app2.py

import json
import os
import re
import time
import logging
import mimetypes
import zipfile
import tempfile
import chardet
import io
import csv
import xml.etree.ElementTree as ET
from datetime import datetime
from typing import List, Dict, Optional, Union, Tuple, Any
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
import random
import pandas as pd
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry

# 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__)

# 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.")

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.")

try:
    from pyth.plugins.plaintext.writer import PlaintextWriter
    from pyth.plugins.rtf15.reader import Rtf15Reader
    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.")

# 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 and recursive link following."""

    def __init__(self):
        # Use a real requests session with retry strategy
        self.session = requests.Session()
        retry_strategy = Retry(
            total=3,
            backoff_factor=1,
            status_forcelist=[429, 500, 502, 503, 504],
            allowed_methods=["HEAD", "GET"]
        )
        adapter = HTTPAdapter(max_retries=retry_strategy)
        self.session.mount("http://", adapter)
        self.session.mount("https://", adapter)

        self.user_agent = UserAgent()
        self.timeout = 15 # seconds

    def validate_url(self, url: str) -> Dict[str, Any]:
        """Enhanced URL validation with accessibility check."""
        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:
            # Use a HEAD request to check accessibility without downloading full content
            headers = {'User-Agent': self.user_agent.random}
            response = self.session.head(url, timeout=self.timeout, headers=headers, allow_redirects=True)
            response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)

            # Check content type if available in HEAD response
            content_type = response.headers.get('Content-Type', '').split(';')[0].strip()
            if not content_type or not (content_type.startswith('text/') or 'json' in content_type or 'xml' in content_type):
                 # Basic check if content type seems relevant for text extraction
                 logger.warning(f"URL {url} returned potentially irrelevant content type: {content_type}")
                 # Decide if this should invalidate the URL or just add a note
                 # For now, we'll allow fetching but add a note.

            return {
                'is_valid': True,
                'message': 'URL is valid and accessible',
                'details': {
                    'final_url': response.url, # Capture final URL after redirects
                    'content_type': content_type,
                    'server': response.headers.get('Server', 'N/A'),
                    'size': response.headers.get('Content-Length', 'N/A')
                }
            }
        except requests.exceptions.RequestException as e:
            return {'is_valid': False, 'message': 'URL not accessible', 'details': str(e)}
        except Exception as e:
            logger.error(f"Unexpected error during URL validation for {url}: {e}")
            return {'is_valid': False, 'message': 'Unexpected validation error', 'details': str(e)}


    def fetch_content(self, url: str, retry_count: int = 0) -> Optional[Dict[str, Any]]:
        """Enhanced content fetcher with retry mechanism and complete character extraction."""
        try:
            logger.info(f"Fetching content from URL: {url} (Attempt {retry_count + 1})")
            headers = {'User-Agent': self.user_agent.random}
            response = self.session.get(url, timeout=self.timeout, headers=headers, allow_redirects=True)
            response.raise_for_status() # Raise HTTPError for bad responses (4xx or 5xx)

            final_url = response.url # Capture potential redirects
            content_type = response.headers.get('Content-Type', '').split(';')[0].strip()

            # Attempt to detect encoding if not specified in headers
            encoding = response.encoding # requests attempts to guess encoding
            if encoding is None or encoding == 'ISO-8859-1': # Fallback if requests guess is default/uncertain
                 try:
                     encoding_detection = chardet.detect(response.content)
                     encoding = encoding_detection['encoding'] or 'utf-8'
                     logger.debug(f"Chardet detected encoding: {encoding} for {url}")
                 except Exception as e:
                     logger.warning(f"Chardet detection failed for {url}: {e}. Falling back to utf-8.")
                     encoding = 'utf-8'


            raw_content = response.content.decode(encoding, errors='replace')

            # Extract metadata
            metadata = {
                'original_url': url,
                'final_url': final_url,
                'timestamp': datetime.now().isoformat(),
                'detected_encoding': encoding,
                'content_type': 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 for this step
                'raw_content': raw_content,
                'metadata': metadata,
                'extracted_data': processed_extraction['data'],
                'processing_notes': processed_extraction['notes']
            }
        except requests.exceptions.RequestException as e:
            logger.error(f"Failed to fetch content from {url}: {e}")
            return {
                 'source': 'url',
                 'url': url,
                 'raw_content': None,
                 'metadata': {'original_url': url, 'timestamp': datetime.now().isoformat(), 'status_code': getattr(e.response, 'status_code', None)},
                 '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(), 'status_code': None},
                '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
        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
                     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:
                     root = ET.fromstring(content)
                     xml_text = ET.tostring(root, encoding='unicode', method='xml')
                     extracted_data = xml_text
                     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:
                 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
                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
            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 and links."""
        extracted: Dict[str, Any] = {
            'title': None,
            'meta_description': None,
            'full_text': "",
            'links': []
        }
        try:
            soup = BeautifulSoup(content, 'html.parser')

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

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

            unique_links = set()
            for a_tag in soup.find_all('a', href=True):
                 href = a_tag['href'].strip()
                 if href and not href.startswith(('#', 'mailto:', 'tel:', 'javascript:')):
                    text = a_tag.get_text().strip()
                    try:
                         absolute_url = urljoin(base_url, href)
                         if absolute_url not in unique_links:
                             extracted['links'].append({'text': text, 'url': absolute_url})
                             unique_links.add(absolute_url)
                    except Exception:
                         if validators.url(href) and href not in unique_links:
                             extracted['links'].append({'text': text, 'url': href})
                             unique_links.add(href)
                         elif urlparse(href).netloc and href not in unique_links:
                              extracted['links'].append({'text': text, 'url': href})
                              unique_links.add(href)

