"""
AI Dataset Studio - Minimal Version
Guaranteed to work with basic dependencies only
"""

import gradio as gr
import pandas as pd
import json
import re
import requests
from bs4 import BeautifulSoup
from urllib.parse import urlparse
from datetime import datetime
import logging
from typing import Dict, List, Tuple, Optional, Any
from dataclasses import dataclass, asdict
import uuid
import time

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

@dataclass
class SimpleScrapedItem:
    """Simplified scraped content structure"""
    id: str
    url: str
    title: str
    content: str
    word_count: int
    scraped_at: str
    quality_score: float = 0.0

class SimpleWebScraper:
    """Simplified web scraper with basic functionality"""
    
    def __init__(self):
        self.session = requests.Session()
        self.session.headers.update({
            'User-Agent': 'Mozilla/5.0 (compatible; AI-DatasetStudio/1.0)',
            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8'
        })
    
    def scrape_url(self, url: str) -> Optional[SimpleScrapedItem]:
        """Scrape a single URL"""
        try:
            if not self._validate_url(url):
                return None
            
            response = self.session.get(url, timeout=10)
            response.raise_for_status()
            
            soup = BeautifulSoup(response.content, 'html.parser')
            
            # Extract title
            title_tag = soup.find('title')
            title = title_tag.get_text().strip() if title_tag else "Untitled"
            
            # Extract content
            # Remove unwanted elements
            for element in soup(['script', 'style', 'nav', 'header', 'footer']):
                element.decompose()
            
            # Try to find main content
            content_element = (soup.find('article') or 
                             soup.find('main') or 
                             soup.find(class_='content') or
                             soup.find('body'))
            
            if content_element:
                content = content_element.get_text(separator=' ', strip=True)
            else:
                content = soup.get_text(separator=' ', strip=True)
            
            # Clean content
            content = re.sub(r'\s+', ' ', content).strip()
            
            # Calculate basic metrics
            word_count = len(content.split())
            quality_score = min(1.0, word_count / 100) if word_count > 0 else 0.0
            
            return SimpleScrapedItem(
                id=str(uuid.uuid4()),
                url=url,
                title=title,
                content=content,
                word_count=word_count,
                scraped_at=datetime.now().isoformat(),
                quality_score=quality_score
            )
            
        except Exception as e:
            logger.error(f"Failed to scrape {url}: {e}")
            return None
    
    def _validate_url(self, url: str) -> bool:
        """Basic URL validation"""
        try:
            parsed = urlparse(url)
            return parsed.scheme in ['http', 'https'] and parsed.netloc
        except:
            return False
    
    def batch_scrape(self, urls: List[str], progress_callback=None) -> List[SimpleScrapedItem]:
        """Scrape multiple URLs"""
        results = []
        total = len(urls)
        
        for i, url in enumerate(urls):
            if progress_callback:
                progress_callback((i + 1) / total, f"Scraping {i+1}/{total}")
            
            item = self.scrape_url(url)
            if item:
                results.append(item)
            
            time.sleep(1)  # Rate limiting
        
        return results

class SimpleDataProcessor:
    """Basic data processing"""
    
    def process_items(self, items: List[SimpleScrapedItem], options: Dict[str, bool]) -> List[SimpleScrapedItem]:
        """Process scraped items"""
        processed = []
        
        for item in items:
            # Apply quality filter
            if options.get('quality_filter', True) and item.quality_score < 0.3:
                continue
            
            # Clean text if requested
            if options.get('clean_text', True):
                item.content = self._clean_text(item.content)
            
            processed.append(item)
        
        return processed
    
    def _clean_text(self, text: str) -> str:
        """Basic text cleaning"""
        # Remove URLs
        text = re.sub(r'http\S+', '', text)
        # Remove extra whitespace
        text = re.sub(r'\s+', ' ', text)
        # Remove common navigation text
        text = re.sub(r'(Click here|Read more|Subscribe|Advertisement)', '', text, flags=re.IGNORECASE)
        return text.strip()

class SimpleExporter:
    """Basic export functionality"""
    
    def export_dataset(self, items: List[SimpleScrapedItem], format_type: str) -> str:
        """Export dataset"""
        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
        
        if format_type == "json":
            filename = f"dataset_{timestamp}.json"
            data = [asdict(item) for item in items]
            with open(filename, 'w', encoding='utf-8') as f:
                json.dump(data, f, indent=2, ensure_ascii=False)
            return filename
        
        elif format_type == "csv":
            filename = f"dataset_{timestamp}.csv"
            data = [asdict(item) for item in items]
            df = pd.DataFrame(data)
            df.to_csv(filename, index=False)
            return filename
        
        else:
            raise ValueError(f"Unsupported format: {format_type}")

class SimpleDatasetStudio:
    """Simplified main application"""
    
    def __init__(self):
        self.scraper = SimpleWebScraper()
        self.processor = SimpleDataProcessor()
        self.exporter = SimpleExporter()
        
