import gradio as gr
import google.generativeai as genai
import textstat
import re
import requests
from urllib.parse import urlparse
import json
import time
from typing import Dict, List, Tuple
import concurrent.futures

# --- Manual Keywords Processor ---
class ManualKeywordProcessor:
    def parse_manual_keywords(self, manual_keywords: str) -> List[str]:
        """Parse and clean manual target keywords"""
        if not manual_keywords.strip():
            return []
        
        keywords = []
        # Split by comma, semicolon, or newline
        raw_keywords = re.split(r'[,;\n]', manual_keywords)
        
        for keyword in raw_keywords:
            cleaned = keyword.strip().lower()
            if cleaned and len(cleaned) > 2:  # Minimum keyword length
                keywords.append(cleaned)
        
        return list(set(keywords))  # Remove duplicates
    
    def integrate_manual_keywords(self, auto_keywords: Dict, manual_keywords: List[str]) -> Dict:
        """Integrate manual keywords with auto-generated ones"""
        # Add manual keywords to primary keywords list
        manual_keyword_objects = []
        for kw in manual_keywords:
            manual_keyword_objects.append({
                "keyword": kw,
                "search_volume": "manual",
                "difficulty": "unknown",
                "intent": "targeted"
            })
        
        # Merge with existing keywords
        integrated = auto_keywords.copy()
        
        if "manual_targets" not in integrated:
            integrated["manual_targets"] = []
        
        integrated["manual_targets"] = manual_keyword_objects
        
        return integrated
class KeywordResearcher:
    def __init__(self):
        self.serp_api_key = None  # Users can add their SERP API key for real data
        
    def suggest_keywords(self, seed_keyword: str, model) -> Dict:
        """Generate keyword suggestions with estimated metrics"""
        prompt = f"""
        Generate a comprehensive keyword research report for: "{seed_keyword}"
        
        Provide 15-20 related keywords with estimated metrics:
        - Primary keyword variations
        - Long-tail keywords  
        - Question-based keywords
        - Commercial intent keywords
        - Informational intent keywords
        
        Format as JSON:
        {{
            "primary_keywords": [
                {{"keyword": "example", "search_volume": "high/medium/low", "difficulty": "easy/medium/hard", "intent": "informational/commercial/navigational"}}
            ],
            "long_tail": [...],
            "questions": [...],
            "commercial": [...]
        }}
        """
        
        try:
            response = model.generate_content(prompt)
            # Parse JSON response
            json_text = response.text.replace('```json', '').replace('```', '').strip()
            return json.loads(json_text)
        except:
            return {
                "primary_keywords": [
                    {"keyword": f"{seed_keyword} guide", "search_volume": "medium", "difficulty": "medium", "intent": "informational"},
                    {"keyword": f"{seed_keyword} tips", "search_volume": "medium", "difficulty": "easy", "intent": "informational"},
                    {"keyword": f"best {seed_keyword}", "search_volume": "high", "difficulty": "hard", "intent": "commercial"}
                ],
                "long_tail": [
                    {"keyword": f"how to use {seed_keyword} effectively", "search_volume": "low", "difficulty": "easy", "intent": "informational"}
                ],
                "questions": [
                    {"keyword": f"what is {seed_keyword}", "search_volume": "medium", "difficulty": "easy", "intent": "informational"}
                ],
                "commercial": [
                    {"keyword": f"{seed_keyword} service", "search_volume": "medium", "difficulty": "medium", "intent": "commercial"}
                ]
            }

# --- Competitor Analysis ---
class CompetitorAnalyzer:
    def analyze_top_competitors(self, keyword: str, model) -> Dict:
        """Analyze top competitors for content gaps"""
        prompt = f"""
        Analyze the top 5 competitors ranking for "{keyword}" and identify:
        
        1. Common content themes they cover
        2. Content gaps they're missing
        3. Average content length
        4. Heading structures they use
        5. Unique angles to differentiate our content
        
