import json
import os
import re
import time
import logging
import mimetypes
import tempfile
from datetime import datetime
from pathlib import Path
from urllib.parse import urlparse
from typing import List, Dict, Tuple, Union, Optional
import requests
import validators
import gradio as gr
from diskcache import Cache
from bs4 import BeautifulSoup
from fake_useragent import UserAgent
from cleantext import clean
import qrcode
import PyPDF2
from PIL import Image
import pytesseract
import cv2
import numpy as np
import fitz  # PyMuPDF
import zipfile

# Setup logging with detailed configuration
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - [%(filename)s:%(lineno)d] - %(message)s',
    handlers=[
        logging.StreamHandler(),
        logging.FileHandler('app.log', encoding='utf-8')
    ]
)
logger = logging.getLogger(__name__)

class URLProcessor:
    def __init__(self):
        self.session = requests.Session()
        self.timeout = 10  # seconds
        self.session.headers.update({
            'User-Agent': UserAgent().random,
            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
            'Accept-Language': 'en-US,en;q=0.5',
            'Accept-Encoding': 'gzip, deflate, br',
            'Connection': 'keep-alive',
            'Upgrade-Insecure-Requests': '1'
        })
        self.supported_content_types = {
            'text/html': self._fetch_html_content,
            'application/pdf': self._fetch_pdf_content,
            'image': self._fetch_image_content,
            'application/json': self._fetch_json_content,
            'text/plain': self._fetch_text_content
        }

    def advanced_text_cleaning(self, text: str) -> str:
        """Robust text cleaning with version compatibility"""
        try:
            cleaned_text = clean(
                text,
                fix_unicode=True,
                to_ascii=True,
                lower=True,
                no_line_breaks=True,
                no_urls=True,
                no_emails=True,
                no_phone_numbers=True,
                no_numbers=False,
                no_digits=False,
                no_currency_symbols=True,
                no_punct=False
            ).strip()
            return cleaned_text
        except Exception as e:
            logger.warning(f"Text cleaning error: {e}. Using fallback method.")
            text = re.sub(r'[\x00-\x1F\x7F-\x9F]', '', text)  # Remove control characters
            text = text.encode('ascii', 'ignore').decode('ascii')  # Remove non-ASCII characters
            text = re.sub(r'\s+', ' ', text)  # Normalize whitespace
            return text.strip()

    def validate_url(self, url: str) -> Dict:
        """Validate URL format and accessibility"""
        try:
            if not validators.url(url):
                return {'is_valid': False, 'message': 'Invalid URL format'}
            
            response = self.session.head(url, timeout=self.timeout)
            response.raise_for_status()
            return {'is_valid': True, 'message': 'URL is valid and accessible'}
        except Exception as e:
            return {'is_valid': False, 'message': f'URL validation failed: {str(e)}'}

    def fetch_content(self, url: str) -> Optional[Dict]:
        """Universal content fetcher with enhanced content type handling"""
        try:
            # Special case handling
            if 'drive.google.com' in url:
                return self._handle_google_drive(url)
            if 'calendar.google.com' in url and 'ical' in url:
                return self._handle_google_calendar(url)

            # Get content type
            response = self.session.head(url, timeout=self.timeout)
            content_type = response.headers.get('Content-Type', '').split(';')[0].lower()

            # Find appropriate handler
            handler = None
            for supported_type, type_handler in self.supported_content_types.items():
                if content_type.startswith(supported_type):
                    handler = type_handler
                    break

            if handler:
                return handler(url)
            else:
                logger.warning(f"Unsupported content type: {content_type}")
                return self._fetch_text_content(url)

        except Exception as e:
            logger.error(f"Content fetch failed: {e}")
            return None

    def _handle_google_drive(self, url: str) -> Optional[Dict]:
        """Process Google Drive file links"""
        try:
            file_id = re.search(r'/file/d/([a-zA-Z0-9_-]+)', url)
            if not file_id:
                logger.error(f"Invalid Google Drive URL: {url}")
                return None
                
            direct_url = f"https://drive.google.com/uc?export=download&id={file_id.group(1)}"
            response = self.session.get(direct_url, timeout=self.timeout)
            response.raise_for_status()
            
            return {
                'content': response.text,
                'content_type': response.headers.get('Content-Type', ''),
                'timestamp': datetime.now().isoformat()
            }
        except Exception as e:
            logger.error(f"Google Drive processing failed: {e}")
            return None

    def _handle_google_calendar(self, url: str) -> Optional[Dict]:
        """Process Google Calendar ICS feeds"""
        try:
            response = self.session.get(url, timeout=self.timeout)
            response.raise_for_status()
            return {
                'content': response.text,
                'content_type': 'text/calendar',
                'timestamp': datetime.now().isoformat()
            }
        except Exception as e:
            logger.error(f"Calendar fetch failed: {e}")
            return None

    def _fetch_html_content(self, url: str) -> Optional[Dict]:
        """Enhanced HTML content processing with metadata extraction"""
        try:
            response = self.session.get(url, timeout=self.timeout)
            response.raise_for_status()
            
            soup = BeautifulSoup(response.text, 'html.parser')
            
