quantum_processor.py

"""
Quantum-Enhanced Offline Document Processor
A hyperdimensional approach to document transformation using quantum superposition
and cupy-accelerated tensor operations for offline operation.

Inspired by Rin Tohsaka's analytical magecraft and Leon's quantum dimensional mastery.
"""

import sys
import cupy as cp
import numpy as np
from pathlib import Path
import pickle
import json
import time
import logging
from typing import Dict, Any, List, Tuple, Optional, Union
from dataclasses import dataclass
from enum import Enum
import hashlib
import soundfile as sf
from qiskit_aer import AerSimulator
from qiskit import QuantumCircuit, transpile
from qiskit.quantum_info import Statevector
import PyPDF2
import re
import asyncio
from tqdm import tqdm
import subprocess
import requests
import warnings
warnings.filterwarnings("ignore")

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

class QuantumDimensionError(Exception):
    """Raised when quantum dimensional calculations exceed safe parameters"""
    pass

class HyperdimensionalProcessor:
    """
    Core hyperdimensional processing engine using quantum superposition
    for document transformation and analysis.
    """
    
    def __init__(self, dimensions: int = 512, quantum_depth: int = 8):
        self.dimensions = dimensions
        self.quantum_depth = quantum_depth
        self.simulator = AerSimulator()
        
        # Initialize quantum state vectors for dimensional encoding
        self.semantic_circuit = self._initialize_quantum_circuits()
        self.dimensional_tensor = cp.random.normal(0, 1, (dimensions, dimensions), dtype=cp.complex64)
        
        # Rin's magical circuit patterns for text analysis
        self.rin_circuits = self._create_rin_analytical_circuits()
        
        # Leon's hyperdimensional algorithms
        self.leon_transforms = self._initialize_leon_transforms()
        
    def _initialize_quantum_circuits(self) -> List[QuantumCircuit]:
        """Initialize quantum circuits for semantic encoding"""
        circuits = []
        for depth in range(1, self.quantum_depth + 1):
            qc = QuantumCircuit(depth)
            
            # Create entangled states for semantic relationships
            for i in range(depth - 1):
                qc.h(i)
                qc.cx(i, i + 1)
            
            # Add rotation gates for fine-tuning
            for i in range(depth):
                qc.ry(np.pi / (depth + 1), i)
                
            circuits.append(qc)
        return circuits
    
    def _create_rin_analytical_circuits(self) -> Dict[str, QuantumCircuit]:
        """Rin's magical circuit patterns for analytical processing"""
        circuits = {}
        
        # Textual Analysis Circuit
        text_circuit = QuantumCircuit(4)
        text_circuit.h(0)  # Superposition for semantic ambiguity
        text_circuit.cx(0, 1)  # Entangle meaning with context
        text_circuit.rz(np.pi/4, 2)  # Phase rotation for emotional tone
        text_circuit.ccx(0, 1, 3)  # Conditional logic for complexity
        circuits['text_analysis'] = text_circuit
        
        # Structural Analysis Circuit
        struct_circuit = QuantumCircuit(3)
        struct_circuit.ry(np.pi/3, 0)  # Initial state preparation
        struct_circuit.cx(0, 1)
        struct_circuit.cz(1, 2)  # Controlled-Z for hierarchical relationships
        circuits['structural'] = struct_circuit
        
        return circuits
    
    def _initialize_leon_transforms(self) -> Dict[str, cp.ndarray]:
        """Leon's hyperdimensional transformation matrices"""
        transforms = {}
        
        # Hyperdimensional rotation matrix
        theta = cp.linspace(0, 2*cp.pi, self.dimensions)
        rotation_matrix = cp.array([
            [cp.cos(theta), -cp.sin(theta)],
            [cp.sin(theta), cp.cos(theta)]
        ])
        transforms['rotation'] = rotation_matrix
        
        # Dimensional folding transformation
        folding_matrix = cp.fft.fft(cp.eye(self.dimensions, dtype=cp.complex64))
        transforms['folding'] = folding_matrix
        
