"""Main summarizer module that orchestrates the entire process.""" import os from typing import Optional, Dict, Any, Tuple from datetime import datetime from pathlib import Path from docx import Document from docx.enum.text import WD_ALIGN_PARAGRAPH, WD_BREAK from reportlab.lib.pagesizes import letter from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle from reportlab.lib.units import inch from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer from reportlab.lib.enums import TA_CENTER from config import config from youtube_transcript import YouTubeExtractor, extract_transcript from groq_client import GroqClient, create_client from utils.statistics import calculate_transcript_stats class YouTubeBookSummarizer: """Main class for converting YouTube videos to book-style summaries.""" def __init__( self, api_key: Optional[str] = None, model: Optional[str] = None, output_dir: Optional[Path] = None, ): """ Initialize the summarizer. Args: api_key: Groq API key model: LLM model to use output_dir: Directory for saving summaries """ self.transcript_extractor = YouTubeExtractor() self.groq_client = create_client(api_key=api_key, model=model) self.output_dir = output_dir or config.OUTPUT_DIR self.output_dir.mkdir(parents=True, exist_ok=True) # Load the summary template self.template = self._load_template() def _load_template(self) -> str: """Load the summary template from youtube_summary.md.""" template_path = Path(__file__).parent / "youtube_summary.md" if template_path.exists(): return template_path.read_text() return self._get_default_template() def _get_default_template(self) -> str: """Get default template if file not found.""" return """# IDENTITY and PURPOSE You are an expert author and content analyst. You transform YouTube video transcripts into comprehensive, well-structured book-style summaries that capture the full depth and nuance of the content. # STEPS 1. READ AND COMPREHEND THE ENTIRE TRANSCRIPT: - First read through the entire transcript to understand the overall structure - Identify the main topic, key themes, and primary arguments - Note the speaker's expertise and perspective - Identify any supporting evidence, examples, or anecdotes 2. ANALYZE THE CONTENT STRUCTURE: - Identify major sections or chapters in the video - Extract key turning points or major topic shifts - Note recurring themes or concepts - Identify the core message the presenter is conveying 3. EXTRACT AND ORGANIZE KEY INFORMATION: - Pull out main ideas and central arguments - Extract important facts, statistics, and data points - Note expert opinions or expert quotes mentioned - Identify practical applications or takeaways - Capture stories, examples, and case studies 4. SYNTHESIZE INTO BOOK FORMAT: - Organize content into proper book structure - Write in narrative prose, avoiding excessive lists - Maintain the presenter's voice and perspective - Connect ideas logically across sections 5. WRITE COMPREHENSIVE SUMMARY WITH: # OUTPUT SECTIONS - EXECUTIVE OVERVIEW: - What this video/book covers in 2-3 paragraphs - Why this content matters - Who would benefit most from this - INTRODUCTION: - Background and context of the topic - Presenter's credentials and perspective - What problem or question the content addresses - CHAPTER-BY-CHAPTER SUMMARY: **Chapter 1: [Chapter Title]** - Detailed summary of this section - Key concepts introduced - Main arguments or points made - Examples or evidence provided - How this chapter builds on previous content **Chapter 2: [Chapter Title]** - [Same structure] - KEY CONCEPTS AND DEFINITIONS: - Important terms explained in context - Technical vocabulary with plain-language explanations - Conceptual frameworks introduced - KEY TAKEAWAYS (numbered): 1. [Most important insight] 2. [Second most important insight] 3. [Third most important insight] - MEMORABLE QUOTATIONS: - "Quote from the video that captures essential wisdom" - "Another powerful statement" - PRACTICAL APPLICATIONS: - How to apply the concepts learned - Actionable steps or exercises - Real-world scenarios where this knowledge applies - CRITICAL ANALYSIS: - Strengths of the arguments presented - Potential limitations or counterpoints - Areas where more depth would be valuable - FURTHER READING/SOURCES: - Books or resources mentioned in the