app2.py

import json
import os
import re
import time
import logging
import mimetypes
import zipfile
import tempfile
import chardet
import io  # Needed for processing CSV from string
import csv # Needed for CSV
import xml.etree.ElementTree as ET # Needed for XML
from datetime import datetime
from typing import List, Dict, Optional, Union, Tuple, Any # Added Any for extracted_data
from pathlib import Path
from urllib.parse import urlparse, urljoin
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
from PIL import Image, ImageDraw, ImageFont
import numpy as np
import tarfile
import gzip
import math

# Setup enhanced logging with more detailed formatting
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__) # logger is now defined here

# Conditional imports for document processing
try:
    from PyPDF2 import PdfReader
    PDF_SUPPORT = True
except ImportError:
    PDF_SUPPORT = False
    logger.warning("PyPDF2 not installed. PDF file processing will be limited.") # logger is available

try:
    from docx import Document
    DOCX_SUPPORT = True
except ImportError:
    DOCX_SUPPORT = False
    logger.warning("python-docx not installed. DOCX file processing will be limited.") # logger is available

try:
    from pyth.plugins.rtf15.reader import Rtf15Reader
    from pyth.plugins.plaintext.writer import PlaintextWriter
    RTF_SUPPORT = True
except ImportError:
    RTF_SUPPORT = False
    logger.warning("pyth not installed. RTF file processing will be limited.")

try:
    from odf.opendocument import OpenDocumentText
    from odf import text as odftext
    ODT_SUPPORT = True
except ImportError:
    ODT_SUPPORT = False
    logger.warning("odfpy not installed. ODT file processing will be limited.")


# Setup enhanced logging with more detailed formatting
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__)

# Ensure output directories exist with modern structure
OUTPUTS_DIR = Path('output')
QR_CODES_DIR = OUTPUTS_DIR / 'qr_codes'
TEMP_DIR = OUTPUTS_DIR / 'temp'
for directory in [OUTPUTS_DIR, QR_CODES_DIR, TEMP_DIR]:
    directory.mkdir(parents=True, exist_ok=True)

class EnhancedURLProcessor:
    """Advanced URL processing with enhanced content extraction"""
    def __init__(self):
        self.session = requests.Session()
        self.timeout = 15  # Extended timeout for larger content
        self.max_retries = 3
        self.user_agent = UserAgent()

        # Enhanced headers for better site compatibility
        self.session.headers.update({
            'User-Agent': self.user_agent.random,
            'Accept': 'text/html, application/json, application/xml, text/plain, */*', # Request common types
            'Accept-Language': 'en-US,en;q=0.9',
            'Accept-Encoding': 'gzip, deflate, br',
            'Connection': 'keep-alive',
            'Upgrade-Insecure-Requests': '1', # May be ignored for non-HTML
            'Sec-Fetch-Dest': 'document',
            'Sec-Fetch-Mode': 'navigate',
            'Sec-Fetch-Site': 'none',
            'Sec-Fetch-User': '?1',
            'DNT': '1'
        })

    def validate_url(self, url: str) -> Dict:
        """Enhanced URL validation with detailed feedback"""
        try:
            if not validators.url(url):
                return {'is_valid': False, 'message': 'Invalid URL format', 'details': 'URL must begin with http:// or https://'}
            parsed = urlparse(url)
            if not all([parsed.scheme, parsed.netloc]):
                return {'is_valid': False, 'message': 'Incomplete URL', 'details': 'Missing scheme or domain'}
            # Try HEAD request first to check accessibility
            try:
                head_response = self.session.head(url, timeout=5)
                head_response.raise_for_status()
                final_url = head_response.url # Capture potential redirects
            except requests.exceptions.RequestException:
                 # If HEAD fails, try GET as some servers don't support HEAD
                response = self.session.get(url, timeout=self.timeout)
                response.raise_for_status()
                final_url = response.url # Capture potential redirects

            return {
                'is_valid': True,
                'message': 'URL is valid and accessible',
                'details': {
                    'final_url': final_url,
                    'content_type': head_response.headers.get('Content-Type', 'unknown'),
                    'server': head_response.headers.get('Server', 'unknown'),
                    'size': head_response.headers.get('Content-Length', 'unknown')
                }
            }
        except Exception as e:
            return {'is_valid': False, 'message': f'URL validation failed: {str(e)}', 'details': str(e)}

    def fetch_content(self, url: str, retry_count: int = 0) -> Optional[Dict]:
        """Enhanced content fetcher with retry mechanism and complete character extraction"""
        try:
            logger.info(f"Fetching content from URL: {url} (Attempt {retry_count + 1}/{self.max_retries})")

            # Update User-Agent randomly for each request
            self.session.headers.update({'User-Agent': self.user_agent.random})

            response = self.session.get(url, timeout=self.timeout)
            response.raise_for_status()
            final_url = response.url # Capture potential redirects

            # Detect encoding
            if response.encoding is None or response.encoding == 'ISO-8859-1': # chardet often better than default response.encoding for text
                encoding_detection = chardet.detect(response.content)
                encoding = encoding_detection['encoding'] or 'utf-8'
                logger.debug(f"Detected encoding '{encoding}' with confidence {encoding_detection['confidence']:.2f} for {url}")
            else:
                encoding = response.encoding
                logger.debug(f"Using response.encoding '{encoding}' for {url}")

            # Decode content with fallback
            try:
                raw_content = response.content.decode(encoding, errors='replace')
            except (UnicodeDecodeError, LookupError):
                 # Fallback to a more common encoding if the first attempt fails
                try:
                     raw_content = response.content.decode('utf-8', errors='replace')
                     encoding = 'utf-8 (fallback)'
                     logger.warning(f"Decoding with {encoding} fallback for {url}")
                except Exception:
                     raw_content = response.content.decode('latin-1', errors='replace') # Another common fallback
                     encoding = 'latin-1 (fallback)'
                     logger.warning(f"Decoding with {encoding} fallback for {url}")


            # Extract metadata
            metadata = {
                'original_url': url,
                'final_url': final_url,
                'timestamp': datetime.now().isoformat(),
                'detected_encoding': encoding,
                'content_type': response.headers.get('Content-Type', ''),
                'content_length': len(response.content),
                'headers': dict(response.headers),
                'status_code': response.status_code
            }