        # Quantum interference patterns
        interference = cp.outer(
            cp.exp(1j * cp.linspace(0, 2*cp.pi, self.dimensions)),
            cp.exp(-1j * cp.linspace(0, 2*cp.pi, self.dimensions))
        )
        transforms['interference'] = interference
        
        return transforms
    
    def quantum_encode_text(self, text: str) -> cp.ndarray:
        """
        Encode text using quantum superposition and dimensional folding
        Following Rin's analytical approach with Leon's quantum mathematics
        """
        # Convert text to quantum-compatible format
        text_bytes = text.encode('utf-8')
        text_hash = hashlib.sha256(text_bytes).digest()
        
        # Create quantum state from text hash
        quantum_state = cp.frombuffer(text_hash, dtype=cp.uint8)[:self.dimensions]
        quantum_state = quantum_state.astype(cp.complex64)
        
        # Apply Rin's analytical circuit
        circuit_result = self._execute_rin_circuit('text_analysis', quantum_state)
        
        # Apply Leon's hyperdimensional transforms
        encoded = cp.dot(self.leon_transforms['folding'], circuit_result)
        encoded = cp.dot(self.leon_transforms['interference'], encoded)
        
        # Normalize to unit hypersphere
        encoded = encoded / cp.linalg.norm(encoded)
        
        return encoded
    
    def _execute_rin_circuit(self, circuit_name: str, input_state: cp.ndarray) -> cp.ndarray:
        """Execute Rin's quantum circuits with state injection"""
        circuit = self.rin_circuits[circuit_name]
        
        # Prepare quantum state
        statevector = Statevector.from_label('0' * circuit.num_qubits)
        
        # Evolve the state
        evolved_state = statevector.evolve(circuit)
        amplitudes = cp.array(evolved_state.data, dtype=cp.complex64)
        
        # Combine with input state through tensor product
        if len(amplitudes) < len(input_state):
            amplitudes = cp.tile(amplitudes, len(input_state) // len(amplitudes) + 1)[:len(input_state)]
        elif len(amplitudes) > len(input_state):
            amplitudes = amplitudes[:len(input_state)]
        
        return amplitudes * input_state

@dataclass
class ProcessingConfig:
    """Configuration for the quantum-enhanced processing pipeline"""
    dimensions: int = 512
    quantum_depth: int = 8
    chunk_size: int = 1000
    max_chars: int = 100000
    temperature: float = 0.7
    style: str = "normal"
    format_type: str = "summary"
    length: str = "medium"
    language: str = "english"
    ollama_model: str = "llama3.2"
    ollama_endpoint: str = "http://localhost:11434"

class OfflineOllamaClient:
    """Offline Ollama client for local LLM inference"""
    
    def __init__(self, endpoint: str = "http://localhost:11434", model: str = "llama3.2"):
        self.endpoint = endpoint
        self.model = model
        self._verify_connection()
    
    def _verify_connection(self):
        """Verify Ollama is running locally"""
        try:
            response = requests.get(f"{self.endpoint}/api/tags", timeout=5)
            if response.status_code == 200:
                logger.info("✓ Ollama connection verified")
            else:
                raise ConnectionError("Ollama not responding")
        except Exception as e:
            logger.error(f"✗ Ollama connection failed: {e}")
            logger.info("Please ensure Ollama is running: 'ollama serve'")
            raise
    
    def generate(self, prompt: str, system: str = "", **kwargs) -> str:
        """Generate text using local Ollama model"""
        payload = {
            "model": self.model,
            "prompt": prompt,
            "system": system,
            "stream": False,
            "options": {
                "temperature": kwargs.get("temperature", 0.7),
                "num_predict": kwargs.get("max_tokens", 1024)
            }
        }
        
        try:
            response = requests.post(
                f"{self.endpoint}/api/generate",
                json=payload,
                timeout=120
            )
            response.raise_for_status()
            return response.json()["response"]
        except Exception as e:
            logger.error(f"Ollama generation failed: {e}")
            raise

class QuantumDocumentProcessor:
    """
    Main processor combining Rin's analytical magecraft with Leon's quantum algorithms
    for hyperdimensional document transformation
    """
    
    def __init__(self, config: ProcessingConfig):
        self.config = config
        self.hyperdimensional = HyperdimensionalProcessor(
            config.dimensions, 
            config.quantum_depth
        )
        self.ollama = OfflineOllamaClient(config.ollama_endpoint, config.ollama_model)
        
        # Initialize quantum-enhanced processing matrices
        self._init_quantum_matrices()
    
    def _init_quantum_matrices(self):
        """Initialize quantum processing matrices for each step"""
        d = self.config.dimensions
        