            soup_copy = BeautifulSoup(content, 'html.parser')
            for script_or_style in soup_copy(["script", "style"]):
                script_or_style.extract()
            text = soup_copy.get_text(separator='\n')
            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}")
            soup_copy = BeautifulSoup(content, 'html.parser')
            for script_or_style in soup_copy(["script", "style"]):
                script_or_style.extract()
            extracted['full_text'] = soup_copy.get_text(separator='\n').strip()
            extracted['processing_error'] = f"Enhanced HTML processing failed: {e}"

        return extracted

    def fetch_content_with_depth(self, url: str, max_steps: int = 0) -> Dict[str, Any]:
        """Fetches content from a URL and recursively follows links up to max_steps depth."""
        if not isinstance(max_steps, int) or not (0 <= max_steps <= 10):
            logger.error(f"Invalid max_steps value: {max_steps}. Must be an integer between 0 and 10.")
            return {
                'url': url,
                'level': 0,
                'fetch_result': None,
                'linked_extractions': [],
                'processing_notes': [f"Invalid max_steps value: {max_steps}. Must be an integer between 0 and 10."]
            }

        validation_result = self.validate_url(url)
        if not validation_result['is_valid']:
            logger.error(f"Initial URL validation failed for {url}: {validation_result['message']}")
            return {
                'url': url,
                'level': 0,
                'fetch_result': None,
                'linked_extractions': [],
                'processing_notes': [f"Initial URL validation failed: {validation_result['message']}"]
            }

        # Use a set to keep track of visited URLs during the crawl to avoid infinite loops
        visited_urls = set()
        return self._fetch_content_recursive(url, max_steps, current_step=0, visited_urls=visited_urls)

    def _fetch_content_recursive(self, url: str, max_steps: int, current_step: int, visited_urls: set) -> Dict[str, Any]:
        """Recursive helper function to fetch content and follow links."""
        if current_step > max_steps:
            logger.debug(f"Depth limit ({max_steps}) reached for {url} at level {current_step}.")
            return {
                'url': url,
                'level': current_step,
                'fetch_result': None,
                'linked_extractions': [],
                'processing_notes': [f"Depth limit ({max_steps}) reached."]
            }

        # Normalize URL before checking visited set
        normalized_url = url.rstrip('/') # Simple normalization

        if normalized_url in visited_urls:
            logger.debug(f"Skipping already visited URL: {url} at level {current_step}.")
            return {
                'url': url,
                'level': current_step,
                'fetch_result': None, # Indicate not fetched in this run
                'linked_extractions': [],
                'processing_notes': ["URL already visited in this crawl."]
            }

        visited_urls.add(normalized_url) # Mark as visited

        logger.info(f"Processing URL: {url} at level {current_step}/{max_steps}")
        fetch_result = self.fetch_content(url)
        linked_extractions: List[Dict[str, Any]] = []

        if fetch_result and fetch_result.get('extracted_data') and 'text/html' in fetch_result.get('metadata', {}).get('content_type', '').lower():
            extracted_data = fetch_result['extracted_data']
            links = extracted_data.get('links', [])

            logger.info(f"Found {len(links)} potential links on {url} at level {current_step}. Proceeding to depth {current_step + 1}.")
            if current_step < max_steps:
                for link_info in links:
                    linked_url = link_info.get('url')
                    if linked_url:
                        # Ensure linked URL is absolute and potentially within the same domain
                        # Simple same-domain check (can be made more sophisticated)
                        try:
                            base_domain = urlparse(url).netloc
                            linked_domain = urlparse(linked_url).netloc
                            if linked_domain and linked_domain != base_domain:
                                logger.debug(f"Skipping external link: {linked_url}")
                                continue # Skip external links

                            # Recursively call for linked URLs
                            linked_result = self._fetch_content_recursive(linked_url, max_steps, current_step + 1, visited_urls)
                            if linked_result:
                                linked_extractions.append(linked_result)
                        except Exception as e:
                            logger.warning(f"Error processing linked URL {linked_url} from {url}: {e}")


        current_notes = fetch_result.get('processing_notes', []) if fetch_result else ['Fetch failed.']
        if f"Processed at level {current_step}" not in current_notes:
             current_notes.append(f"Processed at level {current_step}")

        return {
            'url': url,
            'level': current_step,
            'fetch_result': fetch_result,
            'linked_extractions': linked_extractions,
            'processing_notes': current_notes
        }


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
        self.supported_extensions = {
            '.txt', '.md', '.csv', '.json', '.xml', '.html', '.htm',
            '.log', '.yml', '.yaml', '.ini', '.conf', '.cfg',
            '.pdf', '.doc', '.docx', '.rtf', '.odt',
            '.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)

        if not file_path.exists():
             logger.error(f"File path does not exist: {file_path}")
             return [{
                 'source': 'file',
                 'filename': file.name if hasattr(file, 'name') else 'unknown',
                 'file_size': None,
                 'extracted_data': None,
                 'processing_notes': ['File path does not exist.']
             }]

        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)

                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:
                    dataset.extend(self._process_single_file(file_path))
                else:
                    logger.warning(f"Unsupported file type for processing: '{file_path.name}'. Attempting to read as plain text.")
                    try:
                         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},
                             '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: List[str] = []

        try:
            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')

            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'
                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}")

            looks_like_xml = extracted_data is None and raw_content.strip().startswith('<') and raw_content.strip().endswith('>')
            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)
                     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.")
                     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}")

            is_explicit_csv = extracted_data is None and (mime_type == 'text/csv' or file_extension == '.csv')
            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:
                     dialect = 'excel'
                     try:
                          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'

                     csv_reader = csv.reader(io.StringIO(raw_content), dialect=dialect)
                     rows = list(csv_reader)

                     if rows:
                          max_rows_preview = 100
                          extracted_data = {
                              'headers': rows[0] if rows and rows[0] else None,
                              'rows': rows[1:max_rows_preview+1] if len(rows) > 1 else []
                          }
                          if len(rows) > max_rows_preview + 1:
                              processing_notes.append(f"CSV data rows truncated to {max_rows_preview}.")
                          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'

                     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}")