            # Process based on content type
            processed_extraction = self._process_web_content(raw_content, metadata['content_type'], final_url)

            return {
                'source': 'url',
                'url': url, # Keep original URL as identifier
                'raw_content': raw_content,
                'metadata': metadata,
                'extracted_data': processed_extraction['data'],
                'processing_notes': processed_extraction['notes']
            }
        except requests.exceptions.RequestException as e:
            if retry_count < self.max_retries - 1:
                logger.warning(f"Retry {retry_count + 1}/{self.max_retries} for URL: {url}")
                time.sleep(2 ** retry_count)  # Exponential backoff
                return self.fetch_content(url, retry_count + 1)
            logger.error(f"Failed to fetch content after {self.max_retries} attempts from {url}: {e}")
            return {
                 'source': 'url',
                 'url': url,
                 'raw_content': None,
                 'metadata': {'original_url': url, 'timestamp': datetime.now().isoformat()},
                 'extracted_data': None,
                 'processing_notes': f"Failed to fetch content: {str(e)}"
            }
        except Exception as e:
            logger.error(f"Unexpected error while fetching or processing URL {url}: {e}")
            return {
                'source': 'url',
                'url': url,
                'raw_content': raw_content if 'raw_content' in locals() else None,
                'metadata': metadata if 'metadata' in locals() else {'original_url': url, 'timestamp': datetime.now().isoformat()},
                'extracted_data': None,
                'processing_notes': f"Unexpected processing error: {str(e)}"
            }

    def _process_web_content(self, content: str, content_type: str, base_url: str) -> Dict[str, Any]:
        """Process content based on detected content type"""
        lower_content_type = content_type.lower()
        notes = []
        extracted_data: Any = None # Use Any to allow different types

        try:
            if 'text/html' in lower_content_type:
                logger.debug(f"Processing HTML content from {base_url}")
                extracted_data = self._process_html_content_enhanced(content, base_url)
                notes.append("Processed as HTML")
            elif 'application/json' in lower_content_type or 'text/json' in lower_content_type:
                 logger.debug(f"Processing JSON content from {base_url}")
                 try:
                     extracted_data = json.loads(content)
                     notes.append("Parsed as JSON")
                 except json.JSONDecodeError as e:
                     extracted_data = content # Keep raw text if invalid JSON
                     notes.append(f"Failed to parse as JSON: {e}")
                     logger.warning(f"Failed to parse JSON from {base_url}: {e}")
                 except Exception as e:
                     extracted_data = content
                     notes.append(f"Error processing JSON: {e}")
                     logger.error(f"Error processing JSON from {base_url}: {e}")
            elif 'application/xml' in lower_content_type or 'text/xml' in lower_content_type or lower_content_type.endswith('+xml'):
                 logger.debug(f"Processing XML content from {base_url}")
                 try:
                     # Try parsing XML. Convert to a string or a dict representation if needed.
                     # For simplicity, we'll convert to a readable string representation of the tree.
                     root = ET.fromstring(content)
                     # A simple way to represent XML as text
                     xml_text = ET.tostring(root, encoding='unicode', method='xml')
                     extracted_data = xml_text # Store as string for now
                     notes.append("Parsed as XML (text representation)")
                 except ET.ParseError as e:
                     extracted_data = content
                     notes.append(f"Failed to parse as XML: {e}")
                     logger.warning(f"Failed to parse XML from {base_url}: {e}")
                 except Exception as e:
                     extracted_data = content
                     notes.append(f"Error processing XML: {e}")
                     logger.error(f"Error processing XML from {base_url}: {e}")
            elif 'text/plain' in lower_content_type or 'text/' in lower_content_type: # Catch other text types
                 logger.debug(f"Processing Plain Text content from {base_url}")
                 extracted_data = content
                 notes.append("Processed as Plain Text")
            else:
                logger.debug(f"Unknown content type '{content_type}' from {base_url}. Storing raw content.")
                extracted_data = content # Store raw content for unknown types
                notes.append(f"Unknown content type '{content_type}'. Stored raw text.")

        except Exception as e:
            logger.error(f"Unexpected error in _process_web_content for {base_url} ({content_type}): {e}")
            extracted_data = content # Fallback to raw content on error
            notes.append(f"Unexpected processing error: {e}. Stored raw text.")

        return {'data': extracted_data, 'notes': notes}


    def _process_html_content_enhanced(self, content: str, base_url: str) -> Dict[str, Any]:
        """Process HTML content, preserving text, and extracting metadata."""
        extracted: Dict[str, Any] = {
            'title': None,
            'meta_description': None, # Add extraction for meta description
            'full_text': "",
            'links': [] # Add extraction for links
        }
        try:
            soup = BeautifulSoup(content, 'html.parser')

            # Extract Title
            if soup.title and soup.title.string:
                extracted['title'] = soup.title.string.strip()

            # Extract Meta Description
            meta_desc = soup.find('meta', attrs={'name': 'description'})
            if meta_desc and meta_desc.get('content'):
                extracted['meta_description'] = meta_desc['content'].strip()

            # Extract and process links (convert relative to absolute)
            for a_tag in soup.find_all('a', href=True):
                 href = a_tag['href']
                 text = a_tag.get_text().strip()
                 try:
                      absolute_url = urljoin(base_url, href)
                      extracted['links'].append({'text': text, 'url': absolute_url})
                 except Exception:
                      extracted['links'].append({'text': text, 'url': href}) # Keep relative if join fails


            # Extract all text content (similar to stripped_strings but ensures order)
            text_parts = []
            # Use a more robust way to get visible text, including handling script/style tags
            for script_or_style in soup(["script", "style"]):
                script_or_style.extract() # Remove script and style tags
            text = soup.get_text(separator='\n') # Get text with newlines

            # Clean up whitespace and empty lines
            lines = text.splitlines()
            cleaned_lines = [line.strip() for line in lines if line.strip()]
            extracted['full_text'] = '\n'.join(cleaned_lines)

        except Exception as e:
            logger.error(f"Enhanced HTML processing error for {base_url}: {e}")
            extracted['full_text'] = content # Fallback to raw content
            extracted['processing_error'] = f"Enhanced HTML processing failed: {e}"

        return extracted

class EnhancedFileProcessor:
    """Advanced file processing with enhanced content extraction"""
    def __init__(self, max_file_size: int = 5 * 1024 * 1024 * 1024):  # 5GB default
        self.max_file_size = max_file_size
        # Expanded supported extensions to include common docs and structured formats
        self.supported_extensions = {
            '.txt', '.md', '.csv', '.json', '.xml', '.html', '.htm',
            '.log', '.yml', '.yaml', '.ini', '.conf', '.cfg',
            '.pdf', '.doc', '.docx', '.rtf', '.odt',
            # Archives are handled separately but listed for context
            '.zip', '.tar', '.gz', '.bz2', '.7z', '.rar',
        }
        self.archive_extensions = {'.zip', '.tar', '.gz', '.bz2', '.7z', '.rar'}


    def process_file(self, file) -> List[Dict]:
        """Process uploaded file with enhanced error handling and complete extraction"""
        if not file or not hasattr(file, 'name'):
            logger.warning("Received invalid file object.")
            return []

        dataset = []
        file_path = Path(file.name) # Use Path object for easier handling

        try:
            file_size = file_path.stat().st_size
            if file_size > self.max_file_size:
                logger.warning(f"File '{file_path.name}' size ({file_size} bytes) exceeds maximum allowed size ({self.max_file_size} bytes).")
                return [{
                    'source': 'file',
                    'filename': file_path.name,
                    'file_size': file_size,
                    'extracted_data': None,
                    'processing_notes': 'File size exceeds limit.'
                }]

            with tempfile.TemporaryDirectory() as temp_dir:
                temp_dir_path = Path(temp_dir)