        Format as structured analysis with actionable insights.
        """
        
        try:
            response = model.generate_content(prompt)
            return {
                "analysis": response.text,
                "content_gaps": self._extract_content_gaps(response.text),
                "avg_length": "2000-3000 words",
                "differentiation_angles": self._extract_angles(response.text)
            }
        except:
            return {
                "analysis": f"Competitors for '{keyword}' typically cover basic information. Opportunity to add more detailed examples and case studies.",
                "content_gaps": ["Specific examples", "Step-by-step tutorials", "Common mistakes section"],
                "avg_length": "2000-3000 words",
                "differentiation_angles": ["Personal experience", "Updated statistics", "Unique framework"]
            }
    
    def _extract_content_gaps(self, text: str) -> List[str]:
        """Extract content gaps from analysis"""
        gaps = []
        lines = text.split('\n')
        for line in lines:
            if 'gap' in line.lower() or 'missing' in line.lower() or 'opportunity' in line.lower():
                gaps.append(line.strip('- ').strip())
        return gaps[:5] if gaps else ["Advanced techniques", "Case studies", "Common mistakes"]
    
    def _extract_angles(self, text: str) -> List[str]:
        """Extract differentiation angles"""
        angles = []
        lines = text.split('\n')
        for line in lines:
            if 'unique' in line.lower() or 'different' in line.lower() or 'angle' in line.lower():
                angles.append(line.strip('- ').strip())
        return angles[:3] if angles else ["Personal experience", "Latest trends", "Actionable framework"]

# --- Content Outline Generator ---
class ContentOutliner:
    def generate_seo_outline(self, keyword: str, keywords_data: Dict, competitor_data: Dict, model) -> str:
        """Generate comprehensive SEO-optimized outline"""
        
        # Extract top keywords for outline
        all_keywords = []
        for category in keywords_data.values():
            if isinstance(category, list):
                all_keywords.extend([k["keyword"] for k in category])
        
        content_gaps = competitor_data.get("content_gaps", [])
        
        prompt = f"""
        Create a comprehensive SEO content outline for: "{keyword}"
        
        REQUIREMENTS:
        - Include H1, H2, H3 structure
        - Integrate these keywords naturally: {', '.join(all_keywords[:10])}
        - Address these content gaps: {', '.join(content_gaps)}
        - Optimize for featured snippets
        - Include FAQ section
        - Add internal linking opportunities
        
        OUTLINE FORMAT:
        H1: [Compelling title with primary keyword]
        
        Introduction (150-200 words)
        - Hook with statistic or question
        - Include primary keyword in first 100 words
        - Promise what reader will learn
        
        H2: [First main section]
        H3: [Subsection]
        H3: [Subsection]
        
        [Continue with 4-6 main H2 sections]
        
        H2: FAQ Section
        - Question 1 (optimize for featured snippet)
        - Question 2
        - Question 3
        
        Conclusion (100-150 words)
        - Summarize key points
        - Strong call to action
        
        Add [INTERNAL LINK] and [IMAGE] suggestions throughout.
        """
        
        try:
            response = model.generate_content(prompt)
            return response.text
        except:
            return f"Error generating outline. Please check your API key and try again."

# --- Featured Snippet Optimizer ---
class SnippetOptimizer:
    def optimize_for_snippets(self, content: str, questions: List[str], model) -> str:
        """Optimize content sections for featured snippets"""
        
        snippet_formats = {
            "paragraph": "Answer in 40-50 words, clear and direct",
            "list": "Format as numbered or bulleted list",
            "table": "Present data in simple table format",
            "steps": "Break down into clear step-by-step process"
        }
        
        optimized_sections = []
        
        for question in questions[:5]:  # Limit to top 5 questions
            prompt = f"""
            Optimize this answer for Google featured snippets:
            
            Question: {question}
            
            Requirements:
            - Answer directly in first sentence
            - Keep paragraph answers to 40-50 words
            - Use clear, simple language
            - Include the question keywords in the answer
            - Format for easy scanning
            
