Delete end-to-end-use-cases/powerpoint-to-voiceover-transcript/narrative_continuity_workflow.ipynb

end-to-end-use-cases/powerpoint-to-voiceover-transcript/narrative_continuity_workflow.ipynb

@@ -1,668 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "6c33ba3a",
-   "metadata": {},
-   "source": [
-    "# PowerPoint to Narrative-Aware Voiceover Transcript Generator\n",
-    "\n",
-    "This notebook demonstrates the complete workflow for converting PowerPoint presentations into AI-generated voiceover transcripts using the unified transcript processor with Llama 4 Maverick through the Llama API.\n",
-    "\n",
-    "## Overview\n",
-    "\n",
-    "This unified workflow performs the following operations:\n",
-    "\n",
-    "1. **Content Extraction**: Pulls speaker notes and visual elements from PowerPoint slides\n",
-    "2. **Image Conversion**: Transforms slides into high-quality images for AI analysis\n",
-    "3. **Flexible Processing**: Choose between standard or narrative-aware processing modes\n",
-    "4. **Transcript Generation**: Creates natural-sounding voiceover content with optional narrative continuity\n",
-    "5. **Speech Optimization**: Converts numbers, technical terms, and abbreviations to spoken form\n",
-    "6. **Results Export**: Saves transcripts and context information in multiple formats\n",
-    "\n",
-    "## Key Features\n",
-    "\n",
-    "- **Unified Processor**: Single class handles both standard and narrative-aware processing\n",
-    "- **Configurable Context**: Adjustable context window for narrative continuity\n",
-    "- **Mode Selection**: Toggle between standard and narrative processing with a simple flag\n",
-    "- **Backward Compatibility**: Maintains compatibility with existing workflows\n",
-    "\n",
-    "## Prerequisites\n",
-    "\n",
-    "Before running this notebook, ensure you have:\n",
-    "- Created a `.env` file with your `LLAMA_API_KEY`\n",
-    "- Updated `config.yaml` with your presentation file path\n",
-    "---"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d8965447",
-   "metadata": {},
-   "source": [
-    "## Setup and Configuration\n",
-    "\n",
-    "Import required libraries and load environment configuration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "id": "21a962b2",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SUCCESS: Environment loaded successfully!\n",
-      "SUCCESS: Llama API key found\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Import required libraries\n",
-    "import pandas as pd\n",
-    "import os\n",
-    "from pathlib import Path\n",
-    "from dotenv import load_dotenv\n",
-    "import matplotlib.pyplot as plt\n",
-    "from IPython.display import display\n",
-    "\n",
-    "# Load environment variables from .env file\n",
-    "load_dotenv()\n",
-    "\n",
-    "# Verify setup\n",
-    "if os.getenv('LLAMA_API_KEY'):\n",
-    "    print(\"SUCCESS: Environment loaded successfully!\")\n",
-    "    print(\"SUCCESS: Llama API key found\")\n",
-    "else:\n",
-    "    print(\"WARNING: LLAMA_API_KEY not found in .env file\")\n",
-    "    print(\"Please check your .env file and add your API key\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "71c1c8bd",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SUCCESS: All modules imported successfully!\n",
-      "- PPTX processor ready\n",
-      "- Unified transcript generator ready\n",
-      "- Configuration manager ready\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Import custom modules\n",
-    "try:\n",
-    "    from src.core.pptx_processor import extract_pptx_notes, pptx_to_images_and_notes\n",
-    "    from src.processors.unified_transcript_generator import (\n",
-    "        UnifiedTranscriptProcessor,\n",
-    "        process_slides,\n",
-    "        process_slides_with_narrative\n",
-    "    )\n",
-    "    from src.config.settings import load_config, get_config\n",
-    "\n",
-    "    print(\"SUCCESS: All modules imported successfully!\")\n",
-    "    print(\"- PPTX processor ready\")\n",
-    "    print(\"- Unified transcript generator ready\")\n",
-    "    print(\"- Configuration manager ready\")\n",
-    "\n",
-    "except ImportError as e:\n",
-    "    print(f\"ERROR: Import error: {e}\")\n",
-    "    print(\"Make sure you're running from the project root directory\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "53781172",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "SUCCESS: Configuration loaded successfully!\n",
-      "\n",
-      "Current Settings:\n",