                # Decide processing strategy
                if file_path.suffix.lower() in self.archive_extensions:
                    dataset.extend(self._process_archive(file_path, temp_dir_path))
                elif file_path.suffix.lower() in self.supported_extensions:
                    # Pass the path to the single file processor
                    dataset.extend(self._process_single_file(file_path))
                else:
                    logger.warning(f"Unsupported file type for processing: '{file_path.name}'")
                    # Optionally process as raw text even if extension is unsupported
                    try:
                         # Read as text with error replacement
                         content_bytes = file_path.read_bytes()
                         encoding_detection = chardet.detect(content_bytes)
                         encoding = encoding_detection['encoding'] or 'utf-8'
                         raw_content = content_bytes.decode(encoding, errors='replace')
                         dataset.append({
                             'source': 'file',
                             'filename': file_path.name,
                             'file_size': file_size,
                             'mime_type': mimetypes.guess_type(file_path.name)[0] or 'unknown/unknown',
                             'extracted_data': {'plain_text': raw_content}, # Store raw text under a key
                             'processing_notes': 'Processed as plain text (unsupported extension).'
                         })
                    except Exception as e:
                        logger.error(f"Error reading or processing unsupported file '{file_path.name}' as text: {e}")
                        dataset.append({
                             'source': 'file',
                             'filename': file_path.name,
                             'file_size': file_size,
                             'mime_type': mimetypes.guess_type(file_path.name)[0] or 'unknown/unknown',
                             'extracted_data': None,
                             'processing_notes': f'Unsupported file type and failed to read as text: {e}'
                        })


        except Exception as e:
            logger.error(f"Error processing file '{file_path.name}': {str(e)}")
            dataset.append({
                'source': 'file',
                'filename': file_path.name,
                'file_size': file_size if 'file_size' in locals() else None,
                'extracted_data': None,
                'processing_notes': f'Overall file processing error: {str(e)}'
            })
        return dataset

    def _is_archive(self, filepath: Union[str, Path]) -> bool:
        """Check if file is an archive"""
        p = Path(filepath) if isinstance(filepath, str) else filepath
        return p.suffix.lower() in self.archive_extensions

    def _process_single_file(self, file_path: Path) -> List[Dict]:
        """Process a single file with enhanced character extraction and format-specific handling"""
        dataset_entries = []
        filename = file_path.name
        file_size = file_path.stat().st_size
        mime_type, _ = mimetypes.guess_type(file_path)
        mime_type = mime_type or 'unknown/unknown'
        file_extension = file_path.suffix.lower()

        logger.info(f"Processing single file: '{filename}' ({mime_type}, {file_size} bytes)")

        raw_content: Optional[str] = None
        extracted_data: Any = None
        processing_notes = []

        try:
            # Read content efficiently
            content_bytes = file_path.read_bytes()
            encoding_detection = chardet.detect(content_bytes)
            encoding = encoding_detection['encoding'] or 'utf-8'
            raw_content = content_bytes.decode(encoding, errors='replace')

            # --- Attempt format-specific parsing ---

            # 1. Attempt JSON parsing (explicit .json or application/json, OR if content looks like JSON)
            is_explicit_json = mime_type == 'application/json' or file_extension == '.json'
            looks_like_json = raw_content.strip().startswith('{') or raw_content.strip().startswith('[')

            if is_explicit_json or looks_like_json:
                try:
                    extracted_data = json.loads(raw_content)
                    processing_notes.append("Parsed as JSON.")
                    if not is_explicit_json:
                         processing_notes.append("Note: Content looked like JSON despite extension/mime.")
                         logger.warning(f"File '{filename}' identified as JSON content despite extension/mime.")
                    mime_type = 'application/json' # Update mime_type if successfully parsed as JSON
                except json.JSONDecodeError as e:
                    processing_notes.append(f"Failed to parse as JSON: {e}.")
                    if is_explicit_json:
                         logger.error(f"Explicit JSON file '{filename}' has invalid format: {e}")
                    else:
                         logger.warning(f"Content of '{filename}' looks like JSON but failed to parse: {e}")
                except Exception as e:
                    processing_notes.append(f"Error processing JSON: {e}.")
                    logger.error(f"Error processing JSON in '{filename}': {e}")

            # 2. Attempt XML parsing (if not already parsed as JSON, and looks like XML)
            # Add check if extracted_data is still None (meaning JSON parsing failed or wasn't attempted/relevant)
            looks_like_xml = extracted_data is None and raw_content.strip().startswith('<') and raw_content.strip().endswith('>') # Simple heuristic
            is_explicit_xml = extracted_data is None and (mime_type in ('application/xml', 'text/xml') or mime_type.endswith('+xml') or file_extension in ('.xml', '.xsd'))

            if extracted_data is None and (is_explicit_xml or looks_like_xml):
                 try:
                     root = ET.fromstring(raw_content)
                     # Convert XML element tree to a structured dictionary or string
                     # Simple string representation for QR code suitability
                     extracted_data = ET.tostring(root, encoding='unicode', method='xml')
                     processing_notes.append("Parsed as XML (text representation).")
                     if not is_explicit_xml:
                         processing_notes.append("Note: Content looked like XML despite extension/mime.")
                     # Update mime_type if successfully parsed as XML
                     if 'xml' not in mime_type: mime_type = 'application/xml'
                 except ET.ParseError as e:
                     processing_notes.append(f"Failed to parse as XML: {e}.")
                     if is_explicit_xml:
                         logger.error(f"Explicit XML file '{filename}' has invalid format: {e}")
                     else:
                         logger.warning(f"Content of '{filename}' looks like XML but failed to parse: {e}")
                 except Exception as e:
                     processing_notes.append(f"Error processing XML: {e}.")
                     logger.error(f"Error processing XML in '{filename}': {e}")


            # 3. Attempt CSV parsing (if not already parsed, and looks like CSV or is explicit CSV)
            is_explicit_csv = extracted_data is None and (mime_type == 'text/csv' or file_extension == '.csv')
            # Heuristic: check for commas/semicolons and multiple lines
            looks_like_csv = extracted_data is None and (',' in raw_content or ';' in raw_content) and ('\n' in raw_content or len(raw_content.splitlines()) > 1)

            if extracted_data is None and (is_explicit_csv or looks_like_csv):
                 try:
                     # Use Sniffer to guess dialect for better compatibility
                     dialect = 'excel' # Default dialect
                     try:
                          # Look at first few lines to guess dialect
                          sample = '\n'.join(raw_content.splitlines()[:10])
                          if sample:
                              dialect = csv.Sniffer().sniff(sample).name
                              logger.debug(f"Sniffer detected CSV dialect: {dialect} for '{filename}'")
                     except csv.Error:
                         logger.debug(f"Sniffer failed to detect dialect for '{filename}', using 'excel'.")
                         dialect = 'excel' # Fallback