video - Related topics for exploration - CONCLUSION: - Synthesis of the overall message - Final wisdom or call to action - How this content fits into larger context - QUICK BULLET SUMMARY (TL;DR): - 5-10 bullet points capturing the absolute most important takeaways - Each bullet should be a complete, standalone insight - Written for quick scanning and easy understanding - Focus on actionable insights and key learnings - Example format: • [Main point 1 - the core message in one sentence] • [Main point 2 - key insight or finding] • [Main point 3 - practical takeaway] • [Main point 4 - important statistic or fact] • [Main point 5 - recommended action or next step] # OUTPUT INSTRUCTIONS - WRITE IN PROSE: Use flowing paragraphs, not excessive bullet points - MAINTAIN DEPTH: Each chapter summary should be 3-5 substantial paragraphs - USE CONSISTENT FORMATTING: Follow the structure above exactly - INCLUDE SPECIFICS: Include actual examples, numbers, and quotes from the content - BOOK-LIKE QUALITY: Write as if creating a valuable reference book - MAXIMIZE VALUE: Extract every useful insight from the transcript - DO NOT REPEAT: Each piece of information should appear only once - VARIED OPENINGS: Don't start multiple sections with the same words - COMPLETE THOUGHTS: Each paragraph should fully develop an idea - NO PLACEHOLDERS: Fill in actual titles, quotes, and content from the transcript """ def summarize( self, url: str, language: str = "en", save_output: bool = True, verbose: bool = True, ) -> Dict[str, Any]: """ Generate a book-style summary from a YouTube video. Args: url: YouTube video URL language: Transcript language save_output: Whether to save summary to file verbose: Print progress messages Returns: Dictionary with summary and metadata """ result = { 'success': False, 'video_url': url, 'summary': None, 'metadata': {}, 'error': None } try: # Step 1: Extract transcript if verbose: print("[Step 1] Extracting transcript from YouTube...") transcript_result = extract_transcript(url, language) if not transcript_result['success']: raise ValueError(transcript_result['error']) result['metadata']['transcript'] = transcript_result['metadata'] transcript = transcript_result.get('transcript') or transcript_result.get('text', '') if verbose: print(f" [OK] Transcript extracted: {transcript_result['metadata']['transcript_length']} characters") print(f" [OK] Language: {transcript_result['metadata']['language']}") # Step 2: Process transcript with Groq if verbose: print("[Step 2] Generating book-style summary with AI...") # Check if we need to chunk the transcript if len(transcript) > config.MAX_TRANSCRIPT_LENGTH: if verbose: print(f" [INFO] Transcript is long ({len(transcript)} chars), processing in chunks...") chunk_summaries = self._process_in_chunks(transcript, verbose) llm_result = self.groq_client.summarize_transcript( transcript=transcript, prompt_template=self.template, chunk_summaries=chunk_summaries, ) else: llm_result = self.groq_client.summarize_transcript( transcript=transcript, prompt_template=self.template, ) if not llm_result['success']: raise ValueError(llm_result['error']) result['summary'] = llm_result['content'] result['metadata']['usage'] = llm_result['usage'] result['metadata']['model'] = llm_result['model'] result['success'] = True if verbose: print(f" [OK] Summary generated!") print(f" [OK] Tokens used: {llm_result['usage']['total_tokens']}") print(f" [OK] Estimated cost: ${llm_result['usage']['estimated_cost_usd']:.4f}") # Step 3: Save output if save_output: if verbose: print("[Step 3] Saving files...") # Save both book summary and transcript output_paths = self._save_book_summary( result['summary'], transcript_result.get('transcript') or transcript_result.get('text', ''), transcript_result['metadata'], ) # output_paths is a tuple: (book_path, pdf_path, transcript_path) result['metadata']['output_file'] = output_paths if verbose: book_path, pdf_path, transcript_path = output_paths print(f" [OK] Files saved!") print(f" - Book (Word): {book_path}") print(f" - Book (PDF): {pdf_path}") print(f" - Transcript: {transcript_path}") if verbose: print("\n[SUCCESS] Summary complete!") return result except Exception as e: result['error'] = str(e) if verbose: print(f"\n[ERROR] Error: {str(e)}") return result def _process_in_chunks( self, transcript: str, verbose: bool = True, ) -> list: """ Process long transcripts in chunks. Args: transcript: Full transcript text verbose: Print progress Returns: List of chunk summaries """ extractor = YouTubeExtractor() chunks = extractor.chunk_transcript( text=transcript, max_length=config.MAX_TRANSCRIPT_LENGTH, overlap=config.CHUNK_OVERLAP ) if verbose: print(f" [INFO] Split into {len(chunks)} chunks") chunk_summaries = [] for i, chunk in enumerate(chunks): if verbose: print(f" Processing chunk {i + 1}/{len(chunks)}...") # Create a focused summary for each chunk chunk_prompt = f"""Summarize the following transcript segment. Identify: 1. Main topics discussed 2. Key points and arguments 3. Important examples or evidence 4. Any memorable quotes Return this as a concise but comprehensive summary. TRANSCRIPT SEGMENT: {chunk} """ result = self.groq_client.chat_completion( messages=[{"role": "user", "content": chunk_prompt}], max_tokens=4000, temperature=0.5, ) if result['success']: chunk_summaries.append(result['content']) else: # Fall back to raw chunk if API fails chunk_summaries.append(f"[Segment {i + 1}] {chunk[:500]}...") return chunk_summaries def _save_book_summary( self, summary: str, transcript: str, metadata: Dict[str, Any], ) -> Tuple[Path, Path, Path]: """Save both book summary and transcript to Word files. Returns: Tuple of (book_path, pdf_path, transcript_path) """ video_id = metadata.get('video_id', 'unknown') title = metadata.get('title', f'YouTube Video {video_id}') # Create filename base timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") safe_title = "".join( c for c in title if c.isalnum() or c in (' ', '-', '_') ).strip()[:50] # Save Book Summary as Word file book_filename = f"{safe_title}_{video_id}_book_{timestamp}.docx" book_path = self.output_dir / book_filename doc = Document() # Add title title_para = doc.add_heading(title, 0) title_para.alignment = WD_ALIGN_PARAGRAPH.CENTER # Add metadata doc.add_paragraph(f"Video ID: {video_id}") doc.add_paragraph(f"Generated: {datetime.now().isoformat()}") doc.add_paragraph(f"Transcript Length: {metadata.get('transcript_length', 'N/A')} characters") doc.add_paragraph() # Add summary sections sections = summary.split('\n# ') for section in sections: if section.strip(): lines = section.strip().split('\n') if lines: # First line is the section title section_title = lines[0].replace('#', '').strip() if section_title: doc.add_heading(section_title, level=1) # Rest is content content = '\n'.join(lines[1:]).strip() if content: doc.add_paragraph(content) doc.save(book_path) # Also save as PDF pdf_filename = f"{safe_title}_{video_id}_book_{timestamp}.pdf" pdf_path = self.output_dir / pdf_filename # Create PDF doc_pdf = SimpleDocTemplate(str(pdf_path), pagesize=letter, leftMargin=0.5*inch, rightMargin=0.5*inch, topMargin=0.5*inch, bottomMargin=0.5*inch) styles = getSampleStyleSheet() styles.add(ParagraphStyle(name='CenterTitle', parent=styles['Title'], alignment=TA_CENTER)) styles.add(ParagraphStyle(name='SectionHeader', parent=styles['Heading1'], fontSize=14, spaceAfter=12)) # Use existing BodyText style or create custom one if 'CustomBody' not in styles.byName: styles.add(ParagraphStyle(name='CustomBody', parent=styles['Normal'], fontSize=11, leading=14)) story = [] # Title story.append(Paragraph(title, styles['CenterTitle'])) story.append(Spacer(1, 12)) # Metadata story.append(Paragraph(f"Video ID: {video_id}", styles['CustomBody'])) story.append(Paragraph(f"Generated: {datetime.now().isoformat()}", styles['CustomBody'])) story.append(Spacer(1, 24)) # Summary sections sections = summary.split('\n# ') for section in sections: if section.strip(): lines = section.strip().split('\n') if lines: section_title = lines[0].replace('#', '').strip() if section_title: story.append(Paragraph(section_title, styles['SectionHeader'])) content = '\n'.join(lines[1:]).strip() if content: # Wrap long text for para in content.split('\n\n'): if para.strip(): story.append(Paragraph(para, styles['CustomBody'])) story.append(Spacer(1, 12)) doc_pdf.build(story) # Save Transcript as Word file transcript_filename = f"{safe_title}_{video_id}_transcript_{timestamp}.docx" transcript_path = self.output_dir / transcript_filename doc_transcript = Document() # Add