-      "- Llama Model: Llama-4-Maverick-17B-128E-Instruct-FP8\n",
-      "- Image DPI: 200\n",
-      "- Image Format: png\n",
-      "- Context Window: 5 previous slides (default)\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Load and display configuration\n",
-    "config = load_config()\n",
-    "print(\"SUCCESS: Configuration loaded successfully!\")\n",
-    "print(\"\\nCurrent Settings:\")\n",
-    "print(f\"- Llama Model: {config['api']['llama_model']}\")\n",
-    "print(f\"- Image DPI: {config['processing']['default_dpi']}\")\n",
-    "print(f\"- Image Format: {config['processing']['default_format']}\")\n",
-    "print(f\"- Context Window: 5 previous slides (default)\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "9386e035",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "File Configuration:\n",
-      "- Input File: input/Part 1 Llama Certification V2 PPT Outline.pptx\n",
-      "- Output Directory: output/\n",
-      "- SUCCESS: Input file found (80.4 MB)\n",
-      "- SUCCESS: Output directory ready\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Configure file paths from config.yaml\n",
-    "pptx_file = config['current_project']['pptx_file'] + config['current_project']['extension']\n",
-    "output_dir = config['current_project']['output_dir']\n",
-    "\n",
-    "print(\"File Configuration:\")\n",
-    "print(f\"- Input File: {pptx_file}\")\n",
-    "print(f\"- Output Directory: {output_dir}\")\n",
-    "\n",
-    "# Verify input file exists\n",
-    "if Path(pptx_file).exists():\n",
-    "    file_size = Path(pptx_file).stat().st_size / 1024 / 1024\n",
-    "    print(f\"- SUCCESS: Input file found ({file_size:.1f} MB)\")\n",
-    "else:\n",
-    "    print(f\"- ERROR: Input file not found: {pptx_file}\")\n",
-    "    print(\"  Please update the 'pptx_file' path in config.yaml\")\n",
-    "\n",
-    "# Create output directory if needed\n",
-    "Path(output_dir).mkdir(parents=True, exist_ok=True)\n",
-    "print(f\"- SUCCESS: Output directory ready\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "35a9e13a-4f85-488e-880b-62c7512d1248",
-   "metadata": {},
-   "source": [
-    "## Processing Mode Configuration\n",
-    "\n",
-    "Choose your processing mode and configure the unified processor."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "id": "6fbfcb28-2f09-4497-8098-35cf3d62ebf3",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Processing Mode Configuration:\n",
-      "- Mode: NARRATIVE CONTINUITY\n",
-      "- Context Window: 5 previous slides\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Configure processing mode\n",
-    "\n",
-    "USE_NARRATIVE = True  # Set to False for standard processing, True for narrative continuity\n",
-    "CONTEXT_WINDOW_SIZE = 5  # Number of previous slides to use as context (only used when USE_NARRATIVE=True)\n",
-    "\n",
-    "print(\"Processing Mode Configuration:\")\n",
-    "if USE_NARRATIVE:\n",
-    "    print(f\"- Mode: NARRATIVE CONTINUITY\")\n",
-    "    print(f\"- Context Window: {CONTEXT_WINDOW_SIZE} previous slides\")\n",
-    "else:\n",
-    "    print(f\"- Mode: STANDARD PROCESSING\")\n",
-    "    print(f\"- Features: Independent slide processing, faster execution\")\n",
-    "\n",
-    "# Initialize the unified processor\n",
-    "processor = UnifiedTranscriptProcessor(\n",
-    "    use_narrative=USE_NARRATIVE,\n",
-    "    context_window_size=CONTEXT_WINDOW_SIZE\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea4851e6",
-   "metadata": {},
-   "source": [
-    "---\n",
-    "## Processing Pipeline\n",
-    "\n",
-    "Execute the main processing pipeline in three key steps."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0f098fdf",
-   "metadata": {},
-   "source": [
-    "### Step 1: Extract Content and Convert to Images\n",
-    "\n",
-    "Extract speaker notes and slide text, then convert the presentation to high-quality images for AI analysis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "id": "644ee94c",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PROCESSING: Converting PPTX to images and extracting notes...\n",
-      "Processing: Part 1 Llama Certification V2 PPT Outline.pptx\n",
-      "Extracting speaker notes...\n",
-      "Found notes on 41 of 102 slides\n",
-      "Notes df saved to: /Users/yucedincer/Desktop/Projects/llama-cookbook/end-to-end-use-cases/powerpoint-to-voiceover-transcript/output/Part 1 Llama Certification V2 PPT Outline_notes.csv\n",
-      "Converting to PDF...\n",