            Provide the optimized answer.
            """
            
            try:
                response = model.generate_content(prompt)
                optimized_sections.append(f"<h3>{question}</h3>\n<p>{response.text}</p>\n")
            except:
                optimized_sections.append(f"<h3>{question}</h3>\n<p>Answer optimized for featured snippets will appear here.</p>\n")
        
        return "\n".join(optimized_sections)

# --- Enhanced Link Management ---
class LinkManager:
    def parse_manual_links(self, links_input: str) -> Dict:
        """Parse manual internal and external links"""
        internal_links = []
        external_links = []
        
        if not links_input.strip():
            return {"internal": [], "external": []}
        
        lines = links_input.strip().split('\n')
        for line in lines:
            if ':' in line:
                anchor_text, url = line.split(':', 1)
                anchor_text = anchor_text.strip()
                url = url.strip()
                
                # Determine if internal or external
                if url.startswith('http'):
                    # Check if it's same domain (simplified check)
                    if 'website.com' in url or 'yourdomain.com' in url or url.startswith('/'):
                        internal_links.append({"anchor": anchor_text, "url": url})
                    else:
                        external_links.append({"anchor": anchor_text, "url": url})
                else:
                    # Assume internal if no http
                    internal_links.append({"anchor": anchor_text, "url": url})
        
        return {"internal": internal_links, "external": external_links}
    
    def suggest_internal_links(self, content: str, keyword: str, manual_links: Dict, model) -> List[Dict]:
        """Suggest relevant internal links based on content and manual links"""
        
        # Include manual internal links in suggestions
        existing_internal = manual_links.get("internal", [])
        
        prompt = f"""
        Analyze this content and suggest 5-7 internal linking opportunities:
        
        Primary keyword: {keyword}
        Content sample: {content[:1000]}...
        
        Already provided internal links:
        {chr(10).join([f"- {link['anchor']}: {link['url']}" for link in existing_internal])}
        
        For each NEW suggestion, provide:
        - Anchor text (natural, not over-optimized)
        - Context where it should be placed
        - Reason why it's valuable for SEO
        - Suggested target page type
        
        Format as actionable suggestions. Don't repeat the existing links above.
        """
        
        try:
            response = model.generate_content(prompt)
            # Parse suggestions into structured format
            suggestions = []
            lines = response.text.split('\n')
            current_suggestion = {}
            
            for line in lines:
                if line.strip():
                    if 'anchor' in line.lower() or line.startswith('1.') or line.startswith('-'):
                        if current_suggestion:
                            suggestions.append(current_suggestion)
                        current_suggestion = {"text": line.strip()}
                    else:
                        if current_suggestion:
                            current_suggestion["text"] += " " + line.strip()
            
            if current_suggestion:
                suggestions.append(current_suggestion)
                
            return suggestions[:7]
        except:
            return [
                {"text": f"Link to related '{keyword}' resources in the introduction"},
                {"text": f"Add contextual links to '{keyword}' tools or guides"},
                {"text": f"Reference other '{keyword}' articles in conclusion"}
            ]
    
    def format_links_for_content(self, manual_links: Dict) -> str:
        """Format manual links for inclusion in content"""
        formatted_links = []
        
        # Internal links
        internal_links = manual_links.get("internal", [])
        if internal_links:
            formatted_links.append("**Internal Links to Include:**")
            for link in internal_links:
                formatted_links.append(f'<a href="{link["url"]}">{link["anchor"]}</a>')
        
        # External links
        external_links = manual_links.get("external", [])
        if external_links:
            formatted_links.append("\n**External Links to Include:**")
            for link in external_links:
                formatted_links.append(f'<a href="{link["url"]}" target="_blank" rel="noopener">{link["anchor"]}</a>')
        
        return "\n".join(formatted_links)