                     # Read using the guessed or default dialect
                     csv_reader = csv.reader(io.StringIO(raw_content), dialect=dialect)
                     rows = list(csv_reader)

                     if rows:
                          # Limit the number of rows included for potentially huge CSVs
                          max_rows_preview = 100
                          extracted_data = {
                              'headers': rows[0] if rows[0] else None, # Assume first row is header
                              'rows': rows[1:max_rows_preview+1] # Get up to max_rows_preview data rows
                          }
                          if len(rows) > max_rows_preview + 1:
                              processing_notes.append(f"CSV truncated to {max_rows_preview} data rows.")
                          processing_notes.append("Parsed as CSV.")
                          if not is_explicit_csv:
                               processing_notes.append("Note: Content looked like CSV despite extension/mime.")
                          mime_type = 'text/csv' # Update mime_type

                     else:
                         extracted_data = "Empty CSV"
                         processing_notes.append("Parsed as empty CSV.")
                         if not is_explicit_csv:
                               processing_notes.append("Note: Content looked like CSV but was empty.")

                 except Exception as e:
                     processing_notes.append(f"Failed to parse as CSV: {e}.")
                     logger.warning(f"Failed to parse CSV from '{filename}': {e}")


            # 4. Attempt Document Text Extraction (if not already parsed)
            if extracted_data is None:
                 try:
                      extracted_text = None
                      if file_extension == '.pdf' and PDF_SUPPORT:
                          with tempfile.NamedTemporaryFile(delete=False, suffix='.pdf') as tmp_file:
                              tmp_file.write(content_bytes) # Write bytes to temp file
                              temp_path = Path(tmp_file.name)
                          try:
                              reader = PdfReader(temp_path)
                              text_content = "".join(page.extract_text() or "" for page in reader.pages)
                              extracted_text = text_content
                              processing_notes.append("Extracted text from PDF.")
                          finally:
                              temp_path.unlink() # Clean up temp file
                      elif file_extension == '.docx' and DOCX_SUPPORT:
                           with tempfile.NamedTemporaryFile(delete=False, suffix='.docx') as tmp_file:
                               tmp_file.write(content_bytes) # Write bytes to temp file
                               temp_path = Path(tmp_file.name)
                           try:
                               document = Document(temp_path)
                               text_content = "\n".join(paragraph.text for paragraph in document.paragraphs)
                               extracted_text = text_content
                               processing_notes.append("Extracted text from DOCX.")
                           finally:
                               temp_path.unlink() # Clean up temp file
                      elif file_extension == '.rtf' and RTF_SUPPORT:
                           # pyth can read directly from file-like object or string
                           try:
                                doc = Rtf15Reader.read(io.StringIO(raw_content))
                                text_content = PlaintextWriter.write(doc).getvalue()
                                extracted_text = text_content
                                processing_notes.append("Extracted text from RTF.")
                           except Exception as e:
                               processing_notes.append(f"RTF extraction error: {e}")
                               logger.warning(f"Failed to extract RTF text from '{filename}': {e}")
                      elif file_extension == '.odt' and ODT_SUPPORT:
                           with tempfile.NamedTemporaryFile(delete=False, suffix='.odt') as tmp_file:
                               tmp_file.write(content_bytes) # Write bytes to temp file
                               temp_path = Path(tmp_file.name)
                           try:
                                text_doc = OpenDocumentText(temp_path)
                                paragraphs = text_doc.getElementsByType(odftext.P)
                                text_content = "\n".join("".join(node.text for node in p.childNodes) for p in paragraphs)
                                extracted_text = text_content
                                processing_notes.append("Extracted text from ODT.")
                           finally:
                                temp_path.unlink() # Clean up temp file
                      elif file_extension in ['.doc', '.ppt', '.pptx', '.xls', '.xlsx']:
                           # These require more complex or platform-specific libraries (e.g. antiword, pandoc, COM objects on Windows)
                           processing_notes.append(f"Automatic text extraction for {file_extension.upper()} not fully implemented.")
                           logger.warning(f"Automatic text extraction for {file_extension.upper()} not fully implemented for '{filename}'.")

                      if extracted_text is not None:
                           # Limit extracted text size
                           max_extracted_text_size = 10000 # Limit text preview
                           extracted_data = {'text': extracted_text[:max_extracted_text_size]}
                           if len(extracted_text) > max_extracted_text_size:
                                extracted_data['text'] += "..."
                                processing_notes.append("Extracted text truncated.")

                 except ImportError as e:
                      processing_notes.append(f"Missing dependency for document type ({e}). Cannot extract text.")
                 except Exception as e:
                      processing_notes.append(f"Error during document text extraction: {e}")
                      logger.warning(f"Error during document text extraction for '{filename}': {e}")


            # 5. Fallback to Plain Text (if no specific extraction succeeded)
            if extracted_data is None:
                extracted_data = {'plain_text': raw_content}
                processing_notes.append("Stored as plain text.")
                # Re-guess mime type if it was something specific like application/octet-stream and we just got text
                if mime_type in ['unknown/unknown', 'application/octet-stream']:
                     guessed_text_mime, _ = mimetypes.guess_type('dummy.txt') # Use a dummy file name to guess plain text
                     if guessed_text_mime: mime_type = guessed_text_mime


        except Exception as e:
             # Catch errors during initial read or other unexpected issues
            logger.error(f"Fatal error processing single file '{filename}': {e}")
            processing_notes.append(f"Fatal processing error: {e}")
            raw_content = None # Ensure raw_content is None if reading failed
            extracted_data = None


        # Add file info to the entry
        entry = {
            'source': 'file',
            'filename': filename,
            'file_size': file_size,
            'mime_type': mime_type,
            'created': datetime.fromtimestamp(file_path.stat().st_ctime).isoformat() if file_path.exists() else None,
            'modified': datetime.fromtimestamp(file_path.stat().st_mtime).isoformat() if file_path.exists() else None,
            'raw_content': raw_content, # Always include raw content if readable
            'extracted_data': extracted_data, # Include the structured/extracted data
            'processing_notes': processing_notes # Include any notes/errors encountered
        }

        dataset_entries.append(entry)
        return dataset_entries


    def _process_archive(self, archive_path: Path, extract_to: Path) -> List[Dict]:
        """Process an archive file with enhanced extraction"""
        dataset = []
        archive_extension = archive_path.suffix.lower()
        logger.info(f"Processing archive: '{archive_path.name}'")

        try:
            if archive_extension == '.zip':
                if zipfile.is_zipfile(archive_path):
                    with zipfile.ZipFile(archive_path, 'r') as zip_ref:
                        for file_info in zip_ref.infolist():
                            if file_info.file_size > 0 and not file_info.filename.endswith('/'):
                                try:
                                    zip_ref.extract(file_info, path=extract_to)
                                    extracted_file_path = extract_to / file_info.filename
                                    # Recursively process the extracted file if it's supported and not an archive itself
                                    if extracted_file_path.suffix.lower() in self.supported_extensions and not self._is_archive(extracted_file_path):
                                         dataset.extend(self._process_single_file(extracted_file_path))
                                    elif extracted_file_path.suffix.lower() in self.archive_extensions:
                                         # Recursively process nested archives (careful with depth!)
                                         logger.info(f"Found nested archive '{file_info.filename}', processing recursively.")
                                         dataset.extend(self._process_archive(extracted_file_path, extract_to))
                                    else:
                                         logger.debug(f"Skipping unsupported file in archive: '{file_info.filename}'")
                                except Exception as e:
                                    logger.warning(f"Error extracting/processing file '{file_info.filename}' from zip '{archive_path.name}': {e}")
                else:
                     logger.error(f"'{archive_path.name}' is not a valid zip file.")