            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)
                              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:
                              if temp_path.exists(): temp_path.unlink()
                      elif file_extension == '.docx' and DOCX_SUPPORT:
                           with tempfile.NamedTemporaryFile(delete=False, suffix='.docx') as tmp_file:
                               tmp_file.write(content_bytes)
                               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:
                               if temp_path.exists(): temp_path.unlink()
                      elif file_extension == '.rtf' and RTF_SUPPORT:
                           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)
                               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:
                                if temp_path.exists(): temp_path.unlink()
                      elif file_extension in ['.doc', '.ppt', '.pptx', '.xls', '.xlsx']:
                           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:
                           max_extracted_text_size = 10000
                           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}")

            if extracted_data is None:
                extracted_data = {'plain_text': raw_content}
                processing_notes.append("Stored as plain text.")
                if mime_type in ['unknown/unknown', 'application/octet-stream']:
                     guessed_text_mime, _ = mimetypes.guess_type('dummy.txt')
                     if guessed_text_mime: mime_type = guessed_text_mime

        except Exception as e:
            logger.error(f"Fatal error processing single file '{filename}': {e}")
            processing_notes.append(f"Fatal processing error: {e}")
            raw_content = None
            extracted_data = None

        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,
            'extracted_data': extracted_data,
            'processing_notes': processing_notes
        }

        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('/'):
                                sanitized_filename = Path(file_info.filename).name
                                extracted_file_path = extract_to / sanitized_filename
                                try:
                                    with zip_ref.open(file_info) as zf, open(extracted_file_path, 'wb') as outfile:
                                         outfile.write(zf.read())

                                    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 '{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}")
                                finally:
                                     if extracted_file_path.exists():
                                          try:
                                               extracted_file_path.unlink()
                                          except OSError as e:
                                               logger.warning(f"Failed to clean up extracted file {extracted_file_path}: {e}")
                else:
                     logger.error(f"'{archive_path.name}' is not a valid zip file.")

            elif archive_extension in ('.tar', '.gz', '.tgz'):
                try:
                    mode = 'r'
                    if archive_extension in ('.tar.gz', '.tgz'): mode = 'r:gz'

                    with tarfile.open(archive_path, mode) as tar_ref:
                        for member in tar_ref.getmembers():
                            if member.isfile():
                                sanitized_filename = Path(member.name).name
                                extracted_file_path = extract_to / sanitized_filename
                                try:
                                    if not str(extracted_file_path).startswith(str(extract_to)):
                                        logger.warning(f"Skipping potentially malicious path in tar: {member.name}")
                                        continue

                                    with tar_ref.extractfile(member) as tf, open(extracted_file_path, 'wb') as outfile:
                                         if tf:
                                              outfile.write(tf.read())
                                         else:
                                              logger.warning(f"Could not extract file-like object for {member.name} from tar.")
                                              continue

                                    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}")
                                finally:
                                     if extracted_file_path.exists():
                                          try:
                                               extracted_file_path.unlink()
                                          except OSError as e:
                                               logger.warning(f"Failed to clean up extracted file {extracted_file_path}: {e}")

                except tarfile.TarError as e:
                    logger.error(f"Error processing TAR archive '{archive_path.name}': {e}")

            elif archive_extension == '.gz':
                 extracted_name = archive_path.stem
                 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())
                     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():
                           try:
                                extracted_path.unlink()
                           except OSError as e:
                                logger.warning(f"Failed to clean up extracted file {extracted_path}: {e}")

            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}")

        return dataset

    def chunk_data(self, data: Union[Dict, List], max_size: int = 2953) -> List[Dict]:
        """Enhanced data chunking with sequence metadata"""
        try:
            json_str = json.dumps(data, ensure_ascii=False, separators=(',', ':'))
            total_length = len(json_str)

            metadata_template = {
                "idx": 0,
                "tc": 1,
                "tl": total_length,
                "hash": 0,
                "data": ""
            }
            metadata_template_with_hash = {**metadata_template, "hash": 1234567890}
            overhead_estimate = len(json.dumps(metadata_template_with_hash, separators=(',', ':'))) + 50

            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:
                chunk_data = json_str

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

            num_chunks = -(-total_length // effective_chunk_size)
            chunks = []
            current_pos = 0
            for i in range(num_chunks):
                end_pos = min(current_pos + effective_chunk_size, total_length)
                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

            if current_pos < total_length:
                 logger.error(f"Chunking logic error: Only processed {current_pos} of {total_length} characters.")
                 return []

            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,
            box_size=size,
            border=border
        )

        if isinstance(data, dict):
            qr.add_data(json.dumps(data, ensure_ascii=False, separators=(',', ':')))
        else:
            qr.add_data(str(data))

        qr.make(fit=True)

        qr_image = qr.make_image(fill_color=fill_color, back_color=back_color)
        qr_image = qr_image.convert('RGBA')

        try:
            gradient = Image.new('RGBA', qr_image.size, (0, 0, 0, 0))
            draw = ImageDraw.Draw(gradient)
            for i in range(qr_image.width):
                alpha = int(255 * (i/qr_image.width) * 0.05)
                draw.line([(i, 0), (i, qr_image.height)], fill=(0, 0, 0, alpha))
            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

        output_path = QR_CODES_DIR / filename
        final_image.save(output_path, quality=90)

        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"""
    if not isinstance(data, list):
        logger.error("generate_qr_codes received data that is not a list.")
        return []

    try:
        file_processor = EnhancedFileProcessor()
        paths = []

        if combined:
            chunks = file_processor.chunk_data(data)
            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,
                    filename=filename,
                    fill_color="#1a365d",
                    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:
            if data:
                for idx, item in enumerate(data):
                    chunks = file_processor.chunk_data(item)
                    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,
                            filename=filename,
                            fill_color="#1a365d",
                            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 []