# --- Enhanced Image Strategy ---
class ImageStrategist:
    def create_image_strategy(self, outline: str, keyword: str, model) -> Dict:
        """Create comprehensive image strategy"""
        
        prompt = f"""
        Based on this content outline, create a strategic image plan:
        
        {outline}
        
        For each major section, suggest:
        1. Image type (infographic, screenshot, photo, diagram, chart)
        2. Specific content description
        3. SEO-optimized alt text
        4. Placement strategy
        5. Size recommendations
        
        Focus on images that:
        - Support the content narrative
        - Improve user engagement
        - Optimize for image search
        - Break up text effectively
        
        Primary keyword: {keyword}
        """
        
        try:
            response = model.generate_content(prompt)
            return {
                "strategy": response.text,
                "image_count": self._count_suggested_images(response.text),
                "alt_texts": self._extract_alt_texts(response.text, keyword)
            }
        except:
            return {
                "strategy": f"Add 3-5 relevant images throughout the article about {keyword}",
                "image_count": 4,
                "alt_texts": [
                    f"Comprehensive guide to {keyword} - infographic",
                    f"Step-by-step {keyword} process diagram",
                    f"Benefits of {keyword} - visual comparison",
                    f"Common {keyword} mistakes to avoid"
                ]
            }
    
    def _count_suggested_images(self, text: str) -> int:
        """Count suggested images in strategy"""
        return min(text.lower().count('image') + text.lower().count('infographic') + text.lower().count('diagram'), 8)
    
    def _extract_alt_texts(self, text: str, keyword: str) -> List[str]:
        """Extract alt text suggestions"""
        alt_texts = []
        lines = text.split('\n')
        for line in lines:
            if 'alt' in line.lower() or 'description' in line.lower():
                alt_texts.append(line.strip('- ').strip())
        
        if not alt_texts:
            alt_texts = [
                f"Complete {keyword} guide infographic",
                f"Step-by-step {keyword} tutorial",
                f"{keyword} benefits comparison chart",
                f"Real-world {keyword} examples"
            ]
        
        return alt_texts[:6]

# --- Main Enhanced Generator ---
def generate_complete_seo_content(api_key, seed_keyword, custom_outline, pov, tone, length, emotion, 
                                include_research, include_competitor, include_outline, include_snippets, 
                                include_linking, include_images, manual_target_keywords, manual_links_input, custom_cta):
    
    if not api_key or not seed_keyword:
        return "Please provide API key and seed keyword to generate content."
    
    try:
        genai.configure(api_key=api_key)
        model = genai.GenerativeModel('gemini-1.5-flash')
    except Exception as e:
        return f"Error configuring API: {str(e)}"

    # Initialize components
    researcher = KeywordResearcher()
    competitor_analyzer = CompetitorAnalyzer()
    outliner = ContentOutliner()
    snippet_optimizer = SnippetOptimizer()
    link_manager = LinkManager()
    image_strategist = ImageStrategist()
    keyword_processor = ManualKeywordProcessor()
    
    results = []
    
    # Process manual keywords
    manual_keywords = keyword_processor.parse_manual_keywords(manual_target_keywords)
    if manual_keywords:
        results.append("🎯 **MANUAL TARGET KEYWORDS PROCESSED**")
        results.append("**Your Specified Keywords:**")
        for kw in manual_keywords:
            results.append(f"• {kw}")
        results.append("\n" + "="*50 + "\n")
    
    # Process manual links
    parsed_links = link_manager.parse_manual_links(manual_links_input)
    if parsed_links["internal"] or parsed_links["external"]:
        results.append("🔗 **MANUAL LINKS PROCESSED**")
        
        if parsed_links["internal"]:
            results.append("**Internal Links:**")
            for link in parsed_links["internal"]:
                results.append(f"• {link['anchor']} → {link['url']}")
        
        if parsed_links["external"]:
            results.append("**External Links:**")
            for link in parsed_links["external"]:
                results.append(f"• {link['anchor']} → {link['url']}")
        
        results.append("\n" + "="*50 + "\n")
    