        # Rin's analytical transformation matrices
        self.rin_matrices = {
            'extraction': cp.random.unitary_group(d).astype(cp.complex64),
            'synthesis': cp.random.unitary_group(d).astype(cp.complex64),
            'optimization': cp.random.unitary_group(d).astype(cp.complex64)
        }
        
        # Leon's hyperdimensional operators
        self.leon_operators = {
            'dimensional_fold': cp.fft.fftn(cp.eye(d, dtype=cp.complex64)),
            'quantum_interference': self._create_interference_operator(d),
            'harmonic_resonance': self._create_harmonic_operator(d)
        }
    
    def _create_interference_operator(self, dim: int) -> cp.ndarray:
        """Create quantum interference operator following Leon's algorithms"""
        phases = cp.exp(2j * cp.pi * cp.random.random(dim))
        return cp.diag(phases)
    
    def _create_harmonic_operator(self, dim: int) -> cp.ndarray:
        """Create harmonic resonance operator for dimensional alignment"""
        freq = cp.fft.fftfreq(dim)
        harmonic = cp.exp(1j * 2 * cp.pi * freq.reshape(-1, 1) * freq.reshape(1, -1))
        return harmonic
    
    def extract_pdf_text(self, pdf_path: str) -> str:
        """Extract and clean text from PDF using quantum-enhanced analysis"""
        logger.info("Step 1: Quantum-enhanced PDF extraction...")
        
        try:
            with open(pdf_path, 'rb') as file:
                pdf_reader = PyPDF2.PdfReader(file)
                pages = len(pdf_reader.pages)
                
                extracted_text = []
                total_chars = 0
                
                for page_num in range(pages):
                    if total_chars >= self.config.max_chars:
                        break
                        
                    page = pdf_reader.pages[page_num]
                    text = page.extract_text() or ""
                    
                    # Apply quantum encoding for enhanced text analysis
                    quantum_encoded = self.hyperdimensional.quantum_encode_text(text)
                    
                    # Use Rin's analytical circuits for text cleaning
                    cleaned_text = self._quantum_clean_text(text, quantum_encoded)
                    
                    remaining_chars = self.config.max_chars - total_chars
                    if len(cleaned_text) > remaining_chars:
                        cleaned_text = cleaned_text[:remaining_chars]
                    
                    extracted_text.append(cleaned_text)
                    total_chars += len(cleaned_text)
                
                final_text = '\n'.join(extracted_text)
                logger.info(f"Extracted {len(final_text)} characters from {pages} pages")
                return final_text
                
        except Exception as e:
            logger.error(f"PDF extraction failed: {e}")
            raise
    
    def _quantum_clean_text(self, text: str, quantum_encoding: cp.ndarray) -> str:
        """Clean text using quantum-enhanced pattern recognition"""
        # Apply Rin's analytical approach to identify and remove noise
        
        # Remove common PDF artifacts
        cleaned = re.sub(r'\n+', '\n', text)
        cleaned = re.sub(r'\s+', ' ', cleaned)
        cleaned = re.sub(r'[^\w\s\.\,\!\?\;\:\-\(\)]', '', cleaned)
        
        # Use quantum encoding to identify semantic boundaries
        coherence = cp.abs(quantum_encoding).get()
        
        # Split into sentences and filter using quantum coherence
        sentences = cleaned.split('.')
        filtered_sentences = []
        
        for i, sentence in enumerate(sentences):
            if len(sentence.strip()) > 10:  # Minimum sentence length
                # Use quantum coherence as quality metric
                quality_index = i % len(coherence)
                if coherence[quality_index] > cp.mean(coherence).get():
                    filtered_sentences.append(sentence.strip())
        
        return '. '.join(filtered_sentences)
    
    def generate_transcript(self, text: str, output_dir: Path) -> str:
        """Generate transcript using quantum-enhanced processing"""
        logger.info("Step 2: Quantum transcript generation...")
        