# --- Chatbot Logic ---
def respond_to_chat(
    message: str, 
    chat_history: List[Tuple[str, str]], 
    chatbot_data: Optional[List[Dict]],
    # Add current_filtered_df_state as input, it will be updated and returned
    current_filtered_df_state: Optional[pd.DataFrame] 
) -> Tuple[List[Tuple[str, str]], List[Dict], Optional[pd.DataFrame]]:
    """
    Responds to user chat messages based on the loaded JSON data.
    Manages and returns the state of the filtered DataFrame.
    """
    if chatbot_data is None or not chatbot_data:
        chat_history.append((message, "Please process some data first using the other tabs before chatting."))
        return chat_history, chatbot_data, current_filtered_df_state # Return existing state

    chat_history.append((message, ""))

    response = ""
    lower_message = message.lower().strip()
    # Initialize new_filtered_df_state with the current state to preserve it unless a filter changes it
    new_filtered_df_state = current_filtered_df_state 

    try:
        # Attempt to flatten the data structure for easier querying
        flat_data = []
        def flatten_item(d, parent_key='', sep='_'):
            items = []
            if isinstance(d, dict):
                for k, v in d.items():
                    new_key = parent_key + sep + k if parent_key else k
                    if isinstance(v, (dict, list)):
                        items.extend(flatten_item(v, new_key, sep=sep).items())
                    else:
                        items.append((new_key, v))
            elif isinstance(d, list):
                for i, elem in enumerate(d):
                    if isinstance(elem, (dict, list)):
                         items.extend(flatten_item(elem, f'{parent_key}_{i}' if parent_key else str(i), sep=sep).items())
                    else:
                         items.append((f'{parent_key}_{i}' if parent_key else str(i), elem)) # Handle lists of non-dicts
            # Note: If the top-level chatbot_data is NOT a list of dicts, this flattening might need adjustment.
            # Assuming chatbot_data is a list of results, where each result is a dict.

            return dict(items)

        # Process each top-level item in chatbot_data
        for i, item in enumerate(chatbot_data):
            if isinstance(item, dict):
                 # Flatten the 'extracted_data' part if it exists and is a dict/list
                 extracted_data_part = item.get('extracted_data')
                 if isinstance(extracted_data_part, (dict, list)):
                      flat_item_data = flatten_item(extracted_data_part, parent_key=f'item_{i}_extracted_data')
                      # Include some top-level metadata if useful
                      metadata_part = {k: v for k, v in item.items() if k not in ['extracted_data', 'raw_content', 'linked_extractions']}
                      flat_data.append({**metadata_part, **flat_item_data})
                 else:
                      # If extracted_data is not dict/list, just include top-level keys
                      flat_data.append({k: v for k, v in item.items() if k != 'raw_content'}) # Exclude raw_content

            elif isinstance(item, list):
                 # If a top-level item is a list itself (less common for single file/URL results but possible), flatten it
                 flat_data.extend(flatten_item(item, parent_key=f'item_{i}'))
            else:
                 # Handle cases where top-level item is not a dict or list
                 flat_data.append({f'item_{i}_value': item})


        df = None
        if flat_data:
            try:
                df = pd.DataFrame(flat_data)
                logger.debug(f"Created DataFrame with shape: {df.shape}")
                logger.debug(f"DataFrame columns: {list(df.columns)}")
            except Exception as e:
                logger.warning(f"Could not create pandas DataFrame from processed data: {e}. Falling back to manual processing.")
                df = None

        # --- Complex Queries and Analysis ---

        if df is not None:
            # List available columns
            if "what columns are available" in lower_message or "list columns" in lower_message:
                 response = f"The available columns in the data are: {', '.join(df.columns)}"

            # Describe a specific column
            match = re.search(r'describe column (\w+)', lower_message)
            if match:
                 column_name = match.group(1)
                 if column_name in df.columns:
                      description = df[column_name].describe().to_string()
                      response = f"Description for column '{column_name}':\n```\n{description}\n```"
                 else:
                      response = f"I couldn't find a column named '{column_name}'. Available columns are: {', '.join(df.columns)}"


            # How many unique values in a column?
            match = re.search(r'how many unique values in (\w+)', lower_message)
            if match:
                column_name = match.group(1)
                if column_name in df.columns:
                    unique_count = df[column_name].nunique()
                    response = f"There are {unique_count} unique values in the '{column_name}' column."
                else:
                    response = f"I couldn't find a column named '{column_name}' in the data. Available columns are: {', '.join(df.columns)}"

            # What is the average/sum/min/max of a numeric column?
            match = re.search(r'what is the (average|sum|min|max) of (\w+)', lower_message)
            if match:
                operation, column_name = match.groups()
                if column_name in df.columns:
                    try:
                        numeric_col = pd.to_numeric(df[column_name], errors='coerce')
                        numeric_col = numeric_col.dropna()

                        if not numeric_col.empty:
                            if operation == 'average':
                                result = numeric_col.mean()
                                response = f"The average of '{column_name}' is {result:.2f}."
                            elif operation == 'sum':
                                result = numeric_col.sum()
                                response = f"The sum of '{column_name}' is {result:.2f}."
                            elif operation == 'min':
                                result = numeric_col.min()
                                response = f"The minimum of '{column_name}' is {result}."
                            elif operation == 'max':
                                result = numeric_col.max()
                                response = f"The maximum of '{column_name}' is {result}."
                            else:
                                response = "I can calculate average, sum, min, or max."
                        else:
                            response = f"The column '{column_name}' does not contain numeric values that I can analyze."
                    except Exception as e:
                        response = f"An error occurred while calculating the {operation} of '{column_name}': {e}"
                        logger.error(f"Error calculating {operation} for column '{column_name}': {e}")
                else:
                    response = f"I couldn't find a column named '{column_name}'. Available columns are: {', '.join(df.columns)}"