            # Remove unwanted elements
            for element in soup(['script', 'style', 'nav', 'footer', 'header', 'meta', 'link']):
                element.decompose()
                
            # Extract main content
            main_content = soup.find('main') or soup.find('article') or soup.body
            
            # Extract metadata
            metadata = {
                'title': soup.title.string if soup.title else None,
                'description': soup.find('meta', {'name': 'description'})['content'] if soup.find('meta', {'name': 'description'}) else None,
                'keywords': soup.find('meta', {'name': 'keywords'})['content'] if soup.find('meta', {'name': 'keywords'}) else None,
                'author': soup.find('meta', {'name': 'author'})['content'] if soup.find('meta', {'name': 'author'}) else None
            }
            
            # Clean and structure content
            text_content = main_content.get_text(separator='\n', strip=True)
            cleaned_content = self.advanced_text_cleaning(text_content)
            
            return {
                'content': cleaned_content,
                'metadata': metadata,
                'content_type': response.headers.get('Content-Type', ''),
                'timestamp': datetime.now().isoformat()
            }
        except Exception as e:
            logger.error(f"HTML processing failed: {e}")
            return None

    def _fetch_pdf_content(self, url: str) -> Optional[Dict]:
        """Process PDF content with enhanced metadata extraction"""
        try:
            response = self.session.get(url, timeout=self.timeout)
            response.raise_for_status()
            
            with tempfile.NamedTemporaryFile(suffix='.pdf') as temp_file:
                temp_file.write(response.content)
                temp_file.flush()
                
                # Extract text and metadata using PyMuPDF
                doc = fitz.open(temp_file.name)
                
                # Extract text with formatting preservation
                text = ""
                metadata = {
                    'title': doc.metadata.get('title'),
                    'author': doc.metadata.get('author'),
                    'subject': doc.metadata.get('subject'),
                    'keywords': doc.metadata.get('keywords'),
                    'creator': doc.metadata.get('creator'),
                    'producer': doc.metadata.get('producer'),
                    'page_count': len(doc),
                    'file_size': os.path.getsize(temp_file.name),
                    'version': doc.version
                }
                
                # Extract text with layout preservation
                for page in doc:
                    blocks = page.get_text("blocks")
                    for block in blocks:
                        if block[6] == 0:  # Text block
                            text += block[4] + "\n"
                
                doc.close()
                cleaned_content = self.advanced_text_cleaning(text)
                
                return {
                    'content': cleaned_content,
                    'metadata': metadata,
                    'content_type': 'application/pdf',
                    'timestamp': datetime.now().isoformat()
                }
        except Exception as e:
            logger.error(f"PDF processing failed: {e}")
            return None

    def _fetch_image_content(self, url: str) -> Optional[Dict]:
        """Process image content with OCR and advanced image processing"""
        try:
            response = self.session.get(url, timeout=self.timeout)
            response.raise_for_status()
            
            with tempfile.NamedTemporaryFile(suffix='.jpg') as temp_file:
                temp_file.write(response.content)
                temp_file.flush()
                
                # Load image with OpenCV
                img = cv2.imread(temp_file.name)
                if img is None:
                    raise ValueError("Failed to load image")
                
                # Image preprocessing for better OCR
                gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
                denoised = cv2.fastNlMeansDenoising(gray)
                thresh = cv2.threshold(denoised, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
                
                # Extract text using Tesseract
                text = pytesseract.image_to_string(thresh)
                cleaned_text = self.advanced_text_cleaning(text) if text else None
                
                # Extract metadata and additional image features
                with Image.open(temp_file.name) as pil_img:
                    exif = pil_img._getexif() if hasattr(pil_img, '_getexif') else None
                    metadata = {
                        'format': pil_img.format,
                        'mode': pil_img.mode,
                        'size': pil_img.size,
                        'exif': exif,
                        'image_features': {
                            'resolution': img.shape,
                            'channels': img.shape[2] if len(img.shape) > 2 else 1,
                            'mean_brightness': np.mean(gray),
                            'has_text': bool(cleaned_text and cleaned_text.strip())
                        }
                    }
                
                return {
                    'content': cleaned_text,
                    'metadata': metadata,
                    'content_type': response.headers.get('Content-Type', ''),
                    'timestamp': datetime.now().isoformat()
                }
        except Exception as e:
            logger.error(f"Image processing failed: {e}")
            return None