            elif archive_extension in ('.tar', '.gz', '.tgz'):
                try:
                    # Determine mode: 'r' for tar, 'r:gz' for tar.gz, 'r:bz2' for tar.bz2 (bz2 not fully supported yet)
                    mode = 'r'
                    if archive_extension in ('.tar.gz', '.tgz'): mode = 'r:gz'
                    # elif archive_extension == '.tar.bz2': mode = 'r:bz2' # Needs bz2 support
                    # Note: 'r:*' attempts to guess compression, safer to be explicit

                    with tarfile.open(archive_path, mode) as tar_ref:
                        for member in tar_ref.getmembers():
                            if member.isfile():
                                try:
                                    tar_ref.extract(member, path=extract_to)
                                    extracted_file_path = extract_to / member.name
                                     # Recursively process extracted file
                                    if extracted_file_path.suffix.lower() in self.supported_extensions and not self._is_archive(extracted_file_path):
                                         dataset.extend(self._process_single_file(extracted_file_path))
                                    elif extracted_file_path.suffix.lower() in self.archive_extensions:
                                         logger.info(f"Found nested archive '{member.name}', processing recursively.")
                                         dataset.extend(self._process_archive(extracted_file_path, extract_to))
                                    else:
                                         logger.debug(f"Skipping unsupported file in archive: '{member.name}'")
                                except Exception as e:
                                    logger.warning(f"Error extracting/processing file '{member.name}' from tar '{archive_path.name}': {e}")
                except tarfile.TarError as e:
                    logger.error(f"Error processing TAR archive '{archive_path.name}': {e}")

            elif archive_extension == '.gz':
                 # GZIP archives typically contain a single file. Extract it and process.
                 extracted_name = archive_path.stem # Get name without .gz
                 extracted_path = extract_to / extracted_name
                 try:
                     with gzip.open(archive_path, 'rb') as gz_file, open(extracted_path, 'wb') as outfile:
                         outfile.write(gz_file.read())
                     # Process the extracted file if supported
                     if extracted_path.suffix.lower() in self.supported_extensions and not self._is_archive(extracted_path):
                          dataset.extend(self._process_single_file(extracted_path))
                     elif extracted_path.suffix.lower() in self.archive_extensions:
                          logger.info(f"Found nested archive '{extracted_name}', processing recursively.")
                          dataset.extend(self._process_archive(extracted_path, extract_to))
                     else:
                          logger.debug(f"Skipping unsupported file (from gz): '{extracted_name}'")

                 except gzip.GzipFile as e:
                     logger.error(f"Error processing GZIP file '{archive_path.name}': {e}")
                 except Exception as e:
                     logger.error(f"Error extracting/processing from GZIP '{archive_path.name}': {e}")
                 finally:
                      if extracted_path.exists(): extracted_path.unlink() # Clean up extracted file

            # TODO: Add support for other archive types (.bz2, .7z, .rar)
            elif archive_extension in ('.bz2', '.7z', '.rar'):
                logger.warning(f"Support for {archive_extension} archives is not yet fully implemented and requires external tools/libraries.")

        except Exception as e:
            logger.error(f"Overall archive processing error for '{archive_path.name}': {e}")

        # Clean up extracted files in temp_dir after processing
        # Handled by context manager 'with tempfile.TemporaryDirectory()'


        return dataset

    def chunk_data(self, data: Union[Dict, List], max_size: int = 2953) -> List[Dict]:
        """Enhanced data chunking with sequence metadata"""
        try:
            # Convert data to JSON string
            # Use separators=(',', ':') to remove unnecessary whitespace for maximum data density in QR code
            json_str = json.dumps(data, ensure_ascii=False, separators=(',', ':'))
            total_length = len(json_str)

            # Calculate overhead for metadata
            # Metadata structure: {"idx":0,"tc":1,"tl":XXX,"hash":"YYYY","data":"..."}, shortened keys
            metadata_template = {
                "idx": 0, # chunk_index
                "tc": 1, # total_chunks
                "tl": total_length, # total_length
                "hash": "", # chunk_hash
                "data": "" # chunk_data
            }
            # Estimate overhead more accurately by dumping a sample metadata structure
            # and adding some safety margin. Shortened keys reduce overhead.
            overhead_estimate = len(json.dumps(metadata_template, separators=(',', ':'))) + 50 # Extra padding

            # Calculate effective chunk size
            effective_chunk_size = max_size - overhead_estimate

            if effective_chunk_size <= 0:
                 logger.error(f"Max QR size ({max_size}) is too small for metadata overhead ({overhead_estimate}). Cannot chunk.")
                 return []

            if total_length <= effective_chunk_size:
                # Data fits in one chunk
                chunk_data = json_str # Use the full string

                chunk = {
                    "idx": 0,
                    "tc": 1,
                    "tl": total_length,
                    "hash": hash(chunk_data) & 0xFFFFFFFF,  # 32-bit hash
                    "data": chunk_data
                }
                return [chunk]

            # Calculate number of chunks needed
            num_chunks = -(-total_length // effective_chunk_size)  # Ceiling division
            # Adjust chunk_size slightly to distribute evenly, maybe not strictly necessary
            # chunk_size = -(-total_length // num_chunks) # Use this if perfect distribution is needed

            chunks = []
            current_pos = 0
            for i in range(num_chunks):
                # Find the end of the current chunk. Avoid splitting in the middle of escaped characters or surrogate pairs if possible,
                # but simple slicing is usually okay for standard text that's already been errors='replace'.
                # We'll use basic slicing for simplicity, as the JSON string is just text.
                end_pos = min(current_pos + effective_chunk_size, total_length)

                # Basic attempt to not break in the middle of a UTF-8 character if slicing bytes,
                # but since we are slicing a *decoded string*, this is less of an issue.
                # However, slicing in the middle of JSON structure is bad.
                # For simplicity and robustness with arbitrary JSON structures, slicing the raw string is the easiest.
                chunk_data_str = json_str[current_pos:end_pos]

                chunk = {
                    "idx": i,
                    "tc": num_chunks,
                    "tl": total_length,
                    "hash": hash(chunk_data_str) & 0xFFFFFFFF,
                    "data": chunk_data_str
                }
                chunks.append(chunk)
                current_pos = end_pos