            # Enhanced Filter data based on more complex conditions
            # Regex to capture: "filter by status active", "show items where category is 'electronics'", "find entries where price > 100"
            # It tries to capture:
            # 1. column_name (e.g., category, status, price)
            # 2. operator (e.g., is, equals, =, >, <, contains, starts with, ends with) - flexible operators
            # 3. value (e.g., 'electronics', active, 100) - can be quoted or unquoted
            filter_match = re.search(
                r'(?:filter|show items|show me items|find entries|select items|get items)\s+' # Optional action phrases
                r'(?:where|by|for|with|if)\s+' # Keyword indicating condition
                r'(\w+)\s+' # Column name
                r'(is|equals?|==|!=|>=?|<=?|contains?|starts with|ends with)\s+' # Operator
                r'([\'"]?[\w\s.-]+[\'"]?)', # Value (allows spaces, dots, hyphens if quoted, or single words)
                lower_message
            )

            if filter_match:
                column_name, operator, value_str = filter_match.groups()
                column_name = column_name.strip()
                operator = operator.strip().lower()
                value_str = value_str.strip().strip("'\"")

                logger.info(f"Filter request: Column='{column_name}', Operator='{operator}', Value='{value_str}'")

                if column_name not in df.columns:
                    response = f"I couldn't find a column named '{column_name}'. Available columns are: {', '.join(df.columns)}"
                    new_filtered_df_state = None # Clear previous filter if column not found
                else:
                    # IMPORTANT: Always filter from the original full dataframe 'df'
                    active_df_to_filter = df.copy() 
                    try:
                        # Attempt to infer value type for comparison
                        target_value: Any
                        col_dtype = df[column_name].dtype

                        if pd.api.types.is_numeric_dtype(col_dtype) and operator in ['>', '>=', '<', '<=', '==', '!=']:
                            try:
                                target_value = float(value_str)
                                col_series = pd.to_numeric(filtered_df[column_name], errors='coerce')
                            except ValueError:
                                response = f"For numeric column '{column_name}', '{value_str}' is not a valid number."
                                target_value = None # Error case
                        elif pd.api.types.is_bool_dtype(col_dtype) or value_str.lower() in ['true', 'false']:
                            target_value = value_str.lower() == 'true'
                            col_series = filtered_df[column_name].astype(bool, errors='ignore')
                        else: # Assume string comparison otherwise
                            target_value = str(value_str)
                            col_series = filtered_df[column_name].astype(str).str.lower() # Case-insensitive for strings
                            value_str_lower = target_value.lower()


                        if 'response' not in locals(): # If no type conversion error occurred
                            if operator in ['is', 'equals', '==']:
                                if pd.api.types.is_numeric_dtype(col_dtype) or pd.api.types.is_bool_dtype(col_dtype):
                                    condition = col_series == target_value
                                else: # String comparison
                                    condition = col_series == value_str_lower
                            elif operator == '!=':
                                if pd.api.types.is_numeric_dtype(col_dtype) or pd.api.types.is_bool_dtype(col_dtype):
                                    condition = col_series != target_value
                                else: # String comparison
                                    condition = col_series != value_str_lower
                            elif operator == '>' and pd.api.types.is_numeric_dtype(col_dtype):
                                condition = col_series > target_value
                            elif operator == '>=' and pd.api.types.is_numeric_dtype(col_dtype):
                                condition = col_series >= target_value
                            elif operator == '<' and pd.api.types.is_numeric_dtype(col_dtype):
                                condition = col_series < target_value
                            elif operator == '<=' and pd.api.types.is_numeric_dtype(col_dtype):
                                condition = col_series <= target_value
                            elif operator in ['contains', 'contain'] and pd.api.types.is_string_dtype(col_series):
                                condition = col_series.str.contains(value_str_lower, case=False, na=False)
                            elif operator == 'starts with' and pd.api.types.is_string_dtype(col_series):
                                condition = col_series.str.startswith(value_str_lower, na=False)
                            elif operator == 'ends with' and pd.api.types.is_string_dtype(col_series):
                                condition = col_series.str.endswith(value_str_lower, na=False)
                            else:
                                response = f"Unsupported operator '{operator}' for column '{column_name}' (type: {col_dtype})."
                                condition = None
                                # If operator was bad, response is set, clear filter state
                                if response: new_filtered_df_state = None


                            if condition is not None:
                                # Apply condition to the active_df_to_filter (which is a copy of the full df)
                                filtered_results_df = active_df_to_filter[condition]
                                if not filtered_results_df.empty:
                                    new_filtered_df_state = filtered_results_df # Update state with new filter result
                                    num_results = len(filtered_results_df)
                                    preview_rows = min(num_results, 5)
                                    preview_cols = min(len(filtered_results_df.columns), 5)
                                    
                                    preview_df = filtered_results_df.head(preview_rows).iloc[:, :preview_cols]
                                    preview_str = preview_df.to_string(index=False)
                                    
                                    response = (f"Found {num_results} items where '{column_name}' {operator} '{value_str}'.\n"
                                                f"Here's a preview:\n```\n{preview_str}\n```\n"
                                                f"The full filtered dataset is now available for download using the 'Download Filtered JSON' button.")
                                else:
                                    new_filtered_df_state = pd.DataFrame() # Store empty DF for "no results"
                                    response = f"No items found where '{column_name}' {operator} '{value_str}'."
                            # If condition is None (e.g. bad operator) and response not already set by type check, set generic invalid op message.
                            elif not response: # Avoid overwriting specific error from type check
                                response = f"Unsupported operator '{operator}' for column '{column_name}' (type: {col_dtype})."
                                new_filtered_df_state = None


                    except ValueError as ve: # Specifically catch ValueError for target_value conversion
                        response = f"Invalid value '{value_str}' for numeric column '{column_name}'. {ve}"
                        new_filtered_df_state = None # Clear on value error
                        logger.warning(f"ValueError during filter: {ve}")
                    except Exception as e:
                        new_filtered_df_state = None # Clear on other errors
                        response = f"An error occurred while applying the filter: {e}"
                        logger.error(f"Error applying filter (column='{column_name}', op='{operator}', val='{value_str}'): {e}")
                # If the message was a filter, new_filtered_df_state is now set (or None/empty if error/no results)
            
            # --- End of Enhanced Filter Logic ---

            # If `response` is still empty, it means no filter query was matched by the filter_match regex.
            # In this case, new_filtered_df_state (initialized from current_filtered_df_state) remains unchanged.