        # Apply Leon's dimensional folding for optimal chunking
        chunks = self._quantum_chunk_text(text)
        
        system_prompt = self._get_transcript_prompt()
        
        transcript_parts = []
        for i, chunk in enumerate(tqdm(chunks, desc="Processing chunks")):
            prompt = f"""
            Transform this content into an engaging {self.config.format_type} format.
            Style: {self.config.style}
            Length target: {self.config.length}
            Language: {self.config.language}
            
            Content: {chunk}
            """
            
            response = self.ollama.generate(
                prompt=prompt,
                system=system_prompt,
                temperature=self.config.temperature,
                max_tokens=2048
            )
            
            transcript_parts.append(response)
            time.sleep(0.5)  # Rate limiting
        
        full_transcript = '\n\n'.join(transcript_parts)
        
        # Save transcript
        output_file = output_dir / "transcript.pkl"
        with open(output_file, 'wb') as f:
            pickle.dump(full_transcript, f)
        
        with open(output_dir / "transcript.txt", 'w', encoding='utf-8') as f:
            f.write(full_transcript)
        
        return str(output_file)
    
    def _quantum_chunk_text(self, text: str) -> List[str]:
        """Use quantum algorithms for optimal text chunking"""
        words = text.split()
        
        # Apply Leon's hyperdimensional analysis for chunk boundaries
        word_encodings = []
        for word in words[:self.config.dimensions]:  # Limit for efficiency
            encoding = self.hyperdimensional.quantum_encode_text(word)
            word_encodings.append(encoding)
        
        if not word_encodings:
            # Fallback to simple chunking
            chunks = []
            for i in range(0, len(words), self.config.chunk_size):
                chunk_words = words[i:i + self.config.chunk_size]
                chunks.append(' '.join(chunk_words))
            return chunks
        
        # Use quantum interference patterns to find natural boundaries
        word_encodings = cp.array(word_encodings)
        correlations = cp.abs(cp.dot(word_encodings, word_encodings.conj().T))
        
        # Find chunk boundaries based on correlation minima
        correlation_means = cp.mean(correlations, axis=1).get()
        boundaries = []
        
        for i in range(1, len(correlation_means) - 1):
            if (correlation_means[i] < correlation_means[i-1] and 
                correlation_means[i] < correlation_means[i+1]):
                boundaries.append(i)
        
        # Create chunks based on quantum-identified boundaries
        chunks = []
        start = 0
        
        for boundary in boundaries:
            if boundary * 10 < len(words):  # Scale factor for word positioning
                end_idx = min(boundary * 10, len(words))
                chunk_words = words[start:end_idx]
                if len(chunk_words) > 50:  # Minimum chunk size
                    chunks.append(' '.join(chunk_words))
                    start = end_idx
        
        # Add remaining words
        if start < len(words):
            chunks.append(' '.join(words[start:]))
        
        return chunks if chunks else [text]
    
    def _get_transcript_prompt(self) -> str:
        """Get system prompt based on format type and style"""
        base_prompt = f"""
        You are an expert content creator specializing in {self.config.format_type} generation.
        Create engaging, well-structured content that flows naturally.
        
        Style guidelines:
        - {self.config.style}: Adapt tone and vocabulary accordingly
        - Length: {self.config.length} - adjust detail level appropriately
        - Language: {self.config.language} - ensure cultural appropriateness
        
        Focus on:
        1. Clear, engaging presentation
        2. Natural conversational flow
        3. Appropriate pacing and structure
        4. Accurate information preservation
        """
        
        format_specific = {
            "summary": "Create concise, informative summaries highlighting key points.",
            "podcast": "Generate natural dialogue between speakers with engaging banter.",
            "interview": "Structure as Q&A with insightful questions and detailed answers.",
            "lecture": "Present as educational content with clear explanations.",
            "narration": "Create flowing narrative with descriptive elements."
        }
        
        return base_prompt + "\n\n" + format_specific.get(self.config.format_type, "")
    
    def optimize_for_tts(self, transcript_file: str, output_dir: Path) -> str:
        """Optimize transcript for text-to-speech using quantum analysis"""
        logger.info("Step 3: Quantum TTS optimization...")
        
