch_optimize",
                    # "content_variations",
                    "readability_analysis",
                    # "entity_extraction"
                ],
                format_func=lambda x: {
                    "standard": "🔧 Standard Enhancement",
                    "seo": "🌐 SEO-Focused Optimization", 
                    # "competitive": "📊 Competitive Analysis",
                    # "voice_search": "🎤 Voice Search Optimization",
                    # "batch_optimize": "📦 Batch Optimization",
                    # "content_variations": "🔄 Content Variations",
                    "readability_analysis": "📖 Readability Analysis",
                    # "entity_extraction": "🏷️ Entity Extraction"
                }[x],
                index=0,
                help="Choose the type of optimization to apply to your content"
            )
        
        with col2:
            # Additional options based on optimization type
            if optimization_type in ["standard", "seo", "competitive", "readability_analysis"]:
                analyze_only = st.checkbox("Analysis only (no rewriting)", value=False)
                include_keywords = st.checkbox("Include keyword suggestions", value=True)
            # elif optimization_type == "batch_optimize":
            #     st.info("For batch optimization, separate multiple content pieces with '---' in the text area above")
            # elif optimization_type == "content_variations":
            #     num_variations = st.slider("Number of variations", min_value=1, max_value=5, value=3)
            else:
                analyze_only = False
                include_keywords = True
                # num_variations = 3
        
        # Show description based on optimization type
        optimization_descriptions = {
            "standard": "General content enhancement focusing on clarity, structure, and AI answerability.",
            "seo": "SEO-focused optimization for AI search engines with semantic keyword analysis.",
            # "competitive": "Competitive analysis against AI search best practices with gap identification.",
            # "voice_search": "Optimization for voice search and conversational AI systems.",
            # "batch_optimize": "Process multiple content pieces simultaneously.",
            # "content_variations": "Generate multiple optimized variations of the same content.",
            "readability_analysis": "Detailed readability analysis specifically for AI systems.",
            # "entity_extraction": "Extract key entities, topics, and concepts for optimization insights."
        }
        
        st.info(f"**{optimization_descriptions[optimization_type]}**")
        
        # Submit button
        if st.button("🚀 Process Content", key="enhancement_submit"):
            if not input_text.strip():
                st.warning("Please enter some content to analyze.")
                return
            
            try:
                with st.spinner(f"Processing content with {optimization_type} optimization..."):
                    # Handle different optimization types
                    if optimization_type == "standard":
                        result = self.content_optimizer.optimize_content(
                            input_text,
                            analyze_only=analyze_only,
                            include_keywords=include_keywords,
                            optimization_type="standard"
                        )
                    
                    elif optimization_type == "seo":
                        result = self.content_optimizer.optimize_content(
                            input_text,
                            analyze_only=analyze_only,
                            include_keywords=include_keywords,
                            optimization_type="seo"
                        )
                    
                    # elif optimization_type == "competitive":
                    #     result = self.content_optimizer.optimize_content(
                    #         input_text,
                    #         optimization_type="competitive"
                    #     )
                    
                    # elif optimization_type == "voice_search":
                    #     result = self.content_optimizer.optimize_for_voice_search(input_text)
                    
                    # elif optimization_type == "batch_optimize":
                    #     # Split content by '---' separator
                    #     content_pieces = [piece.strip() for piece in input_text.split('---') if piece.strip()]
                    #     if len(content_pieces) > 1:
                    #         result = self.content_optimizer.batch_optimize_content(content_pieces)
                    #     else:
                    #         st.warning("For batch optimization, please separate content pieces with '---'")
                    #         return
                    
                    # elif optimization_type == "content_variations":
                    #     result = self.content_optimizer.generate_content_variations(
                    #         input_text, 
                    #         num_variations=num_variations
                    #     )
                    
                    elif optimization_type == "readability_analysis":
                        result = self.content_optimizer.analyze_content_readability(input_text)
                    
                    # elif optimization_type == "entity_extraction":
                    #     result = self.content_optimizer.extract_key_entities(input_text)
                
                if result.get("error"):
                    st.error(f"Processing failed: {result['error']}")
                    return
                
                # Display results based on optimization type
                self.display_enhancement_results(result, optimization_type, input_text)
                
            except Exception as e:
                st.error(f"An error occurred: {str(e)}")

    def display_enhancement_results(self, result, optimization_type, original_text):
        """Display results based on optimization type"""
        st.success(f"{optimization_type.title()} optimization completed successfully!")
        