    # Step 1: Keyword Research
    if include_research:
        results.append("🔍 **STEP 1: KEYWORD RESEARCH**")
        keywords_data = researcher.suggest_keywords(seed_keyword, model)
        
        # Integrate manual keywords
        if manual_keywords:
            keywords_data = keyword_processor.integrate_manual_keywords(keywords_data, manual_keywords)
        
        # Format keyword research results
        results.append("**Primary Keywords:**")
        for kw in keywords_data.get("primary_keywords", [])[:5]:
            results.append(f"• {kw['keyword']} (Volume: {kw['search_volume']}, Difficulty: {kw['difficulty']})")
        
        # Show manual keywords separately
        if manual_keywords:
            results.append("\n**Manual Target Keywords:**")
            for kw in keywords_data.get("manual_targets", []):
                results.append(f"• {kw['keyword']} (Manual Target)")
        
        results.append("\n**Long-tail Keywords:**")
        for kw in keywords_data.get("long_tail", [])[:3]:
            results.append(f"• {kw['keyword']} (Intent: {kw['intent']})")
        
        results.append("\n**Question Keywords:**")
        for kw in keywords_data.get("questions", [])[:3]:
            results.append(f"• {kw['keyword']}")
        
        results.append("\n" + "="*50 + "\n")
    else:
        keywords_data = {"primary_keywords": [{"keyword": seed_keyword, "search_volume": "medium", "difficulty": "medium", "intent": "informational"}]}
        if manual_keywords:
            keywords_data = keyword_processor.integrate_manual_keywords(keywords_data, manual_keywords)
    
    # Step 2: Competitor Analysis
    if include_competitor:
        results.append("🏆 **STEP 2: COMPETITOR ANALYSIS**")
        competitor_data = competitor_analyzer.analyze_top_competitors(seed_keyword, model)
        results.append(competitor_data["analysis"])
        results.append("\n**Content Gaps Identified:**")
        for gap in competitor_data["content_gaps"]:
            results.append(f"• {gap}")
        results.append("\n" + "="*50 + "\n")
    else:
        competitor_data = {"content_gaps": [], "analysis": ""}
    
    # Step 3: Content Outline
    if include_outline:
        results.append("📋 **STEP 3: SEO-OPTIMIZED OUTLINE**")
        if custom_outline.strip():
            outline = custom_outline
        else:
            outline = outliner.generate_seo_outline(seed_keyword, keywords_data, competitor_data, model)
        results.append(outline)
        results.append("\n" + "="*50 + "\n")
    else:
        outline = custom_outline if custom_outline.strip() else f"Article about {seed_keyword}"
    
    # Step 4: Generate Main Content
    results.append("✍️ **STEP 4: FULL ARTICLE CONTENT**")
    
    # Extract all keywords for content generation
    all_keywords = [seed_keyword]
    
    # Add manual keywords first (highest priority)
    all_keywords.extend(manual_keywords)
    
    # Add auto-generated keywords
    for category in keywords_data.values():
        if isinstance(category, list):
            all_keywords.extend([k["keyword"] for k in category[:2]])  # Top 2 from each category
    
    # Prepare manual links for content integration
    manual_links_formatted = link_manager.format_links_for_content(parsed_links)
    
    # Enhanced content generation prompt
    content_prompt = f"""
    Write a comprehensive, SEO-optimized blog post based on this outline:
    
    {outline}
    
    REQUIREMENTS:
    - Primary keyword: "{seed_keyword}"
    - MUST integrate these manual target keywords naturally: {', '.join(manual_keywords) if manual_keywords else 'None specified'}
    - Also include these keywords: {', '.join(all_keywords[len(manual_keywords)+1:15])}
    - Tone: {tone}, POV: {pov}, Emotion: {emotion}
    - Length: {length}
    - Write in genuinely human style - avoid AI phrases
    - Use HTML formatting for WordPress
    - Include specific examples and actionable advice
    - Optimize for readability with short paragraphs
    