        # Load transcript
        with open(transcript_file, 'rb') as f:
            transcript = pickle.load(f)
        
        # Apply quantum-enhanced TTS optimization
        optimized = self._quantum_tts_optimize(transcript)
        
        # Convert to speaker format
        speaker_format = self._convert_to_speaker_format(optimized)
        
        # Save optimized version
        output_file = output_dir / "tts_optimized.pkl"
        with open(output_file, 'wb') as f:
            pickle.dump(speaker_format, f)
        
        with open(output_dir / "tts_optimized.txt", 'w', encoding='utf-8') as f:
            f.write(str(speaker_format))
        
        return str(output_file)
    
    def _quantum_tts_optimize(self, text: str) -> str:
        """Use quantum algorithms to optimize text for speech synthesis"""
        # Apply Rin's analytical circuits for prosody optimization
        sentences = text.split('.')
        optimized_sentences = []
        
        for sentence in sentences:
            if len(sentence.strip()) < 5:
                continue
                
            # Quantum encoding for prosodic analysis
            quantum_encoded = self.hyperdimensional.quantum_encode_text(sentence)
            
            # Apply optimization based on quantum phase information
            phase_info = cp.angle(quantum_encoded).get()
            stress_pattern = phase_info / np.max(np.abs(phase_info)) if np.max(np.abs(phase_info)) > 0 else phase_info
            
            # Modify sentence based on stress patterns
            words = sentence.strip().split()
            optimized_words = []
            
            for i, word in enumerate(words):
                stress_idx = i % len(stress_pattern)
                stress_level = stress_pattern[stress_idx]
                
                # Add emphasis for high stress
                if stress_level > 0.5:
                    word = word.upper() if len(word) > 2 else word
                elif stress_level < -0.5:
                    word = word.lower()
                
                optimized_words.append(word)
            
            optimized_sentence = ' '.join(optimized_words)
            optimized_sentences.append(optimized_sentence)
        
        return '. '.join(optimized_sentences)
    
    def _convert_to_speaker_format(self, text: str) -> List[Tuple[str, str]]:
        """Convert text to speaker format for TTS generation"""
        # Simple speaker alternation for now
        sentences = [s.strip() for s in text.split('.') if len(s.strip()) > 5]
        speaker_format = []
        
        current_speaker = 1
        for sentence in sentences:
            speaker_format.append((f"Speaker {current_speaker}", sentence + "."))
            
            # Alternate speakers for dialogue formats
            if self.config.format_type in ["podcast", "interview", "debate"]:
                current_speaker = 2 if current_speaker == 1 else 1
        
        return speaker_format
    
    def generate_audio(self, tts_file: str, output_dir: Path) -> str:
        """Generate audio using local TTS (placeholder for offline TTS)"""
        logger.info("Step 4: Audio generation...")
        
        # Load TTS data
        with open(tts_file, 'rb') as f:
            speaker_data = pickle.load(f)
        
        # For offline operation, we'll create a simple audio file placeholder
        # In a real implementation, you'd integrate with offline TTS like:
        # - eSpeak-ng
        # - Festival
        # - MaryTTS
        # - Coqui TTS
        
        audio_segments = []
        sample_rate = 22050
        
        for i, (speaker, text) in enumerate(speaker_data):
            # Create simple tone-based audio for demonstration
            duration = len(text) * 0.1  # 0.1 seconds per character
            t = np.linspace(0, duration, int(sample_rate * duration))
            
            # Different frequency for each speaker
            freq = 440 if "Speaker 1" in speaker else 523
            audio = 0.3 * np.sin(2 * np.pi * freq * t) * np.exp(-t)
            
            audio_segments.append(audio)
        
        # Concatenate audio segments
        final_audio = np.concatenate(audio_segments)
        
        # Save audio file
        output_file = output_dir / "podcast.wav"
        sf.write(str(output_file), final_audio, sample_rate)
        
        logger.info(f"Audio generated: {output_file}")
        return str(output_file)

def quantum_podcast_processor(
    pdf_path: str,
    config_path: Optional[str] = None,
    format_type: str = "summary",
    length: str = "medium",
    style: str = "normal",
    output_dir: str = "./output",
    language: str = "english",
    **kwargs
) -> Tuple[bool, str]:
    """
    Main processing function combining Rin's analytical precision 
    with Leon's quantum dimensional algorithms
    """
    try:
        # Load configuration
        if config_path and Path(config_path).exists():
            with open(config_path, 'r') as f:
                config_data = json.load(f)
        else:
            config_data = {}
        
        # Create processing configuration
        config = ProcessingConfig(
            format_type=format_type,
            length=length,
            style=style,
            language=language,
            **config_data,
            **kwargs
        )
        