        # if optimization_type == "batch_optimize":
        #     self.display_batch_results(result)
        # elif optimization_type == "content_variations":
        #     self.display_variation_results(result)
        if optimization_type == "readability_analysis":
            self.display_readability_results(result)
        # elif optimization_type == "entity_extraction":
        #     self.display_entity_results(result)
        # elif optimization_type == "voice_search":
        #     self.display_voice_search_results(result)
        else:
            self.display_standard_results(result, optimization_type)
        
        # Export functionality
        self.display_export_options(result, optimization_type, original_text)

    def display_standard_results(self, result, optimization_type):
        """Display results for standard, SEO, and competitive optimizations"""
        st.markdown("### 📊 Analysis Results")
        
        # Show scores if available
        scores = result.get("scores", {})
        if scores:
            col1, col2, col3 = st.columns(3)
            
            with col1:
                clarity = scores.get("clarity", 0)
                st.metric("Clarity", f"{clarity}/10")
            
            with col2:
                structure = scores.get("structuredness", 0)
                st.metric("Structure", f"{structure}/10")
            
            with col3:
                answerability = scores.get("answerability", 0)
                st.metric("Answerability", f"{answerability}/10")
        
        # Show SEO analysis if available
        if "seo_analysis" in result:
            st.markdown("#### 🌐 SEO Analysis")
            seo_data = result["seo_analysis"]
            if "readability_score" in seo_data:
                st.metric("Readability Score", f"{seo_data['readability_score']}/10")
            if "semantic_gaps" in seo_data:
                st.write("**Semantic Gaps:**", ", ".join(seo_data["semantic_gaps"]))
        
        # Show competitive analysis if available
        if "competitive_analysis" in result:
            st.markdown("#### 📊 Competitive Analysis")
            comp_data = result["competitive_analysis"]
            for key, value in comp_data.items():
                if isinstance(value, list):
                    st.write(f"**{key.replace('_', ' ').title()}:**", ", ".join(value))
                else:
                    st.write(f"**{key.replace('_', ' ').title()}:**", value)
        
        # Show keywords
        keywords = result.get("keywords", [])
        if keywords:
            st.markdown("#### 🔑 Key Terms")
            st.write(", ".join(keywords))
        
        # Show optimized content
        optimized_content = result.get("optimized_text") or result.get("optimized_content", {}).get("enhanced_content", "")
        if optimized_content:
            st.markdown("#### ✨ Optimized Content")
            st.text_area(
                "Enhanced version:", 
                value=optimized_content, 
                height=200, 
                key="optimized_output"
            )
        
        # Show recommendations
        recommendations = result.get("recommendations", [])
        if recommendations:
            st.markdown("#### 💡 Recommendations")
            for i, rec in enumerate(recommendations, 1):
                st.write(f"**{i}.** {rec}")

    def display_batch_results(self, results):
        """Display batch optimization results"""
        st.markdown("### 📦 Batch Processing Results")
        
        successful_results = [r for r in results if not r.get('error')]
        failed_results = [r for r in results if r.get('error')]
        
        col1, col2, col3 = st.columns(3)
        with col1:
            st.metric("Total Pieces", len(results))
        with col2:
            st.metric("Successful", len(successful_results))
        with col3:
            st.metric("Failed", len(failed_results))
        
        # Show individual results
        for result in results:
            idx = result.get('batch_index', 0)
            st.markdown(f"#### Content Piece {idx + 1}")
            
            if result.get('error'):
                st.error(f"Processing failed: {result['error']}")
            else:
                # Show scores
                scores = result.get("scores", {})
                if scores:
                    col1, col2, col3 = st.columns(3)
                    with col1:
                        st.metric("Clarity", f"{scores.get('clarity', 0)}/10")
                    with col2:
                        st.metric("Structure", f"{scores.get('structuredness', 0)}/10") 
                    with col3:
                        st.metric("Answerability", f"{scores.get('answerability', 0)}/10")
                
                # Show optimized content if available
                optimized = result.get("optimized_text", "")
                if optimized:
                    with st.expander("View optimized content"):
                        st.text_area("", value=optimized, height=150, key=f"batch_output_{idx}")
            
            st.write("---")

    def display_variation_results(self, variations):
        """Display content variation results"""
        st.markdown("### 🔄 Content Variations")
        
        for i, variation in enumerate(variations):
            if variation.get('error'):
                st.error(f"Variation {i+1} failed: {variation['error']}")
                continue
            
            variation_type = variation.get('variation_type', f'Variation {i+1}')
            st.markdown(f"#### {variation_type.title()} Version")
            