    LINKS TO INTEGRATE:
    {manual_links_formatted if manual_links_formatted else 'No manual links provided'}
    
    Instructions for links:
    - Naturally integrate the provided internal and external links where contextually relevant
    - Add [INTERNAL LINK OPPORTUNITY] markers for additional internal link suggestions
    - Add [IMAGE: description] placeholders for visuals
    
    Content should be comprehensive, engaging, and provide real value to readers.
    Make it feel like it's written by an expert who genuinely cares about helping the reader.
    Prioritize the manual target keywords - they should appear naturally throughout the content.
    """
    
    try:
        content_response = model.generate_content(content_prompt)
        main_content = content_response.text
        results.append(main_content)
    except Exception as e:
        results.append(f"Error generating main content: {str(e)}")
        main_content = f"Content about {seed_keyword} would appear here."
    
    results.append("\n" + "="*50 + "\n")
    
    # Step 5: Featured Snippet Optimization
    if include_snippets:
        results.append("🎯 **STEP 5: FEATURED SNIPPET OPTIMIZATION**")
        questions = [kw["keyword"] for kw in keywords_data.get("questions", [])]
        if not questions:
            questions = [f"What is {seed_keyword}?", f"How to use {seed_keyword}?", f"Benefits of {seed_keyword}?"]
        
        snippet_content = snippet_optimizer.optimize_for_snippets(main_content, questions, model)
        results.append("**FAQ Section Optimized for Featured Snippets:**")
        results.append(snippet_content)
        results.append("\n" + "="*50 + "\n")
    
    # Step 6: Internal Linking Strategy
    if include_linking:
        results.append("🔗 **STEP 6: LINKING STRATEGY**")
        
        # Show processed manual links
        if parsed_links["internal"] or parsed_links["external"]:
            results.append("**Your Manual Links (Already Integrated):**")
            if parsed_links["internal"]:
                results.append("*Internal Links:*")
                for link in parsed_links["internal"]:
                    results.append(f"• {link['anchor']} → {link['url']}")
            if parsed_links["external"]:
                results.append("*External Links:*")
                for link in parsed_links["external"]:
                    results.append(f"• {link['anchor']} → {link['url']}")
            results.append("")
        
        # Suggest additional internal links
        link_suggestions = link_manager.suggest_internal_links(main_content, seed_keyword, parsed_links, model)
        results.append("**Additional Internal Link Suggestions:**")
        for i, suggestion in enumerate(link_suggestions, 1):
            results.append(f"{i}. {suggestion['text']}")
        results.append("\n" + "="*50 + "\n")
    
    # Step 7: Image Strategy
    if include_images:
        results.append("🖼️ **STEP 7: IMAGE STRATEGY**")
        image_strategy = image_strategist.create_image_strategy(outline, seed_keyword, model)
        results.append(f"**Recommended Images: {image_strategy['image_count']}**")
        results.append(image_strategy["strategy"])
        results.append("\n**Optimized Alt Texts:**")
        for i, alt_text in enumerate(image_strategy["alt_texts"], 1):
            results.append(f"{i}. {alt_text}")
        results.append("\n" + "="*50 + "\n")
    
    # Step 8: Meta Data
    results.append("📊 **STEP 8: SEO META DATA**")
    
    # Generate meta title and description
    meta_prompt = f"""
    Create SEO-optimized meta data for this content:
    
    Primary keyword: {seed_keyword}
    Manual target keywords: {', '.join(manual_keywords) if manual_keywords else 'None'}
    Content summary: {main_content[:500]}...
    
    Generate:
    1. Meta title (under 60 characters, include primary keyword and/or manual keywords)
    2. Meta description (under 160 characters, compelling and click-worthy)
    3. 3 alternative title variations
    
    Prioritize the manual target keywords in meta data if provided.
    """
    
    try:
        meta_response = model.generate_content(meta_prompt)
        results.append(meta_response.text)
    except:
        results.append(f"**Meta Title:** {seed_keyword.title()} - Complete Guide")
        results.append(f"**Meta Description:** Discover everything about {seed_keyword} in this comprehensive guide. Get actionable tips and expert insights.")
    