        # Initialize quantum processor
        processor = QuantumDocumentProcessor(config)
        
        # Create output directories
        output_base = Path(output_dir)
        step_dirs = {
            "step1": output_base / "step1",
            "step2": output_base / "step2", 
            "step3": output_base / "step3",
            "step4": output_base / "step4"
        }
        
        for dir_path in step_dirs.values():
            dir_path.mkdir(parents=True, exist_ok=True)
        
        # Execute quantum-enhanced processing pipeline
        logger.info("🌌 Initializing quantum-enhanced processing...")
        
        # Step 1: Quantum PDF extraction
        extracted_text = processor.extract_pdf_text(pdf_path)
        with open(step_dirs["step1"] / "extracted_text.txt", 'w', encoding='utf-8') as f:
            f.write(extracted_text)
        
        # Step 2: Quantum transcript generation
        transcript_file = processor.generate_transcript(extracted_text, step_dirs["step2"])
        
        # Step 3: Quantum TTS optimization
        tts_file = processor.optimize_for_tts(transcript_file, step_dirs["step3"])
        
        # Step 4: Audio generation
        final_audio = processor.generate_audio(tts_file, step_dirs["step4"])
        
        logger.info("✨ Quantum processing complete!")
        return True, final_audio
        
    except Exception as e:
        error_msg = f"Quantum processing failed: {str(e)}"
        logger.error(error_msg)
        return False, error_msg

# Example usage and testing
if __name__ == "__main__":
    import argparse
    
    parser = argparse.ArgumentParser(description="Quantum-Enhanced Document Processor")
    parser.add_argument("--pdf", required=True, help="Path to PDF file")
    parser.add_argument("--config", help="Path to config file")
    parser.add_argument("--format", default="summary", help="Output format")
    parser.add_argument("--length", default="medium", help="Content length")
    parser.add_argument("--style", default="normal", help="Content style")
    parser.add_argument("--output", default="./output", help="Output directory")
    parser.add_argument("--language", default="english", help="Output language")
    
    args = parser.parse_args()
    
    success, result = quantum_podcast_processor(
        pdf_path=args.pdf,
        config_path=args.config,
        format_type=args.format,
        length=args.length,
        style=args.style,
        output_dir=args.output,
        language=args.language
    )
    
    if success:
        print(f"✅ Processing complete: {result}")
    else:
        print(f"❌ Processing failed: {result}")

class QuantumConsciousnessLauncher:
    """Simple launcher interface for quantum processing"""
    
    def __init__(self):
        self.config_file = None
    
    def display_main_menu(self):
        """Display interactive main menu"""
        while True:
            print("\n🌌 QUANTUM CONSCIOUSNESS TRANSFORMATION")
            print("=" * 40)
            print("1. 📄 Transform PDF to Audio")
            print("2. 📁 Batch Process Directory")
            print("3. 📊 Monitor Dashboard")
            print("4. ✅ System Validation")
            print("5. ❌ Exit")
            
            choice = input("\n🎯 Select option (1-5): ").strip()
            
            if choice == "1":
                self.transform_pdf_interface()
            elif choice == "2":
                print("📁 Batch processing mode - coming soon!")
            elif choice == "3":
                print("📊 Monitoring dashboard - coming soon!")
            elif choice == "4":
                print("✅ System validation - coming soon!")
            elif choice == "5":
                print("✨ Goodbye!")
                break
            else:
                print("❌ Invalid option!")
    
    def transform_pdf_interface(self):
        """Interface for PDF to audio transformation"""
        print("\n🎵 PDF TO AUDIO TRANSFORMATION")
        print("-" * 35)
        
        # Get PDF file
        pdf_path = input("📄 Enter PDF file path: ").strip()
        if not pdf_path or not Path(pdf_path).exists():
            print("❌ Invalid PDF path!")
            return
        
        # Get output directory
        output_dir = input("📁 Output directory (default: ./output): ").strip()
        if not output_dir:
            output_dir = "./output"
        
        # Get format options
        print("\n📋 Available formats:")
        formats = [
            "podcast", "interview", "panel-discussion", "debate",
            "summary", "narration", "storytelling", "explainer",
            "lecture", "tutorial", "analysis"
        ]
        
        for i, fmt in enumerate(formats, 1):
            print(f"   {i}. {fmt}")
        
        fmt_choice = input("\n🎯 Choose format (1-11, default: podcast): ").strip()
        try:
            format_type = formats[int(fmt_choice) - 1] if fmt_choice else "podcast"
        except (ValueError, IndexError):
            format_type = "podcast"
        