            # Request structured output (e.g., as CSV or simplified JSON)
            # This section should act on the *original* df unless specifically asked for filtered data export.
            # The new download buttons handle filtered data export separately.
            # Let's assume for now it acts on the original df, and a separate command would be needed for "export filtered data"
            # If no filter query matched, and no other specific df query matched,
            # then `response` might still be empty. `new_filtered_df_state` will be the same as `current_filtered_df_state`.
            # The general queries below should not reset `new_filtered_df_state` unless it's a "clear" command.

            elif "output as csv" in lower_message or "export as csv" in lower_message:
                 if df is not None and not df.empty:
                      csv_output = df.to_csv(index=False)
                      response = f"Here is the data in CSV format:\n```csv\n{csv_output[:1000]}...\n```\n(Output truncated for chat display)"
                 else:
                      response = "There is no data available to output as CSV."
            elif "output as json" in lower_message or "export as json" in lower_message: # Note: "export as json" is different from download buttons
                 if df is not None and not df.empty:
                      json_output = df.to_json(orient='records', indent=2)
                      response = f"Here is the data in JSON format:\n```json\n{json_output[:1000]}...\n```\n(Output truncated for chat display)"
                 else:
                      response = "There is no data available to output as JSON."

        # --- General Queries (if no DataFrame or specific query matched AND no filter was applied in this turn) ---
        # These should not clear new_filtered_df_state unless it's a "clear chat"
        if not response: # Only enter if no response has been generated by DataFrame/filter logic
            if "how many items" in lower_message or "number of items" in lower_message:
                if new_filtered_df_state is not None and not new_filtered_df_state.empty:
                    response = f"The currently filtered dataset has {len(new_filtered_df_state)} items. The original dataset has {len(df if df is not None else chatbot_data)} items."
                elif df is not None: # Check df from original chatbot_data
                    response = f"There are {len(df)} top-level items in the processed data."
                elif isinstance(chatbot_data, list): # Fallback if df creation failed but chatbot_data is list
                    response = f"There are {len(chatbot_data)} top-level items in the processed data (not in DataFrame)."
                elif isinstance(chatbot_data, dict):
                    response = "The processed data is a single dictionary, not a list of items."
                else:
                    response = "The processed data is not a standard list or dictionary structure."

            elif "what is the structure" in lower_message or "tell me about the data" in lower_message:
                if new_filtered_df_state is not None and not new_filtered_df_state.empty:
                     response = f"The filtered data has columns: {', '.join(new_filtered_df_state.columns)}. "
                     if df is not None:
                         response += f"The original data has columns: {', '.join(df.columns)}."
                     else:
                         response += "Original data structure is not tabular."
                elif df is not None:
                     response = f"The data is a table with {len(df)} rows and columns: {', '.join(df.columns)}."
                elif isinstance(chatbot_data, list) and chatbot_data:
                    sample_item = chatbot_data[0]
                    response = f"The data is a list containing {len(chatbot_data)} items. The first item has the following top-level keys: {list(sample_item.keys())}."
                elif isinstance(chatbot_data, dict):
                     response = f"The data is a dictionary with the following top-level keys: {list(chatbot_data.keys())}."
                else:
                    response = "The processed data is not a standard list or dictionary structure that I can easily describe."

            # "show me" without a filter condition might be ambiguous.
            # Let's assume it refers to the original data or provide guidance.
            elif "show me" in lower_message or "get me" in lower_message or "extract" in lower_message:
                 # This specific 'show me' without 'where' should not trigger a filter or clear existing filter state.
                 # It's a general request for data, which is too broad. Guide the user.
                 response = "If you want to filter the data, please use a phrase like 'show me items where column_name is value'. If you want to see the raw data, consider using the download buttons."

            # --- Speculation about Modifications ---
            elif "how can i modify" in lower_message or "how to change" in lower_message or "can i add" in lower_message or "can i remove" in lower_message:
                 response = "I cannot directly modify the data here, but I can tell you how you *could* modify it. What kind of change are you considering (e.g., adding an item, changing a value, removing a field)?"
            elif "add a field" in lower_message or "add a column" in lower_message:
                 response = "To add a field (or column if the data is tabular), you would typically iterate through each item (or row) in the data and add the new key-value pair. For example, adding a 'status' field with a default value."
            elif "change a value" in lower_message or "update a field" in lower_message:
                 response = "To change a value, you would need to identify the specific item(s) and the field you want to update. You could use a condition (like filtering) to find the right items and then assign a new value to the field."
            elif "remove a field" in lower_message or "delete a column" in lower_message:
                 response = "To remove a field, you would iterate through each item and delete the specified key. Be careful, as this is irreversible."
            elif "restructure" in lower_message or "change the format" in lower_message:
                 response = "Restructuring data involves transforming it into a different shape. This could mean flattening nested objects, grouping items, or pivoting data. This often requires writing custom code to map the old structure to the new one."
            elif "what if i" in lower_message or "if i changed" in lower_message:
                 response = "Tell me what specific change you're contemplating, and I can speculate on the potential impact or how you might approach it programmatically."