    # Final analysis including manual keywords
    word_count = len(main_content.split())
    flesch_score = textstat.flesch_reading_ease(main_content) if main_content else 0
    
    # Count manual keyword usage
    manual_keyword_usage = {}
    if manual_keywords:
        for kw in manual_keywords:
            count = main_content.lower().count(kw.lower())
            manual_keyword_usage[kw] = count
    
    results.append(f"\n**Content Analysis:**")
    results.append(f"• Word Count: {word_count}")
    results.append(f"• Readability Score: {flesch_score:.1f}")
    results.append(f"• Total Keywords Integrated: {len(all_keywords)}")
    
    if manual_keyword_usage:
        results.append(f"• Manual Keywords Usage:")
        for kw, count in manual_keyword_usage.items():
            density = round((count / word_count) * 100, 2) if word_count > 0 else 0
            results.append(f"  - '{kw}': {count} times ({density}% density)")
    
    if parsed_links["internal"] or parsed_links["external"]:
        results.append(f"• Manual Links Integrated: {len(parsed_links['internal']) + len(parsed_links['external'])}")
    
    return "\n".join(results)

# --- Gradio Interface ---
with gr.Blocks(css="""
    #generate_button { 
        background: linear-gradient(45deg, #10b981, #059669) !important; 
        color: white !important; 
        font-weight: bold !important;
        border: none !important;
    }
    .gradio-container { max-width: 1200px !important; }
    .step-header { color: #059669; font-weight: bold; }
""") as demo:

    gr.Markdown("# 🚀 SeoPlan2Article v4 - Complete SEO Content System")
    gr.Markdown("*Full workflow: Keyword Research → Competitor Analysis → Content Outline → Article Generation → SEO Optimization → Link Strategy → Image Planning*")

    with gr.Accordion("🔑 API Configuration", open=True):
        api_key_input = gr.Textbox(
            label="Gemini API Key", 
            type="password", 
            placeholder="Enter your Gemini API key..."
        )
        seed_keyword_input = gr.Textbox(
            label="Seed Keyword", 
            placeholder="e.g., sustainable gardening tips",
            info="Primary keyword for research and content generation"
        )

    with gr.Accordion("📋 Content Outline (Optional)", open=False):
        custom_outline_input = gr.Textbox(
            label="Custom Content Outline", 
            lines=8, 
            placeholder="Leave blank to auto-generate, or paste your outline here...",
            info="If provided, this will be used instead of auto-generated outline"
        )

    with gr.Row():
        with gr.Column():
            pov_input = gr.Dropdown(
                label="📖 Point of View", 
                choices=["First Person (I/We)", "Second Person (You/Your)", "Third Person (He/She/It/They)"], 
                value="Second Person (You/Your)"
            )
            tone_input = gr.Dropdown(
                label="🎨 Tone", 
                choices=["Friendly", "Professional", "Witty", "Motivational", "Reassuring", "Authoritative"], 
                value="Professional"
            )
        with gr.Column():
            length_input = gr.Dropdown(
                label="📏 Article Length", 
                choices=["Short (800-1200)", "Standard (1500-2500)", "Long (2500-4000)", "Very Long (4000+)"], 
                value="Standard (1500-2500)"
            )
            emotion_input = gr.Dropdown(
                label="💭 Emotional Tone", 
                choices=["Trust", "Excitement", "Curiosity", "Confidence", "Inspiration", "Urgency"], 
                value="Trust"
            )

    with gr.Accordion("🔧 Workflow Steps", open=True):
        gr.Markdown("Select which steps to include in your SEO content workflow:")
        
        with gr.Row():
            include_research = gr.Checkbox(label="🔍 Keyword Research", value=True)
            include_competitor = gr.Checkbox(label="🏆 Competitor Analysis", value=True)
            include_outline = gr.Checkbox(label="📋 Auto-Generate Outline", value=True)
        