            # Show variation details
            target_use_case = variation.get('target_use_case', '')
            if target_use_case:
                st.info(f"**Target Use Case:** {target_use_case}")
            
            # Show key changes
            key_changes = variation.get('key_changes', [])
            if key_changes:
                st.write("**Key Changes:**")
                for change in key_changes:
                    st.write(f"• {change}")
            
            # Show optimized content
            optimized_content = variation.get('optimized_content', '')
            if optimized_content:
                st.text_area(
                    f"{variation_type} content:", 
                    value=optimized_content, 
                    height=150, 
                    key=f"variation_{i}"
                )
            
            st.write("---")

    def display_readability_results(self, result):
        """Display readability analysis results"""
        st.markdown("### 📖 Readability Analysis")
        
        # Basic metrics
        basic_metrics = result.get('basic_metrics', {})
        if basic_metrics:
            st.markdown("#### 📊 Basic Metrics")
            col1, col2, col3, col4 = st.columns(4)
            
            with col1:
                st.metric("Total Words", basic_metrics.get('total_words', 0))
            with col2:
                st.metric("Sentences", basic_metrics.get('total_sentences', 0))
            with col3:
                st.metric("Paragraphs", basic_metrics.get('total_paragraphs', 0))
            with col4:
                st.metric("AI Readability", f"{result.get('ai_readability_score', 0)}/10")
        
        # Complexity indicators
        complexity = result.get('complexity_indicators', {})
        if complexity:
            st.markdown("#### 🎯 Complexity Analysis")
            col1, col2 = st.columns(2)
            
            with col1:
                st.metric("Long Sentences", f"{complexity.get('long_sentences_percentage', 0):.1f}%")
            with col2:
                st.metric("Complex Words", f"{complexity.get('complex_words_percentage', 0):.1f}%")
        
        # Recommendations
        recommendations = result.get('recommendations', [])
        if recommendations:
            st.markdown("#### 💡 Readability Recommendations")
            for i, rec in enumerate(recommendations, 1):
                st.write(f"**{i}.** {rec}")

    def display_entity_results(self, result):
        """Display entity extraction results"""
        st.markdown("### 🏷️ Entity Analysis")
        
        # Named entities
        named_entities = result.get('named_entities', [])
        if named_entities:
            st.markdown("#### 👥 Named Entities")
            st.write(", ".join(named_entities))
        
        # Key topics
        key_topics = result.get('key_topics', [])
        if key_topics:
            st.markdown("#### 📋 Key Topics")
            st.write(", ".join(key_topics))
        
        # Technical terms
        technical_terms = result.get('technical_terms', [])
        if technical_terms:
            st.markdown("#### 🔧 Technical Terms")
            st.write(", ".join(technical_terms))
        
        # Semantic keywords
        semantic_keywords = result.get('semantic_keywords', [])
        if semantic_keywords:
            st.markdown("#### 🔍 Semantic Keywords")
            st.write(", ".join(semantic_keywords))
        
        # Question opportunities
        questions = result.get('question_opportunities', [])
        if questions:
            st.markdown("#### ❓ Question Opportunities")
            for q in questions:
                st.write(f"• {q}")

    # def display_voice_search_results(self, result):
    #     """Display voice search optimization results"""
    #     st.markdown("### 🎤 Voice Search Optimization")
        
    #     # Conversational score
    #     conv_score = result.get('conversational_score', 0)
    #     if conv_score:
    #         st.metric("Conversational Score", f"{conv_score}/10")
        
    #     # Question-answer pairs
    #     qa_pairs = result.get('question_answer_pairs', [])
    #     if qa_pairs:
    #         st.markdown("#### ❓ Question-Answer Pairs")
    #         for qa in qa_pairs:
    #             st.write(f"**Q:** {qa.get('question', '')}")
    #             st.write(f"**A:** {qa.get('answer', '')}")
    #             st.write("---")
        
    #     # Featured snippet candidates
    #     snippets = result.get('featured_snippet_candidates', [])
    #     if snippets:
    #         st.markdown("#### 🌟 Featured Snippet Candidates")
    #         for i, snippet in enumerate(snippets, 1):
    #             st.write(f"**{i}.** {snippet}")
        