        self.scraped_items = []
        self.processed_items = []
        self.current_project = None
    
    def create_project(self, name: str) -> Dict[str, Any]:
        """Create a new project"""
        self.current_project = {
            'name': name,
            'id': str(uuid.uuid4()),
            'created_at': datetime.now().isoformat()
        }
        self.scraped_items = []
        self.processed_items = []
        return self.current_project
    
    def scrape_urls(self, urls: List[str], progress_callback=None) -> Tuple[int, List[str]]:
        """Scrape URLs"""
        url_list = [url.strip() for url in urls if url.strip()]
        if not url_list:
            return 0, ["No valid URLs provided"]
        
        self.scraped_items = self.scraper.batch_scrape(url_list, progress_callback)
        success_count = len(self.scraped_items)
        failed_count = len(url_list) - success_count
        
        errors = []
        if failed_count > 0:
            errors.append(f"{failed_count} URLs failed")
        
        return success_count, errors
    
    def process_data(self, options: Dict[str, bool]) -> int:
        """Process scraped data"""
        if not self.scraped_items:
            return 0
        
        self.processed_items = self.processor.process_items(self.scraped_items, options)
        return len(self.processed_items)
    
    def get_preview(self) -> List[Dict[str, Any]]:
        """Get data preview"""
        items = self.processed_items or self.scraped_items
        preview = []
        
        for item in items[:5]:
            preview.append({
                'Title': item.title[:50] + "..." if len(item.title) > 50 else item.title,
                'Content Preview': item.content[:100] + "..." if len(item.content) > 100 else item.content,
                'Word Count': item.word_count,
                'Quality Score': round(item.quality_score, 2),
                'URL': item.url[:50] + "..." if len(item.url) > 50 else item.url
            })
        
        return preview
    
    def get_stats(self) -> Dict[str, Any]:
        """Get dataset statistics"""
        items = self.processed_items or self.scraped_items
        if not items:
            return {}
        
        word_counts = [item.word_count for item in items]
        quality_scores = [item.quality_score for item in items]
        
        return {
            'total_items': len(items),
            'avg_word_count': round(sum(word_counts) / len(word_counts)),
            'avg_quality': round(sum(quality_scores) / len(quality_scores), 2),
            'min_words': min(word_counts),
            'max_words': max(word_counts)
        }
    
    def export_data(self, format_type: str) -> str:
        """Export dataset"""
        items = self.processed_items or self.scraped_items
        if not items:
            raise ValueError("No data to export")
        
        return self.exporter.export_dataset(items, format_type)

def create_simple_interface():
    """Create simplified Gradio interface"""
    
    studio = SimpleDatasetStudio()
    
    # Custom CSS
    css = """
    .container { max-width: 1200px; margin: auto; }
    .header { 
        background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
        color: white; padding: 2rem; border-radius: 10px; 
        text-align: center; margin-bottom: 2rem;
    }
    .step-box {
        background: #f8f9ff; border: 1px solid #e1e5ff;
        border-radius: 8px; padding: 1.5rem; margin: 1rem 0;
    }
    """
    
    with gr.Blocks(css=css, title="AI Dataset Studio - Simple") as interface:
        
        # Header
        gr.HTML("""
        <div class="header">
            <h1>🚀 AI Dataset Studio - Simple Version</h1>
            <p>Create datasets from web content - No complex setup required!</p>
        </div>
        """)
        
        # Project state
        project_state = gr.State({})
        
        with gr.Tabs():
            
            # Project Setup
            with gr.Tab("📋 Project Setup"):
                gr.HTML('<div class="step-box"><h3>Step 1: Create Your Project</h3></div>')
                
                project_name = gr.Textbox(
                    label="Project Name",
                    placeholder="e.g., News Articles Dataset",
                    value="My Dataset"
                )
                
                create_btn = gr.Button("Create Project", variant="primary")
                project_status = gr.Markdown("")
                
                def create_project_handler(name):
                    if not name.strip():
                        return "❌ Please enter a project name", {}
                    
                    project = studio.create_project(name.strip())
                    status = f"""
                    ✅ **Project Created!**
                    
                    **Name:** {project['name']}  
                    **ID:** {project['id'][:8]}...  
                    **Created:** {project['created_at'][:19]}
                    
                    👉 Next: Go to Data Collection tab
                    """
                    return status, project
                
                create_btn.click(
                    fn=create_project_handler,
                    inputs=[project_name],
                    outputs=[project_status, project_state]
                )
            
            # Data Collection
            with gr.Tab("🕷️ Data Collection"):
                gr.HTML('<div class="step-box"><h3>Step 2: Scrape Web Content</h3></div>')
                
                urls_input = gr.Textbox(
                    label="URLs to Scrape (one per line)",
                    placeholder="https://example.com/article1\nhttps://example.com/article2",
                    lines=6
                )
                
                scrape_btn = gr.Button("Start Scraping", variant="primary")
                scrape_status = gr.Markdown("")
                
                def scrape_handler(urls_text, project, progress=gr.Progress()):
                    if not project:
                        return "❌ Create a project first"
                    
                    urls = [url.strip() for url in urls_text.split('\n') if url.strip()]
                    if not urls:
                        return "❌ No URLs provided"
                    
                    def progress_callback(pct, msg):
                        progress(pct, desc=msg)
                    
                    success_count, errors = studio.scrape_urls(urls, progress_callback)
                    
                    if success_count > 0:
                        return f"""
                        ✅ **Scraping Complete!**
                        