            # --- General Conversation / Fallback ---
            elif "hello" in lower_message or "hi" in lower_message:
                response = random.choice(["Hello! How can I help you understand the processed data?", "Hi there! What's on your mind about this data?", "Hey! Ask me anything about the data you've loaded."])
            elif "thank you" in lower_message or "thanks" in lower_message:
                response = random.choice(["You're welcome!", "Glad I could help.", "No problem! Let me know if you have more questions about the data."])
            elif "clear chat" in lower_message: # This should be caught by button, but as text too
                 chat_history = [] # Gradio handles this for the Chatbot component via button
                 response = "Chat history cleared."
                 new_filtered_df_state = None # Also clear filtered data on "clear chat" command by text
            elif not response: # Fallback if nothing else matched
                 response = random.choice([
                    "I can analyze the data you've processed. What would you like to know? Try asking to filter data, e.g., 'show items where status is active'.",
                    "Ask me about the number of items, the structure, or values of specific fields. You can also filter data.",
                    "I can perform basic analysis or filter the data. For example: 'filter by price > 100'.",
                    "Tell me what you want to extract or filter from the data. Use phrases like 'show items where ...'.",
                    "I'm equipped to filter your data. Try 'find entries where name contains widget'."
                 ])

    except Exception as e:
        logger.error(f"Chatbot runtime error: {e}")
        response = f"An internal error occurred while processing your request: {e}"
        response += "\nPlease try rephrasing your question or clear the chat history."
        # On unexpected error, preserve the current_filtered_df_state rather than clearing or modifying it.
        # new_filtered_df_state = current_filtered_df_state # This line is effectively already done by initialization

    if not response: # Final safety net for response, if it's somehow still empty
        response = "I'm not sure how to respond to that. Please try rephrasing or ask for help on available commands."

    if chat_history and chat_history[-1][1] == "": # If last history entry is (user_msg, "")
        chat_history[-1] = (chat_history[-1][0], response)
    # else: # This case should ideally not be reached if chat_history.append((message, "")) is always called first.
    #    chat_history.append((message, response)) # Avoids duplicate user message if something went wrong

    return chat_history, chatbot_data, new_filtered_df_state

# --- Gradio Interface Definition ---
def create_modern_interface():
    """Create a modern and visually appealing Gradio interface"""

    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;
        max-height: 150px;
    }
    """
    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);
            }
            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'));
            }
            console.log("Enabled QR Code Indices:", enabledStates);
        }
        </script>
        """
        with gr.Row():
            crawl_depth_slider = gr.Slider(
                label="Crawl Depth",
                minimum=0,
                maximum=10,
                value=0,
                step=1,
                interactive=True,
                info="Select the maximum depth for crawling links (0-10)."
            )

        qr_code_paths = gr.State([])
        chatbot_data = gr.State(None)
        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,
                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"
            )
            generate_qr_toggle = gr.Checkbox(
                label="Generate QR Codes",
                value=False, # Default to False as per task
                info="Enable to generate QR codes for the processed data."
            )
            process_btn = gr.Button(
                "🔄 Process & Generate QR",
                variant="primary"
            )
        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([])

        with gr.Tab("🤖 Chat with Data") as chat_tab:
             chat_history = gr.State([])
             chatbot = gr.Chatbot(label="Data Chatbot")
             filtered_chatbot_df_state = gr.State(None) # To store the filtered DataFrame

             with gr.Row():
                  chat_input = gr.Textbox(label="Your Message", placeholder="Ask me about the processed data...")
                  send_msg_btn = gr.Button("Send")
             with gr.Row():
                download_full_json_btn = gr.Button("Download Full JSON")
                download_filtered_json_btn = gr.Button("Download Filtered JSON")
             download_file_output = gr.File(label="Download Data", interactive=False) # For triggering download
             clear_chat_btn = gr.Button("Clear Chat History")


        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, "", None

        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))
            cols = max(1, min(cols, 6))

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

            if enabled_states is None or len(enabled_states) != num_qr_codes:
                 enabled_states = list(range(num_qr_codes))

            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}">'
                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, crawl_depth, generate_qr_enabled):
            """Process all inputs and generate QR codes based on toggle"""
            results = []
            processing_status_messages = []

            url_processor = EnhancedURLProcessor()
            file_processor = EnhancedFileProcessor()

            try:
                if text and text.strip():
                    try:
                        json_data = json.loads(text)
                        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)}")

                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:
                        processing_status_messages.append(f"🌐 Processing URL: {url} with crawl depth {crawl_depth}...")
                        content_result = url_processor.fetch_content_with_depth(url, max_steps=crawl_depth)
                        if content_result: # Check if a result dictionary was returned
                             results.append(content_result)
                             if content_result.get('fetch_result') is not None:
                                 processing_status_messages.append(f"✅ Processed URL: {url} (Level 0)")
                                 if content_result.get('processing_notes'):
                                      processing_status_messages.append(f"   Notes: {'; '.join(content_result['processing_notes'])}")

                                 if content_result.get('linked_extractions'):
                                      num_linked_processed = len([r for r in content_result['linked_extractions'] if r and r.get('fetch_result') is not None])
                                      processing_status_messages.append(f"   Found and processed {num_linked_processed}/{len(content_result['linked_extractions'])} direct links.")
                             else:
                                 processing_status_messages.append(f"❌ Failed to process URL: {url}")
                                 if content_result.get('processing_notes'):
                                      processing_status_messages.append(f"   Notes: {'; '.join(content_result['processing_notes'])}")
                        else:
                             processing_status_messages.append(f"❌ Failed to process URL: {url} (No result returned)")


                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}")
                             for res in file_results:
                                  if res.get('processing_notes'):
                                       processing_status_messages.append(f"   Notes for {res.get('filename', 'item')}: {'; '.join(res['processing_notes'])}")
                        else:
                             processing_status_messages.append(f"❌ Failed to process file: {file.name}")

                qr_paths = []
                final_json_output = None
                qr_paths = []

                if results:
                    final_json_output = results # Assign processed data regardless of QR generation
                    if generate_qr_enabled:
                        processing_status_messages.append("⚙️ Generating QR codes as requested...")
                        qr_paths = generate_qr_codes(results, combine)
                        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 (empty result or error).")
                    else:
                        processing_status_messages.append("☑️ QR code generation was disabled. Processed data is available.")
                        qr_paths = [] # Ensure it's empty
                else:
                    processing_status_messages.append("⚠️ No valid content collected from inputs.")
                    final_json_output = {} # Ensure output_json is cleared if no results