    #     # Voice optimized content
    #     voice_content = result.get('voice_optimized_content', '')
    #     if voice_content:
    #         st.markdown("#### 🎤 Voice-Optimized Content")
    #         st.text_area("Conversational version:", value=voice_content, height=200, key="voice_output")

    def display_export_options(self, result, optimization_type, original_text):
        """Display export options for results"""
        st.markdown("### 📥 Export Results")
        
        # Prepare export data
        export_data = {
            'timestamp': time.time(),
            'optimization_type': optimization_type,
            'original_text': original_text,
            'original_word_count': len(original_text.split()),
            'results': result
        }
        
        # Serialize data to JSON
        export_json = json.dumps(export_data, indent=2)
        
        # Add download button for direct download
        st.download_button(
            label="📥 Download Analysis Report",
            data=export_json,
            file_name=f"{optimization_type}_analysis_{int(time.time())}.json",
            mime="application/json"
        )
    
    def render_website_analysis_tab(self):
        """Render Website GEO Analysis tab"""
        st.header("🌐 Website GEO Analysis")
        st.markdown("Analyze websites for Generative Engine Optimization (GEO) performance.")
        
        # URL input
        col1, col2 = st.columns([3, 1])
        
        with col1:
            website_url = st.text_input(
                "Enter website URL:", 
                placeholder="https://example.com"
            )
        
        with col2:
            max_pages = st.selectbox("Pages to analyze:", [1, 3, 5], index=0)
        
        # Analysis options
        col1, col2 = st.columns(2)
        with col1:
            include_subpages = st.checkbox("Include subpages", value=False)
        with col2:
            detailed_analysis = st.checkbox("Detailed analysis", value=True)
        
        # Submit button
        if st.button("🌐 Analyze Website", key="website_analyze"):
            if not website_url.strip():
                st.warning("Please enter a website URL.")
                return
            
            try:
                # Normalize URL
                if not website_url.startswith(('http://', 'https://')):
                    website_url = 'https://' + website_url
                
                with st.spinner(f"Analyzing website: {website_url}"):
                    # Parse website content
                    pages_data = self.webpage_parser.parse_website(
                        website_url, 
                        max_pages=max_pages,
                        include_subpages=include_subpages
                    )
                    
                    if not pages_data:
                        st.error("Could not extract content from the website.")
                        return
                    
                    st.success(f"Successfully extracted content from {len(pages_data)} page(s)")
                
                # Analyze GEO scores
                with st.spinner("Calculating GEO scores..."):
                    geo_results = []
                    
                    for i, page_data in enumerate(pages_data):
                        with st.spinner(f"Analyzing page {i+1}/{len(pages_data)}..."):
                            analysis = self.geo_scorer.analyze_page_geo(
                                page_data['content'],
                                page_data['title'],
                                detailed=detailed_analysis
                            )
                            
                            if not analysis.get('error'):
                                analysis['page_data'] = page_data
                                geo_results.append(analysis)
                            else:
                                st.warning(f"Could not analyze page {i+1}: {analysis['error']}")
                
                if not geo_results:
                    st.error("Could not analyze any pages from the website.")
                    return
                
                # Display results
                self.display_geo_results(geo_results, website_url)
                
                # Export functionality
                st.markdown("### 📥 Export Results")
                if st.button("📊 Generate Full Report"):
                    report_data = self.result_exporter.export_geo_results(
                        geo_results, 
                        website_url
                    )
                    
                    st.download_button(
                        label="Download GEO Report",
                        data=json.dumps(report_data, indent=2),
                        file_name=f"geo_analysis_{website_url.replace('https://', '').replace('/', '_')}.json",
                        mime="application/json"
                    )
            
            except Exception as e:
                st.error(f"An error occurred during website analysis: {str(e)}")
    
    def display_geo_results(self, geo_results: List[Dict], website_url: str):
        """Display GEO analysis results"""
        st.markdown("## 📊 GEO Analysis Results")
        
        # Calculate average scores
        avg_scores = self.calculate_average_scores(geo_results)
        overall_avg = sum(avg_scores.values()) / len(avg_scores) if avg_scores else 0
        
        # Main score display
        col1, col2, col3 = st.columns([1, 2, 1])
        with col2:
            st.metric(
                "Overall GEO Score", 
                f"{overall_avg:.1f}/10",
                delta=f"{overall_avg - 7.0:.1f}" if overall_avg != 7.0 else None
            )
        