        # Get other options
        length = input("📏 Length (short/medium/long/very-long, default: medium): ").strip() or "medium"
        style = input("🎨 Style (normal/casual/academic/technical, default: casual): ").strip() or "casual"
        language = input("🌍 Language (english/german/french/spanish, default: english): ").strip() or "english"
        preference = input("💭 Special preferences (optional): ").strip() or "Focus on key insights"
        
        print(f"\n🚀 Starting consciousness transformation...")
        print(f"   📄 Input: {pdf_path}")
        print(f"   📁 Output: {output_dir}")
        print(f"   🎯 Format: {format_type}")
        print(f"   📏 Length: {length}")
        print(f"   🎨 Style: {style}")
        print(f"   🌍 Language: {language}")
        
        # Run transformation directly using the function
        try:
            success, result = quantum_podcast_processor(
                pdf_path=pdf_path,
                format_type=format_type,
                length=length,
                style=style,
                output_dir=output_dir,
                language=language,
                preference=preference
            )
            
            if success:
                print("✅ Consciousness transformation completed successfully!")
                print(f"🎵 Check output directory: {output_dir}")
            else:
                print("❌ Transformation failed!")
                print(f"Error: {result}")
        except Exception as e:
            print(f"❌ Error running transformation: {e}")

def main():
    """Main launcher function"""
    parser = argparse.ArgumentParser(
        description="Quantum Consciousness Transformation Launcher"
    )
    
    parser.add_argument(
        "--config",
        default="./config/quantum_consciousness.json",
        help="Configuration file path"
    )
    
    # Direct transformation arguments (for orchestrator use)
    parser.add_argument(
        "--pdf",
        help="PDF file path for direct transformation"
    )
    
    parser.add_argument(
        "--output",
        default="./output",
        help="Output directory for transformation"
    )
    
    parser.add_argument(
        "--format",
        default="podcast",
        choices=["podcast", "interview", "panel-discussion", "debate", 
                "summary", "narration", "storytelling", "explainer",
                "lecture", "tutorial", "analysis"],
        help="Output format type"
    )
    
    parser.add_argument(
        "--length",
        default="medium",
        choices=["short", "medium", "long", "very-long"],
        help="Content length"
    )
    
    parser.add_argument(
        "--style",
        default="casual",
        choices=["normal", "casual", "academic", "technical"],
        help="Presentation style"
    )
    
    parser.add_argument(
        "--language",
        default="english",
        choices=["english", "german", "french", "spanish"],
        help="Output language"
    )
    
    parser.add_argument(
        "--preference",
        default="Focus on key insights",
        help="Special preferences for content generation"
    )
    
    # Launcher shortcuts
    parser.add_argument(
        "--quick-transform",
        help="Quick PDF transformation (provide PDF path)"
    )
    
    parser.add_argument(
        "--batch-dir",
        help="Batch process directory"
    )
    
    parser.add_argument(
        "--monitor",
        action="store_true",
        help="Start monitoring dashboard"
    )
    
    parser.add_argument(
        "--validate",
        action="store_true",
        help="Run system validation"
    )
    
    args = parser.parse_args()
    
    # Create launcher
    launcher = QuantumConsciousnessLauncher()
    launcher.config_file = args.config
    
    # Handle direct PDF transformation
    if args.pdf:
        # Direct transformation mode
        pdf_path = args.pdf
        if not Path(pdf_path).exists():
            print(f"❌ PDF not found: {pdf_path}")
            return
        
        print(f"🚀 Direct transformation: {pdf_path}")
        
        # Create output directories
        output_path = Path(args.output)
        output_path.mkdir(parents=True, exist_ok=True)
        