        with gr.Row():
            include_snippets = gr.Checkbox(label="🎯 Featured Snippet Optimization", value=True)
            include_linking = gr.Checkbox(label="🔗 Internal Link Strategy", value=True)
            include_images = gr.Checkbox(label="🖼️ Image Strategy", value=True)

    with gr.Accordion("⚙️ Manual Targeting & Links", open=True):
        gr.Markdown("### 🎯 Manual Target Keywords")
        manual_target_keywords_input = gr.Textbox(
            label="Manual Target Keywords", 
            placeholder="fermented pickle, homemade fermentation, pickle fermentation process",
            lines=3,
            info="Comma-separated keywords you specifically want to target. These get PRIORITY in content generation."
        )
        
        gr.Markdown("### 🔗 Manual Internal & External Links")
        manual_links_input = gr.Textbox(
            label="Links to Include", 
            placeholder="fermented pickle guide: https://www.website.com/fermented-pickle-at-home\nhealthy fermentation: /internal-page\nfermentation benefits: https://external-authority.com/benefits",
            lines=6,
            info="Format: 'Anchor Text: URL' (one per line). Use full URLs for external, relative paths for internal."
        )
        
        custom_cta_input = gr.Textbox(
            label="Custom Call-to-Action", 
            placeholder="Leave blank for auto-generated CTA",
            info="Optional: Specify your preferred call-to-action"
        )

    generate_btn = gr.Button("🚀 Generate Complete SEO Content System", elem_id="generate_button", size="lg")
    
    output = gr.Markdown("Your complete SEO content analysis will appear here...")

    generate_btn.click(
        fn=generate_complete_seo_content,
        inputs=[
            api_key_input, seed_keyword_input, custom_outline_input, pov_input, tone_input,
            length_input, emotion_input, include_research, include_competitor, include_outline,
            include_snippets, include_linking, include_images, manual_target_keywords_input, 
            manual_links_input, custom_cta_input
        ],
        outputs=output
    )

    gr.Markdown("""
    ## 🎯 What This System Does:

    **Complete SEO Workflow Coverage:**
    1. **Manual Targeting** - Specify exact keywords and links you want prioritized
    2. **Keyword Research** - Find primary, long-tail, and question keywords with intent analysis
    3. **Competitor Analysis** - Identify content gaps and differentiation opportunities  
    4. **Content Outline** - Generate SEO-optimized H1/H2/H3 structure
    5. **Article Generation** - Write comprehensive, human-like content with your manual targets
    6. **Featured Snippet Optimization** - Format FAQs and answers for Google snippets
    7. **Link Strategy** - Integrate your manual links + suggest additional opportunities
    8. **Image Strategy** - Plan visual content with SEO-optimized alt texts
    9. **Meta Data** - Generate optimized titles and descriptions

    **🆕 NEW Manual Targeting Features:**
    - **Priority Keywords**: Your manual keywords get highest priority in content
    - **Smart Link Integration**: Automatically integrates your internal/external links contextually  
    - **Link Classification**: Automatically detects internal vs external links
    - **Usage Tracking**: Shows exactly how many times your manual keywords appear
    - **Density Analysis**: Calculates keyword density for your target terms

    **Link Format Examples:**
    ```
    fermented pickle guide: https://www.website.com/fermented-pickle-at-home
    healthy fermentation: /internal-fermentation-page
    scientific study: https://pubmed.ncbi.nlm.nih.gov/study-link
    ```

    **Enhanced Features:**
    - Human tone enforcement (avoid AI-like phrases)
    - Semantic keyword integration
    - Competitor content gap analysis
    - Featured snippet formatting
    - Strategic image placement
    - Manual + automatic linking strategy
    - Complete meta data optimization
    - Priority keyword tracking
    """)

if __name__ == "__main__":
    demo.launch(debug=True, show_error=True)