        # Individual metrics
        st.markdown("### 📈 Detailed GEO Metrics")
        
        # First row of metrics
        col1, col2, col3, col4 = st.columns(4)
        metrics_row1 = [
            ("AI Search Visibility", "ai_search_visibility"),
            ("Query Intent Match", "query_intent_matching"),
            ("Factual Accuracy", "factual_accuracy"),
            ("Conversational Ready", "conversational_readiness")
        ]
        
        for i, (display_name, key) in enumerate(metrics_row1):
            with [col1, col2, col3, col4][i]:
                score = avg_scores.get(key, 0)
                st.metric(display_name, f"{score:.1f}")
        
        # Second row of metrics
        col1, col2, col3, col4 = st.columns(4)
        metrics_row2 = [
            ("Semantic Richness", "semantic_richness"),
            ("Context Complete", "context_completeness"),
            ("Citation Worthy", "citation_worthiness"),
            ("Multi-Query Cover", "multi_query_coverage")
        ]
        
        for i, (display_name, key) in enumerate(metrics_row2):
            with [col1, col2, col3, col4][i]:
                score = avg_scores.get(key, 0)
                st.metric(display_name, f"{score:.1f}")
        
        # Recommendations
        self.display_recommendations(geo_results)
        
        # Detailed page analysis
        with st.expander("📋 Detailed Page Analysis"):
            for i, analysis in enumerate(geo_results):
                page_data = analysis.get('page_data', {})
                st.markdown(f"#### Page {i+1}: {page_data.get('title', 'Unknown Title')}")
                st.write(f"**URL**: {page_data.get('url', 'Unknown')}")
                st.write(f"**Word Count**: {page_data.get('word_count', 0)}")
                
                # Show topics and entities if available
                if 'primary_topics' in analysis:
                    st.write(f"**Topics**: {', '.join(analysis['primary_topics'])}")
                
                if 'entities' in analysis:
                    st.write(f"**Entities**: {', '.join(analysis['entities'])}")
                
                # Show page-specific scores
                if 'geo_scores' in analysis:
                    scores = analysis['geo_scores']
                    score_text = ", ".join([f"{k}: {v:.1f}" for k, v in scores.items()])
                    st.write(f"**Scores**: {score_text}")
                
                st.write("---")
    
    def display_recommendations(self, geo_results: List[Dict]):
        """Display optimization recommendations"""
        st.markdown("### 💡 Optimization Recommendations")
        
        # Collect all recommendations
        all_recommendations = []
        all_opportunities = []
        
        for analysis in geo_results:
            all_recommendations.extend(analysis.get('recommendations', []))
            all_opportunities.extend(analysis.get('optimization_opportunities', []))
        
        # Remove duplicates and display
        unique_recommendations = list(set(all_recommendations))
        
        if unique_recommendations:
            for i, rec in enumerate(unique_recommendations[:5], 1):
                st.write(f"**{i}.** {rec}")
        
        # Priority opportunities
        if all_opportunities:
            st.markdown("#### 🚀 Priority Optimizations")
            
            high_priority = [opp for opp in all_opportunities if opp.get('priority') == 'high']
            medium_priority = [opp for opp in all_opportunities if opp.get('priority') == 'medium']
            
            if high_priority:
                st.markdown("##### 🔴 High Priority")
                for opp in high_priority[:3]:
                    st.write(f"**{opp.get('type', 'Optimization')}**: {opp.get('description', 'No description')}")
            
            if medium_priority:
                st.markdown("##### 🟡 Medium Priority")
                for opp in medium_priority[:3]:
                    st.write(f"**{opp.get('type', 'Optimization')}**: {opp.get('description', 'No description')}")
    
    def calculate_average_scores(self, geo_results: List[Dict]) -> Dict[str, float]:
        """Calculate average GEO scores across all pages"""
        if not geo_results:
            return {}
        
        # Get all score keys from the first result
        score_keys = list(geo_results[0].get('geo_scores', {}).keys())
        avg_scores = {}
        
        for key in score_keys:
            scores = [
                result['geo_scores'][key] 
                for result in geo_results 
                if 'geo_scores' in result and key in result['geo_scores']
            ]
            avg_scores[key] = sum(scores) / len(scores) if scores else 0
        
        return avg_scores
    
    def save_uploaded_file(self, uploaded_file) -> str:
        """Save uploaded file to temporary location"""
        with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
            tmp_file.write(uploaded_file.read())
            return tmp_file.name


def main():
    """Main entry point"""
    app = GEOSEOApp()
    app.run()


if __name__ == "__main__":
    main()