    def _fetch_json_content(self, url: str) -> Optional[Dict]:
        """Process JSON content"""
        try:
            response = self.session.get(url, timeout=self.timeout)
            response.raise_for_status()
            
            content = response.json()
            
            return {
                'content': json.dumps(content, indent=2),
                'content_type': 'application/json',
                'timestamp': datetime.now().isoformat()
            }
        except Exception as e:
            logger.error(f"JSON processing failed: {e}")
            return None

    def _fetch_text_content(self, url: str) -> Optional[Dict]:
        """Process plain text content"""
        try:
            response = self.session.get(url, timeout=self.timeout)
            response.raise_for_status()
            
            cleaned_content = self.advanced_text_cleaning(response.text)
            
            return {
                'content': cleaned_content,
                'content_type': response.headers.get('Content-Type', ''),
                'timestamp': datetime.now().isoformat()
            }
        except Exception as e:
            logger.error(f"Text processing failed: {e}")
            return None
            
class FileProcessor:
    """Class to handle file processing"""
    
    def __init__(self, max_file_size: int = 2 * 1024 * 1024 * 1024):  # 2GB default
        self.max_file_size = max_file_size
        self.supported_text_extensions = {'.txt', '.md', '.csv', '.json', '.xml'}
        
    def is_text_file(self, filepath: str) -> bool:
        """Check if file is a text file"""
        try:
            mime_type, _ = mimetypes.guess_type(filepath)
            return (mime_type and mime_type.startswith('text/')) or \
                   (os.path.splitext(filepath)[1].lower() in self.supported_text_extensions)
        except Exception:
            return False

    def process_file(self, file) -> List[Dict]:
        """Process uploaded file with enhanced error handling"""
        if not file:
            return []

        dataset = []
        try:
            file_size = os.path.getsize(file.name)
            if file_size > self.max_file_size:
                logger.warning(f"File size ({file_size} bytes) exceeds maximum allowed size")
                return []

            with tempfile.TemporaryDirectory() as temp_dir:
                if zipfile.is_zipfile(file.name):
                    dataset.extend(self._process_zip_file(file.name, temp_dir))
                else:
                    dataset.extend(self._process_single_file(file))

        except Exception as e:
            logger.error(f"Error processing file: {str(e)}")
            return []

        return dataset

    def _process_zip_file(self, zip_path: str, temp_dir: str) -> List[Dict]:
        """Process ZIP file contents"""
        results = []
        with zipfile.ZipFile(zip_path, 'r') as zip_ref:
            zip_ref.extractall(temp_dir)
            for root, _, files in os.walk(temp_dir):
                for filename in files:
                    filepath = os.path.join(root, filename)
                    if self.is_text_file(filepath):
                        try:
                            with open(filepath, 'r', encoding='utf-8', errors='ignore') as f:
                                content = f.read()
                            if content.strip():
                                results.append({
                                    "source": "file",
                                    "filename": filename,
                                    "content": content,
                                    "timestamp": datetime.now().isoformat()
                                })
                        except Exception as e:
                            logger.error(f"Error reading file {filename}: {str(e)}")
        return results

def _process_single_file(self, file) -> List[Dict]:
    try:
        file_stat = os.stat(file.name)
        
        # For very large files, read in chunks and summarize
        if file_stat.st_size > 100 * 1024 * 1024:  # 100MB
            logger.info(f"Processing large file: {file.name} ({file_stat.st_size} bytes)")
            
            # Read first and last 1MB for extremely large files
            content = ""
            with open(file.name, 'r', encoding='utf-8', errors='ignore') as f:
                content = f.read(1 * 1024 * 1024)  # First 1MB
                content += "\n...[Content truncated due to large file size]...\n"
                
                # Seek to the last 1MB
                f.seek(max(0, file_stat.st_size - 1 * 1024 * 1024))
                content += f.read()  # Last 1MB
        else:
            # Regular file processing
            with open(file.name, 'r', encoding='utf-8', errors='ignore') as f:
                content = f.read()
        
        return [{
            'source': 'file',
            'filename': os.path.basename(file.name),
            'file_size': file_stat.st_size,
            'mime_type': mimetypes.guess_type(file.name)[0],
            'created': datetime.fromtimestamp(file_stat.st_ctime).isoformat(),
            'modified': datetime.fromtimestamp(file_stat.st_mtime).isoformat(),
            'content': content,
            'timestamp': datetime.now().isoformat()
        }]
    except Exception as e:
        logger.error(f"File processing error: {e}")
        return []

def generate_qr_code(json_data):
    """Generate a QR code from JSON data."""
    qr = qrcode.make(json_data)
    qr_path = "output/qr_code.png"
    qr.save(qr_path)
    return qr_path

def create_interface():
    """Create a comprehensive Gradio interface with advanced features"""
    