            # Final check: Ensure all data was chunked
            if current_pos < total_length:
                 # This shouldn't happen with correct ceiling division and min()
                 logger.error(f"Chunking logic error: Only processed {current_pos} of {total_length} characters.")
                 return [] # Indicate failure


            logger.info(f"Chunked data into {num_chunks} chunks for QR codes.")
            return chunks

        except Exception as e:
            logger.error(f"Error chunking data: {e}")
            return []

def generate_stylish_qr(data: Union[str, Dict],
                        filename: str,
                        size: int = 10,
                        border: int = 4,
                        fill_color: str = "#000000",
                        back_color: str = "#FFFFFF") -> str:
    """Generate a stylish QR code with enhanced visual appeal"""
    try:
        qr = qrcode.QRCode(
            version=None,
            error_correction=qrcode.constants.ERROR_CORRECT_M, # Increased error correction
            box_size=size,
            border=border
        )

        # Add data to QR code
        if isinstance(data, dict):
            # Use compact JSON representation
            qr.add_data(json.dumps(data, ensure_ascii=False, separators=(',', ':')))
        else:
            qr.add_data(str(data)) # Ensure it's a string

        qr.make(fit=True)

        # Create QR code image with custom colors
        qr_image = qr.make_image(fill_color=fill_color, back_color=back_color)

        # Convert to RGBA for transparency support and potential overlays
        qr_image = qr_image.convert('RGBA')

        # Optional: Add a small logo or icon in the center (requires design)
        # logo = Image.open("logo.png").convert("RGBA")
        # logo = logo.resize((logo.width // 4, logo.height // 4)) # Resize logo
        # logo_pos = ((qr_image.width - logo.width) // 2, (qr_image.height - logo.height) // 2)
        # qr_image.paste(logo, logo_pos, logo)

        # Add subtle gradient overlay (optional visual enhancement)
        try:
            gradient = Image.new('RGBA', qr_image.size, (0, 0, 0, 0))
            draw = ImageDraw.Draw(gradient)
            # Horizontal gradient for subtle effect
            for i in range(qr_image.width):
                # Fades from left (alpha=0) to right (max_alpha)
                alpha = int(255 * (i/qr_image.width) * 0.05)  # e.g., 5% maximum opacity fade-in
                draw.line([(i, 0), (i, qr_image.height)], fill=(0, 0, 0, alpha))
            # Combine images
            final_image = Image.alpha_composite(qr_image, gradient)
        except Exception as e:
             logger.warning(f"Failed to add gradient overlay to QR code: {e}. Using plain QR.")
             final_image = qr_image


        # Save the image
        output_path = QR_CODES_DIR / filename
        final_image.save(output_path, quality=90) # Save with slightly lower quality for smaller file size

        return str(output_path)
    except Exception as e:
        logger.error(f"QR generation error: {e}")
        return ""

def generate_qr_codes(data: Union[str, Dict, List], combined: bool = True) -> List[str]:
    """Generate QR codes with enhanced visual appeal and metadata"""
    # Assume 'data' here is the list of dictionaries produced by process_inputs
    if not isinstance(data, list):
        logger.error("generate_qr_codes received data that is not a list.")
        return []

    try:
        file_processor = EnhancedFileProcessor() # Use the enhanced processor for chunking
        paths = []

        if combined:
            # Process combined data
            chunks = file_processor.chunk_data(data) # chunk_data works on the list of dicts
            if not chunks:
                 logger.warning("No chunks generated for combined data.")
                 return []
            for i, chunk in enumerate(chunks):
                filename = f'combined_qr_{int(time.time())}_{i+1}_of_{len(chunks)}.png'
                qr_path = generate_stylish_qr(
                    data=chunk, # Pass the chunk dictionary
                    filename=filename,
                    fill_color="#1a365d",  # Deep blue
                    back_color="#ffffff"
                )
                if qr_path:
                    paths.append(qr_path)
                else:
                    logger.warning(f"Failed to generate QR for chunk {i+1}/{len(chunks)}.")
        else:
            # Process individual items (each dictionary in the list)
            if data: # Ensure data is not empty
                for idx, item in enumerate(data):
                    chunks = file_processor.chunk_data(item) # chunk_data works on individual dict
                    if not chunks:
                         logger.warning(f"No chunks generated for item {idx+1}.")
                         continue
                    for chunk_idx, chunk in enumerate(chunks):
                        filename = f'item_{idx+1}_chunk_{chunk_idx+1}_of_{len(chunks)}_{int(time.time())}.png'
                        qr_path = generate_stylish_qr(
                            data=chunk, # Pass the chunk dictionary
                            filename=filename,
                            fill_color="#1a365d",  # Deep blue
                            back_color="#ffffff"
                        )
                        if qr_path:
                            paths.append(qr_path)
                        else:
                            logger.warning(f"Failed to generate QR for item {idx+1} chunk {chunk_idx+1}/{len(chunks)}.")
            else:
                 logger.warning("No items in data list to process individually.")

        logger.info(f"Generated {len(paths)} QR codes.")
        return paths

    except Exception as e:
        logger.error(f"QR code generation error: {e}")
        return []

# Keep the Gradio UI definition and main function as they are,
# as the changes are internal to the processing classes and the
# process_inputs function already handles calling them and getting
# the combined list of results.

def create_modern_interface():
    """Create a modern and visually appealing Gradio interface"""