title for transcript title_para2 = doc_transcript.add_heading(f"Transcript: {title}", 0) title_para2.alignment = WD_ALIGN_PARAGRAPH.CENTER # Calculate transcript statistics video_duration = metadata.get('video_duration_seconds') stats = calculate_transcript_stats(transcript, video_duration) # Add metadata section doc_transcript.add_paragraph() meta_heading = doc_transcript.add_heading("VIDEO METADATA", level=1) # Video metadata table doc_transcript.add_paragraph(f"Video ID: {video_id}") if stats.get('video_duration_formatted'): doc_transcript.add_paragraph(f"Video Duration: {stats['video_duration_formatted']}") doc_transcript.add_paragraph(f"Language: {metadata.get('language', 'en')}") doc_transcript.add_paragraph(f"Generated: {datetime.now().isoformat()}") # Add statistics section doc_transcript.add_paragraph() stats_heading = doc_transcript.add_heading("TRANSCRIPT STATISTICS", level=1) doc_transcript.add_paragraph(f"Word Count: {stats['word_count']:,} words") doc_transcript.add_paragraph(f"Character Count: {stats['character_count']:,} characters") doc_transcript.add_paragraph(f"Reading Time: {stats['reading_time_formatted']} (at 200 WPM)") if stats.get('speaking_rate_wpm'): doc_transcript.add_paragraph(f"Speaking Rate: {stats['speaking_rate_wpm']} words/min") doc_transcript.add_paragraph(f"Paragraphs: {stats['paragraph_count']}") doc_transcript.add_paragraph(f"Sentences: {stats['sentence_count']}") doc_transcript.add_paragraph() doc_transcript.add_paragraph("─" * 50) doc_transcript.add_paragraph() # Add transcript content (split into pages) max_chars = 15000 # Approximate chars per page chunks = [transcript[i:i+max_chars] for i in range(0, len(transcript), max_chars)] for i, chunk in enumerate(chunks): if i > 0: # Add page break using paragraph with break para = doc_transcript.add_paragraph() para.add_run().add_break(WD_BREAK.PAGE) # Page header page_header = doc_transcript.add_heading(f"Page {i+1} of {len(chunks)}", level=2) doc_transcript.add_paragraph(chunk) # Add final summary statistics page para = doc_transcript.add_paragraph() para.add_run().add_break(WD_BREAK.PAGE) final_heading = doc_transcript.add_heading("TRANSCRIPT SUMMARY", level=1) doc_transcript.add_paragraph() doc_transcript.add_paragraph(f"Total Words: {stats['word_count']:,}") doc_transcript.add_paragraph(f"Total Characters: {stats['character_count']:,}") doc_transcript.add_paragraph(f"Estimated Read Time: {stats['reading_time_formatted']} (at 200 WPM)") if stats.get('video_duration_formatted'): doc_transcript.add_paragraph(f"Video Duration: {stats['video_duration_formatted']}") if stats.get('speaking_rate_wpm'): doc_transcript.add_paragraph(f"Speaking Pace: {stats['speaking_rate_wpm']} words/min") doc_transcript.add_paragraph(f"Pages Generated: {len(chunks)}") doc_transcript.save(transcript_path) # Return all file paths return book_path, pdf_path, transcript_path def _save_summary(self, summary: str, metadata: Dict[str, Any]) -> Path: """Legacy function - kept for compatibility.""" video_id = metadata.get('video_id', 'unknown') title = metadata.get('title', f'YouTube Video {video_id}') timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") safe_title = "".join( c for c in title if c.isalnum() or c in (' ', '-', '_') ).strip()[:50] filename = f"{safe_title}_{video_id}_{timestamp}.md" output_path = self.output_dir / filename header = f"""--- title: "{title}" video_id: "{video_id}" generated: "{datetime.now().isoformat()}" transcript_length: "{metadata.get('transcript_length', 'N/A')}" language: "{metadata.get('language', 'en')}" --- """ full_content = header + summary output_path.write_text(full_content) return output_path def create_summarizer( api_key: Optional[str] = None, model: Optional[str] = None, ) -> YouTubeBookSummarizer: """Factory function to create summarizer instance.""" return YouTubeBookSummarizer(api_key=api_key, model=model) if __name__ == "__main__": import sys # Example usage summarizer = create_summarizer() if len(sys.argv) > 1: url = sys.argv[1] result = summarizer.summarize(url, verbose=True) if result['success']: print(f"\n{'='*60}") print("SUMMARY PREVIEW:") print(f"{'='*60}") print(result['summary'][:1000] + "..." if len(result['summary']) > 1000 else result['summary']) else: print("Usage: python summarizer.py ")