            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],
                "\n".join(processing_status_messages),
                final_json_output
            )

        def on_qr_generation(qr_paths_list):
            if qr_paths_list is None:
                num_qrs = 0
            else:
                num_qrs = len(qr_paths_list)

            initial_enabled_states = list(range(num_qrs))
            return qr_paths_list, initial_enabled_states

        example_btn.click(load_example, inputs=[], outputs=text_input)
        clear_btn.click(clear_input, inputs=[], outputs=[url_input, file_input, text_input, chatbot_data])

        process_btn.click(
            process_inputs,
            inputs=[url_input, file_input, text_input, combine_data, crawl_depth_slider, generate_qr_toggle],
            outputs=[output_json, output_gallery, output_text, chatbot_data]
        ).then(
            on_qr_generation,
            inputs=[output_gallery],
            outputs=[qr_code_paths, enabled_qr_codes]
        )

        viewport_tab.select(update_viewport, inputs=[qr_code_paths, enabled_qr_codes], outputs=[viewport_output])

        send_msg_btn.click(
            respond_to_chat,
            inputs=[chat_input, chat_history, chatbot_data, filtered_chatbot_df_state],
            outputs=[chatbot, chatbot_data, filtered_chatbot_df_state]
        ).then(
            lambda: "",
            inputs=None,
            outputs=chat_input
        )

        chat_input.submit(
            respond_to_chat,
            inputs=[chat_input, chat_history, chatbot_data, filtered_chatbot_df_state],
            outputs=[chatbot, chatbot_data, filtered_chatbot_df_state]
        ).then(
            lambda: "",
            inputs=None,
            outputs=chat_input
        )

        clear_chat_btn.click(
            lambda: ([], None), # Clear chat history and filtered data state
            inputs=None,
            outputs=[chatbot, filtered_chatbot_df_state]
        )

        # --- Download Logic ---
        def download_json_data(data_df: Optional[pd.DataFrame], filename_prefix: str) -> Optional[str]:
            if data_df is None or data_df.empty:
                logger.info(f"No data provided for download with prefix '{filename_prefix}'.")
                return None
            try:
                data_list = data_df.to_dict(orient='records')
                json_str = json.dumps(data_list, indent=2, ensure_ascii=False)
                
                timestamp = int(time.time())
                filename = f"{filename_prefix}_{timestamp}.json"
                file_path = TEMP_DIR / filename
                
                with open(file_path, 'w', encoding='utf-8') as f:
                    f.write(json_str)
                
                logger.info(f"Successfully created JSON file for download: {file_path}")
                return str(file_path)
            except Exception as e:
                logger.error(f"Error creating JSON file for {filename_prefix}: {e}")
                return None

        def handle_download_full_json(current_chatbot_data_state: Optional[List[Dict]]) -> Optional[str]:
            if not current_chatbot_data_state:
                logger.info("No full data available to download.")
                # Optionally, could return a gr.Warning or gr.Info to the UI if we had a dedicated status Textbox for downloads
                return None
            
            # The chatbot_data state is a list of dicts. Convert to DataFrame for download_json_data.
            # The df created in respond_to_chat is not directly used here to ensure we get the *original* full data.
            try:
                # A similar flattening logic as in respond_to_chat might be needed if chatbot_data_state is complex
                # For now, assume it's a list of flat dictionaries or can be handled by pd.DataFrame directly.
                # If chatbot_data_state originates from `results` in `process_inputs`, it's a list of dicts.
                # A more robust approach would be to re-use the exact flattening from respond_to_chat if structures are nested.
                # Let's try a direct conversion first.
                df_to_download = pd.DataFrame(current_chatbot_data_state)
                if df_to_download.empty:
                    logger.info("Full data resulted in an empty DataFrame. Nothing to download.")
                    return None
            except Exception as e:
                logger.error(f"Error converting full chatbot_data to DataFrame for download: {e}")
                return None
            
            return download_json_data(df_to_download, "full_data")

        def handle_download_filtered_json(current_filtered_df_state: Optional[pd.DataFrame]) -> Optional[str]:
            if current_filtered_df_state is None or current_filtered_df_state.empty:
                logger.info("No filtered data available to download.")
                # Consider gr.Info("No filtered data to download.") if a text output for this is desired.
                return None
            return download_json_data(current_filtered_df_state, "filtered_data")

        download_full_json_btn.click(
            fn=handle_download_full_json,
            inputs=[chatbot_data], # chatbot_data is the gr.State holding the full dataset (List[Dict])
            outputs=[download_file_output]
        )
        download_filtered_json_btn.click(
            fn=handle_download_filtered_json,
            inputs=[filtered_chatbot_df_state], # This state holds the filtered DataFrame
            outputs=[download_file_output]
        )

        gr.Markdown("""
        ### 🚀 Features
        - **Enhanced URL Scraping**: Extracts HTML text, title, meta description, links, and attempts parsing JSON/XML from URLs based on content type. Supports crawling links up to a specified depth. **(Now performs real fetching)**
        - **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*). **(Now performs real file processing)**
        - **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. **(Now performs real extraction)**
        - **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.
        - **Data Chatbot**: Interact conversationally with the processed JSON data to ask questions about its structure, content, or request specific information.
        ### 💡 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, following links up to the specified **Crawl Depth**.
        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 (`PyPDF2`, `python-docx`, `pyth`, `odfpy`). 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.
        8.  **Chatbot**: After processing data, go to the "Chat with Data" tab to ask questions about the JSON output.
        ### ⚙️ 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:
        mimetypes.init()
        interface = create_modern_interface()
        interface.launch(
            share=False,
            debug=False,
            show_error=True,
            show_api=False
        )
    except Exception as e:
        logger.error(f"Application startup error: {e}")
        print(f"\nFatal Error: {e}\nCheck the logs for details.")
        raise

if __name__ == "__main__":
    main()