    # Modern CSS styling
    css = """
    /* Modern color scheme */
    :root {
        --primary-color: #1a365d;
        --secondary-color: #2d3748;
        --accent-color: #4299e1;
        --background-color: #f7fafc;
        --success-color: #48bb78;
        --error-color: #f56565;
        --warning-color: #ed8936;
    }
    /* Container styling */
    .container {
        max-width: 1200px;
        margin: auto;
        padding: 2rem;
        background-color: var(--background-color);
        border-radius: 1rem;
        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
    }
    /* Component styling */
    .input-container {
        background-color: white;
        padding: 1.5rem;
        border-radius: 0.5rem;
        border: 1px solid #e2e8f0;
        margin-bottom: 1rem;
    }
    /* Button styling */
    .primary-button {
        background-color: var(--primary-color);
        color: white;
        padding: 0.75rem 1.5rem;
        border-radius: 0.375rem;
        border: none;
        cursor: pointer;
        transition: all 0.2s;
    }
    .primary-button:hover {
        background-color: var(--accent-color);
        transform: translateY(-1px);
    }
    /* Status messages */
    .status {
        padding: 1rem;
        border-radius: 0.375rem;
        margin: 1rem 0;
    }
    .status.success { background-color: #f0fff4; color: var(--success-color); }
    .status.error { background-color: #fff5f5; color: var(--error-color); }
    .status.warning { background-color: #fffaf0; color: var(--warning-color); }
    /* Gallery styling */
    .gallery {
        display: grid;
        grid-template-columns: repeat(auto-fit, minmax(200px, 1fr));
        gap: 1rem;
        padding: 1rem;
        background-color: white;
        border-radius: 0.5rem;
        border: 1px solid #e2e8f0;
    }
    .gallery img {
        width: 100%;
        height: auto;
        border-radius: 0.375rem;
        transition: transform 0.2s;
    }
    .gallery img:hover {
        transform: scale(1.05);
    }
    /* QR Code Viewport Styling */
    .viewport-container {
        display: grid;
        gap: 0.5rem;
        padding: 1rem;
        background-color: white;
        border-radius: 0.5rem;
        border: 1px solid #e2e8f0;
        margin-top: 1rem;
    }
    .viewport-item {
        display: flex;
        flex-direction: column;
        align-items: center;
    }
    .viewport-item img {
        width: 100%;
        height: auto;
        border-radius: 0.375rem;
        transition: transform 0.2s;
        max-width: 150px; /* Adjust as needed */
        max-height: 150px; /* Adjust as needed */
    }
    """
    # Create interface with modern design
    with gr.Blocks(css=css, title="Advanced Data Processor & QR Generator") as interface:
        interface.head += """
        <script>
        let enabledStates = [];
        function updateEnabledStates(checkbox) {
            const index = parseInt(checkbox.dataset.index);
            if (checkbox.checked) {
                if (!enabledStates.includes(index)) {
                    enabledStates.push(index);
                }
            } else {
                enabledStates = enabledStates.filter(item => item !== index);
            }
            // Send the updated state back to Gradio (you might need to adjust the target component)
            const enabled_qr_codes_component = document.querySelector('[data-component-type="state"][data-state-name="enabled_qr_codes"]');
            if (enabled_qr_codes_component) {
                enabled_qr_codes_component.value = JSON.stringify(enabledStates);
                enabled_qr_codes_component.dispatchEvent(new Event('input')); // Trigger update
            }
            console.log("Enabled QR Code Indices:", enabledStates);
            // You might want to trigger an update of the viewport here if needed
        }
        </script>
        """
        qr_code_paths = gr.State([])
        gr.Markdown("""
        # 🌐 Advanced Data Processing & QR Code Generator
        Transform your data into beautifully designed, sequenced QR codes with our cutting-edge processor.
        """)
        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",
                value=""
            )
        with gr.Tab("📁 File Input"):
            file_input = gr.File(
                label="Upload Files",
                file_types=None,  # Accept all file types
                file_count="multiple"
            )
        with gr.Tab("📋 JSON Input"):
            text_input = gr.TextArea(
                label="Direct JSON Input",
                lines=15,
                placeholder="Paste your JSON data here...",
                value=""
            )
            with gr.Row():
                example_btn = gr.Button("📝 Load Example", variant="secondary")
                clear_btn = gr.Button("🗑️ Clear", variant="secondary")
        with gr.Row():
            combine_data = gr.Checkbox(
                label="Combine all data into sequence",
                value=True,
                info="Generate sequential QR codes for combined data"
            )
            process_btn = gr.Button(
                "🔄 Process & Generate QR",
                variant="primary"
            )
        # Output components
        output_json = gr.JSON(label="Processed Data")
        output_gallery = gr.Gallery(
            label="Generated QR Codes",
            columns=3,
            height=400,
            show_label=True
        )
        output_text = gr.Textbox(
            label="Processing Status",
            interactive=False
        )

        with gr.Tab("🖼️ QR Code Viewport") as viewport_tab:
            viewport_output = gr.HTML(label="QR Code Sequence Viewport")
            enabled_qr_codes = gr.State([]) # To store the enabled/disabled state

        # Load example data
        def load_example():
            example = {
                "type": "product_catalog",
                "items": [
                    {
                        "id": "123",
                        "name": "Premium Widget",
                        "description": "High-quality widget with advanced features",
                        "price": 299.99,
                        "category": "electronics",
                        "tags": ["premium", "featured", "new"]
                    },
                    {
                        "id": "456",
                        "name": "Basic Widget",
                        "description": "Reliable widget for everyday use",
                        "price": 149.99,
                        "category": "electronics",
                        "tags": ["basic", "popular"]
                    }
                ],
                "metadata": {
                    "timestamp": datetime.now().isoformat(),
                    "version": "2.0",
                    "source": "example"
                }
            }
            return json.dumps(example, indent=2)

        def clear_input():
            return "", None, "" # Clear url, files, text

        def update_viewport(paths, enabled_states):
            if not paths:
                return "<p>No QR codes generated yet.</p>"

            num_qr_codes = len(paths)
            cols = math.ceil(math.sqrt(num_qr_codes)) # Calculate columns for a roughly square grid
            cols = max(1, min(cols, 6)) # Limit max columns for small screens
            rows = math.ceil(num_qr_codes / cols)

            viewport_html = f'<div class="viewport-container" style="grid-template-columns: repeat({cols}, 1fr);">'.format(cols)

            # Initialize enabledStates if it's empty (first load)
            if not enabled_states and paths:
                 enabled_states = list(range(num_qr_codes)) # Enable all by default on first view

            for i, path in enumerate(paths):
                is_enabled = i in enabled_states
                border = "border: 2px solid green;" if is_enabled else "border: 2px solid lightgray;"
                opacity = "opacity: 1.0;" if is_enabled else "opacity: 0.5;"
                viewport_html += f'<div class="viewport-item" id="qr_item_{i}">'
                viewport_html += f'<img src="/file={path}" style="{border} {opacity}" alt="QR Code {i+1}">' # Use /file= for Gradio to serve static files
                viewport_html += f'<label><input type="checkbox" data-index="{i}" {"checked" if is_enabled else ""} onchange="updateEnabledStates(this)"> Enable</label>'
                viewport_html += '</div>'
            viewport_html += '</div>'

            return viewport_html

        def process_inputs(urls, files, text, combine):
            """Process all inputs and generate QR codes"""
            results = []
            processing_status_messages = []

            url_processor = EnhancedURLProcessor()
            file_processor = EnhancedFileProcessor()

            try:
                # Process JSON input
                if text and text.strip():
                    try:
                        json_data = json.loads(text)
                        # Wrap direct JSON input in a dictionary for consistency with file/URL output structure
                        results.append({
                            'source': 'json_input',
                            'extracted_data': json_data,
                            'timestamp': datetime.now().isoformat(),
                            'processing_notes': ['Parsed from direct JSON input.']
                        })
                        processing_status_messages.append("✅ Successfully parsed direct JSON input.")
                    except json.JSONDecodeError as e:
                        processing_status_messages.append(f"❌ Invalid JSON format in text input: {str(e)}")
                    except Exception as e:
                        processing_status_messages.append(f"❌ Error processing direct JSON input: {str(e)}")