                        **Success:** {success_count} URLs  
                        **Failed:** {len(urls) - success_count} URLs  
                        
                        👉 Next: Go to Data Processing tab
                        """
                    else:
                        return f"❌ Scraping failed: {', '.join(errors)}"
                
                scrape_btn.click(
                    fn=scrape_handler,
                    inputs=[urls_input, project_state],
                    outputs=[scrape_status]
                )
            
            # Data Processing
            with gr.Tab("⚙️ Data Processing"):
                gr.HTML('<div class="step-box"><h3>Step 3: Clean and Process Data</h3></div>')
                
                with gr.Row():
                    clean_text = gr.Checkbox(label="Clean Text", value=True)
                    quality_filter = gr.Checkbox(label="Quality Filter", value=True)
                
                process_btn = gr.Button("Process Data", variant="primary")
                process_status = gr.Markdown("")
                
                def process_handler(clean, quality, project):
                    if not project:
                        return "❌ Create a project first"
                    
                    options = {
                        'clean_text': clean,
                        'quality_filter': quality
                    }
                    
                    processed_count = studio.process_data(options)
                    
                    if processed_count > 0:
                        return f"""
                        ✅ **Processing Complete!**
                        
                        **Processed:** {processed_count} items  
                        
                        👉 Next: Check Data Preview tab
                        """
                    else:
                        return "❌ No items passed processing filters"
                
                process_btn.click(
                    fn=process_handler,
                    inputs=[clean_text, quality_filter, project_state],
                    outputs=[process_status]
                )
            
            # Data Preview
            with gr.Tab("👀 Data Preview"):
                gr.HTML('<div class="step-box"><h3>Step 4: Review Your Dataset</h3></div>')
                
                refresh_btn = gr.Button("Refresh Preview")
                preview_table = gr.DataFrame(label="Dataset Preview")
                stats_display = gr.JSON(label="Statistics")
                
                def refresh_handler(project):
                    if not project:
                        return None, {}
                    
                    preview = studio.get_preview()
                    stats = studio.get_stats()
                    return preview, stats
                
                refresh_btn.click(
                    fn=refresh_handler,
                    inputs=[project_state],
                    outputs=[preview_table, stats_display]
                )
            
            # Export
            with gr.Tab("📤 Export Dataset"):
                gr.HTML('<div class="step-box"><h3>Step 5: Export Your Dataset</h3></div>')
                
                export_format = gr.Radio(
                    choices=["JSON", "CSV"],
                    label="Export Format",
                    value="JSON"
                )
                
                export_btn = gr.Button("Export Dataset", variant="primary")
                export_status = gr.Markdown("")
                export_file = gr.File(label="Download", visible=False)
                
                def export_handler(format_type, project):
                    if not project:
                        return "❌ Create a project first", None
                    
                    try:
                        filename = studio.export_data(format_type.lower())
                        return f"✅ Export successful! File: {filename}", filename
                    except Exception as e:
                        return f"❌ Export failed: {str(e)}", None
                
                export_btn.click(
                    fn=export_handler,
                    inputs=[export_format, project_state],
                    outputs=[export_status, export_file]
                )
        
        # Instructions
        with gr.Accordion("📚 Quick Guide", open=False):
            gr.Markdown("""
            ## How to Use
            
            1. **Create Project** - Give your dataset a name
            2. **Add URLs** - Paste URLs of web pages to scrape
            3. **Process Data** - Clean and filter the content
            4. **Review** - Check the quality of your dataset
            5. **Export** - Download in JSON or CSV format
            
            ## Features
            - ✅ Smart content extraction
            - ✅ Quality filtering
            - ✅ Text cleaning
            - ✅ JSON/CSV export
            - ✅ Preview and statistics
            
            ## Tips
            - Use high-quality source URLs
            - Enable quality filtering for better results
            - Review your data before exporting
            - Start with 5-10 URLs to test
            """)
    
    return interface

# Launch application
if __name__ == "__main__":
    logger.info("🚀 Starting AI Dataset Studio (Simple Version)")
    
    try:
        interface = create_simple_interface()
        logger.info("✅ Simple interface created successfully")
        
        interface.launch(
            server_name="0.0.0.0",
            server_port=7860,
            share=False,
            show_error=True
        )
        
    except Exception as e:
        logger.error(f"❌ Failed to launch: {e}")
        print("\n💡 If you see import errors, try installing:")
        print("pip install gradio pandas requests beautifulsoup4")
        raise