    css = """
    .container { max-width: 1200px; margin: auto; }
    .warning { background-color: #fff3cd; color: #856404; }
    .error { background-color: #f8d7da; color: #721c24; }
    """
    
    with gr.Blocks(css=css, title="Advanced Text & URL Processor") as interface:
        gr.Markdown("# 🌐 Advanced URL & Text Processing Toolkit")
        
        with gr.Tab("URL Processing"):
            url_input = gr.Textbox(
                label="Enter URLs (comma or newline separated)", 
                lines=5,
                placeholder="https://example1.com\nhttps://example2.com"
            )
        
        with gr.Tab("File Input"):
            file_input = gr.File(
                label="Upload text file or ZIP archive",
                file_types=[".txt", ".zip", ".md", ".csv", ".json", ".xml"]
            )

        with gr.Tab("Text Input"):
            text_input = gr.Textbox(
                label="Raw Text Input", 
                lines=5,
                placeholder="Paste your text here..."
            )
        
        with gr.Tab("JSON Editor"):
            json_editor = gr.Textbox(
                label="JSON Editor",
                lines=20,
                placeholder="View and edit your JSON data here...",
                interactive=True,
                elem_id="json-editor"  # Optional: for custom styling
            )
        
        with gr.Tab("Scratchpad"):
            scratchpad = gr.Textbox(
                label="Scratchpad",
                lines=10,
                placeholder="Quick notes or text collections...",
                interactive=True
            )
        
        process_btn = gr.Button("Process Input", variant="primary")
        qr_btn = gr.Button("Generate QR Code", variant="secondary")
        
        output_text = gr.Textbox(label="Processing Results", interactive=False)
        output_file = gr.File(label="Processed Output")
        qr_output = gr.Image(label="QR Code", type="filepath")  # To display the generated QR code
        
        def process_all_inputs(urls, file, text, notes):
            """Process all input types with progress tracking"""
            try:
                processor = URLProcessor()
                file_processor = FileProcessor()
                results = []
                
                # Process URLs
                if urls:
                    url_list = re.split(r'[,\n]', urls)
                    url_list = [url.strip() for url in url_list if url.strip()]
                    
                    for url in url_list:
                        validation = processor.validate_url(url)
                        if validation.get('is_valid'):
                            content = processor.fetch_content(url)
                            if content:
                                results.append({
                                    'source': 'url',
                                    'url': url,
                                    'content': content,
                                    'timestamp': datetime.now().isoformat()
                                })
                
                # Process files
                if file:
                    results.extend(file_processor.process_file(file))
        
                # Process text input
                if text:
                    cleaned_text = processor.advanced_text_cleaning(text)
                    results.append({
                        'source': 'direct_input',
                        'content': cleaned_text,
                        'timestamp': datetime.now().isoformat()
                    })
                
                # Generate output
                if results:
                    output_dir = Path('output') / datetime.now().strftime('%Y-%m-%d')
                    output_dir.mkdir(parents=True, exist_ok=True)
                    output_path = output_dir / f'processed_{int(time.time())}.json'
                    
                    with open(output_path, 'w', encoding='utf-8') as f:
                        json.dump(results, f, ensure_ascii=False, indent=2)
                    
                    summary = f"Processed {len(results)} items successfully!"
                    json_data = json.dumps(results, indent=2)  # Prepare JSON for QR code
                    return str(output_path), summary, json_data  # Return JSON for editor
                else:
                    return None, "No valid content to process.", ""
            
            except Exception as e:
                logger.error(f"Processing error: {e}")
                return None, f"Error: {str(e)}", ""
        
        def generate_qr(json_data):
            """Generate QR code from JSON data and return the file path."""
            if json_data:
                return generate_qr_code(json_data)
            return None
        
        process_btn.click(
            process_all_inputs, 
            inputs=[url_input, file_input, text_input, scratchpad], 
            outputs=[output_file, output_text, json_editor]  # Update outputs to include JSON editor
        )
        
        qr_btn.click(
            generate_qr, 
            inputs=json_editor, 
            outputs=qr_output
        )
        
        gr.Markdown("""
        ### Usage Guidelines
        - **URL Processing**: Enter valid HTTP/HTTPS URLs
        - **File Input**: Upload text files or ZIP archives
        - **Text Input**: Direct text processing
        - **JSON Editor**: View and edit your JSON data
        - **Scratchpad**: Quick notes or text collections
        - Advanced cleaning and validation included
        """)
    
    return interface

def main():
    # Configure system settings
    mimetypes.init()
    
    # Create and launch interface
    interface = create_interface()
    
    # Launch with proper configuration
    interface.launch(
        server_name="0.0.0.0",
        server_port=7860,
        show_error=True,
        share=False,
        inbrowser=True,
        debug=True
    )

if __name__ == "__main__":
    main()