                # Process URLs
                if urls and urls.strip():
                    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 = url_processor.validate_url(url)
                        if validation['is_valid']:
                            processing_status_messages.append(f"🌐 Fetching URL: {url}...")
                            content_result = url_processor.fetch_content(url)
                            if content_result:
                                results.append(content_result)
                                processing_status_messages.append(f"✅ Fetched and processed URL: {url}")
                            else:
                                processing_status_messages.append(f"❌ Failed to fetch/process URL: {url}")
                                if validation['details'].get('final_url'):
                                     processing_status_messages[-1] += f" (Redirected to {validation['details']['final_url']})"
                        else:
                            processing_status_messages.append(f"⚠️ Skipping invalid URL: {url} ({validation['message']})")

                # Process files
                if files:
                    for file in files:
                        processing_status_messages.append(f"📁 Processing file: {file.name}...")
                        file_results = file_processor.process_file(file)
                        if file_results:
                             results.extend(file_results)
                             processing_status_messages.append(f"✅ Processed file: {file.name}")
                        else:
                             processing_status_messages.append(f"❌ Failed to process file: {file.name}")

                # Generate QR codes
                qr_paths = []
                final_json_output = None

                if results:
                    # Use the collected results (list of dicts) for QR code generation
                    qr_paths = generate_qr_codes(results, combine)
                    final_json_output = results # Show the structured data in the JSON output box

                    if qr_paths:
                        processing_status_messages.append(f"✅ Successfully generated {len(qr_paths)} QR codes.")
                    else:
                        processing_status_messages.append("❌ Failed to generate QR codes.")

                else:
                    processing_status_messages.append("⚠️ No valid content collected from inputs.")


            except Exception as e:
                logger.error(f"Overall processing error in process_inputs: {e}")
                processing_status_messages.append(f"❌ An unexpected error occurred during processing: {str(e)}")

            return (
                final_json_output,
                [str(path) for path in qr_paths], # Gradio Gallery expects list of paths (strings)
                "\n".join(processing_status_messages) # Join status messages
            )

        def on_qr_generation(qr_paths_list):
             # When QR codes are generated, update the state with the list of paths
             # and initialize the enabled_qr_codes state with all indices enabled
             num_qrs = len(qr_paths_list)
             initial_enabled_states = list(range(num_qrs))
             return qr_paths_list, initial_enabled_states # Return paths list and initial enabled state


        # Link events
        example_btn.click(load_example, inputs=[], outputs=text_input)
        clear_btn.click(clear_input, inputs=[], outputs=[url_input, file_input, text_input]) # Clear all inputs

        process_btn.click(
            process_inputs,
            inputs=[url_input, file_input, text_input, combine_data],
            outputs=[output_json, output_gallery, output_text]
        ).then( # Chain a .then() to update the QR paths state and trigger viewport update
            on_qr_generation,
            inputs=[output_gallery], # Get the list of paths from the gallery output
            outputs=[qr_code_paths, enabled_qr_codes] # Update the state variables
        )

        # The viewport tab's select event will trigger update_viewport to render the grid
        viewport_tab.select(update_viewport, inputs=[qr_code_paths, enabled_qr_codes], outputs=[viewport_output])

        # Add helpful documentation
        gr.Markdown("""
        ### 🚀 Features
        - **Enhanced URL Scraping**: Extracts HTML text, title, meta description, links, and attempts parsing JSON/XML from URLs based on content type.
        - **Advanced File Processing**: Reads various text-based files (.txt, .md, .log etc.), HTML, XML, CSV, and attempts text extraction from common documents (.pdf, .docx, .rtf, .odt - *requires extra dependencies*).
        - **Smart JSON Handling**: Parses valid JSON from direct input, files (.json or content), or URLs.
        - **Archive Support**: Extracts and processes supported files from .zip, .tar, .gz archives.
        - **Robust Encoding Detection**: Uses `chardet` for reliable character encoding identification.
        - **Structured Output**: Provides a consistent JSON output format containing raw content (if applicable), extracted data, and processing notes for each processed item.
        - **Sequential QR Codes**: Maintains data integrity across multiple codes by chunking the combined/individual processed data.
        - **QR Code Viewport**: Visualize generated QR codes in a sequenced square grid with options to enable/disable individual codes for selective scanning/sharing.
        - **Modern Design**: Clean, responsive interface with visual feedback.
        ### 💡 Tips
        1.  **URLs**: Enter multiple URLs separated by commas or newlines. The processor will attempt to fetch and structure the content based on its type.
        2.  **Files**: Upload any type of file. The processor will attempt to handle supported text-based files, archives (.zip, .tar, .gz), and specific document/structured formats.
        3.  **JSON**: Use the "Direct JSON Input" tab for pasting JSON data. The system also tries to detect JSON content in file uploads and URLs. Use the "Load Example" button to see a sample JSON structure.
        4.  **Dependencies**: Processing PDF, DOCX, RTF, and ODT files requires installing optional Python libraries. Check the console logs for warnings if a library is missing.
        5.  **QR Codes**: Choose whether to "Combine all data into sequence" or generate separate sequences for each input item.
        6.  **Processing**: Monitor the "Processing Status" box for real-time updates and notes about errors or processing steps.
        7.  **Output**: The "Processed Data" JSON box shows the structured data extracted from your inputs. The "Generated QR Codes" gallery shows the QR code images.
        ### 🎨 Output Details
        -   The "Processed Data" JSON will be a list of dictionaries. Each dictionary represents one processed input (URL or file).
        -   Each item will have keys like `source`, `filename` (for files), `url` (for URLs), `mime_type`, `raw_content` (if readable), `extracted_data`, and `processing_notes`.
        -   `extracted_data` will contain the parsed/extracted content, structured according to the input type (e.g., dictionary for JSON, text for documents, list of rows for CSV, dictionary with title/text/links for HTML).
        -   `processing_notes` will list any issues encountered during extraction.
        -   Generated QR codes are saved in the `output/qr_codes` directory.
        ### ⚙️ QR Code Viewport Instructions
        1.  Navigate to the **QR Code Viewport** tab after generating QR codes.
        2.  The generated QR codes will be displayed in a grid based on their total count.
        3.  Use the checkboxes below each QR code to enable or disable it for visual selection. Enabled codes have a green border and full opacity.
        4.  This viewport is currently for visualization and selection *within the UI*; it doesn't change the generated files themselves. You would manually select which physical QR codes to scan based on this view.
        """)
    return interface

def main():
    """Initialize and launch the application"""
    try:
        # Configure system settings
        mimetypes.init()

        # Create and launch interface
        interface = create_modern_interface()

        # Launch with configuration
        interface.launch(
            share=False,
            debug=False, # Set to True for more verbose Gradio logging
            show_error=True,
            show_api=False
        )
    except Exception as e:
        logger.error(f"Application startup error: {e}")
        # Optionally print a user-friendly message before exiting
        print(f"\nFatal Error: {e}\nCheck the logs for details.")
        raise # Re-raise the exception to ensure the process exits if launch fails

if __name__ == "__main__":
    main()