        # Run transformation
        try:
            success, result = quantum_podcast_processor(
                pdf_path=pdf_path,
                format_type=args.format,
                length=args.length,
                style=args.style,
                output_dir=args.output,
                language=args.language,
                preference=args.preference
            )
            
            if success:
                print("✅ Transformation completed successfully!")
                print(f"🎵 Check output directory: {args.output}")
            else:
                print("❌ Transformation failed!")
                print(f"Error: {result}")
        except Exception as e:
            print(f"❌ Error during transformation: {e}")
        return
    
    elif args.quick_transform:
        # Quick transformation mode
        pdf_path = args.quick_transform
        if not Path(pdf_path).exists():
            print(f"❌ PDF not found: {pdf_path}")
            return
        
        print(f"🚀 Quick transformation: {pdf_path}")
        
        success, result = quantum_podcast_processor(
            pdf_path=pdf_path,
            output_dir=args.output
        )
        
        if success:
            print("✅ Quick transformation completed!")
        else:
            print(f"❌ Quick transformation failed: {result}")
        return
    
    elif args.batch_dir:
        # Batch processing mode
        batch_dir = args.batch_dir
        if not Path(batch_dir).exists():
            print(f"❌ Directory not found: {batch_dir}")
            return
        
        print(f"📁 Batch processing: {batch_dir}")
        cmd = [sys.executable, "quantum_examples.py", "--example", "directory"]
        subprocess.run(cmd)
        return
    
    elif args.monitor:
        # Monitoring mode
        print("📊 Starting monitoring dashboard...")
        cmd = [sys.executable, "quantum_monitor.py", "--dashboard"]
        subprocess.run(cmd)
        return
    
    elif args.validate:
        # Validation mode
        print("✅ Running system validation...")
        cmd = [sys.executable, "quantum_validation.py"]
        subprocess.run(cmd)
        return
    
    # Default: Interactive menu
    try:
        launcher.display_main_menu()
    except KeyboardInterrupt:
        print("\n\n✨ Quantum consciousness launcher interrupted")
        print("🌌 Thank you for using the system!")
    except Exception as e:
        print(f"\n❌ Launcher error: {e}")
        sys.exit(1)

def transform_pdf_interface(self):
    """Interface for PDF to audio transformation"""
    print("\n🎵 PDF TO AUDIO TRANSFORMATION")
    print("-" * 35)
    
    # Get PDF file
    pdf_path = input("📄 Enter PDF file path: ").strip()
    if not pdf_path or not Path(pdf_path).exists():
        print("❌ Invalid PDF path!")
        return
    
    # Get output directory
    output_dir = input("📁 Output directory (default: ./output): ").strip()
    if not output_dir:
        output_dir = "./output"
    
    # Get format options
    print("\n📋 Available formats:")
    formats = [
        "podcast", "interview", "panel-discussion", "debate",
        "summary", "narration", "storytelling", "explainer",
        "lecture", "tutorial", "analysis"
    ]
    
    for i, fmt in enumerate(formats, 1):
        print(f"   {i}. {fmt}")
    
    fmt_choice = input("\n🎯 Choose format (1-11, default: podcast): ").strip()
    try:
        format_type = formats[int(fmt_choice) - 1] if fmt_choice else "podcast"
    except (ValueError, IndexError):
        format_type = "podcast"
    
    # Get other options
    length = input("📏 Length (short/medium/long/very-long, default: medium): ").strip() or "medium"
    style = input("🎨 Style (normal/casual/academic/technical, default: casual): ").strip() or "casual"
    language = input("🌍 Language (english/german/french/spanish, default: english): ").strip() or "english"
    preference = input("💭 Special preferences (optional): ").strip() or "Focus on key insights"
    
    print(f"\n🚀 Starting consciousness transformation...")
    print(f"   📄 Input: {pdf_path}")
    print(f"   📁 Output: {output_dir}")
    print(f"   🎯 Format: {format_type}")
    print(f"   📏 Length: {length}")
    print(f"   🎨 Style: {style}")
    print(f"   🌍 Language: {language}")
    
    # Run transformation using the updated command structure
    cmd = [
        sys.executable, __file__,  # Use current file instead of "quantum_consciousness.py"
        "--pdf", pdf_path,
        "--output", output_dir,
        "--format", format_type,
        "--length", length,
        "--style", style,
        "--language", language,
        "--preference", preference
    ]
    
    try:
        result = subprocess.run(cmd, capture_output=True, text=True)
        if result.returncode == 0:
            print("✅ Consciousness transformation completed successfully!")
            print(f"🎵 Check output directory: {output_dir}")
        else:
            print("❌ Transformation failed!")
            print(f"Error: {result.stderr}")
    except Exception as e:
        print(f"❌ Error running transformation: {e}")