-      "Converting to PNG images at 200 DPI...\n",
-      "\n",
-      "Successfully processed 102 slides\n",
-      "Images saved to: /Users/yucedincer/Desktop/Projects/llama-cookbook/end-to-end-use-cases/powerpoint-to-voiceover-transcript/output\n",
-      "\n",
-      "SUCCESS: Processing completed successfully!\n",
-      "- Processed 102 slides\n",
-      "- Images saved to: /Users/yucedincer/Desktop/Projects/llama-cookbook/end-to-end-use-cases/powerpoint-to-voiceover-transcript/output\n",
-      "- Found notes on 41 slides\n",
-      "- DataFrame shape: (102, 8)\n",
-      "\n",
-      "Sample Data (First 5 slides):\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style scoped>\n",
-       "    .dataframe tbody tr th:only-of-type {\n",
-       "        vertical-align: middle;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>slide_number</th>\n",
-       "      <th>slide_title</th>\n",
-       "      <th>has_notes</th>\n",
-       "      <th>notes_word_count</th>\n",
-       "      <th>slide_text_word_count</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1</td>\n",
-       "      <td>APRIL 2025</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>2</td>\n",
-       "      <td>Module 1: \u000b",
-       "Introduction to Llama</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0</td>\n",
-       "      <td>41</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>3</td>\n",
-       "      <td>Video 1: Overview of Llama</td>\n",
-       "      <td>False</td>\n",
-       "      <td>0</td>\n",
-       "      <td>15</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>4</td>\n",
-       "      <td>Artificial intelligence (AI)</td>\n",
-       "      <td>True</td>\n",
-       "      <td>243</td>\n",
-       "      <td>34</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>5</td>\n",
-       "      <td>Leverage Llama for unparalleled \u000b",
-       "control, cust...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>244</td>\n",
-       "      <td>147</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "   slide_number                                        slide_title  has_notes  \\\n",
-       "0             1                                         APRIL 2025      False   \n",
-       "1             2                   Module 1: \n",
-       "Introduction to Llama      False   \n",
-       "2             3                         Video 1: Overview of Llama      False   \n",
-       "3             4                       Artificial intelligence (AI)       True   \n",
-       "4             5  Leverage Llama for unparalleled \n",
-       "control, cust...       True   \n",
-       "\n",
-       "   notes_word_count  slide_text_word_count  \n",
-       "0                 0                      6  \n",
-       "1                 0                     41  \n",
-       "2                 0                     15  \n",
-       "3               243                     34  \n",
-       "4               244                    147  "
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "print(\"PROCESSING: Converting PPTX to images and extracting notes...\")\n",
-    "\n",
-    "result = pptx_to_images_and_notes(\n",
-    "    pptx_path=pptx_file,\n",
-    "    output_dir=output_dir,\n",
-    "    extract_notes=True\n",
-    ")\n",
-    "\n",
-    "notes_df = result['notes_df']\n",
-    "image_files = result['image_files']\n",
-    "\n",
-    "print(f\"\\nSUCCESS: Processing completed successfully!\")\n",
-    "print(f\"- Processed {len(image_files)} slides\")\n",
-    "print(f\"- Images saved to: {result['output_dir']}\")\n",
-    "print(f\"- Found notes on {notes_df['has_notes'].sum()} slides\")\n",
-    "print(f\"- DataFrame shape: {notes_df.shape}\")\n",
-    "\n",
-    "# Show sample data\n",
-    "print(\"\\nSample Data (First 5 slides):\")\n",
-    "display(notes_df[['slide_number', 'slide_title', 'has_notes', 'notes_word_count', 'slide_text_word_count']].head())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1f95749d",
-   "metadata": {},
-   "source": [
-    "### Step 2: Generate Narrative-Aware AI Transcripts\n",
-    "\n",
-    "Use the Llama vision model to analyze each slide image and generate natural-sounding voiceover transcripts with narrative continuity.\n",
-    "\n",
-    "This process:\n",
-    "- Analyzes slide visual content using AI vision\n",
-    "- Uses transcripts from previous slides as context\n",
-    "- Combines slide content with speaker notes\n",
-    "- Generates speech-optimized transcripts with smooth transitions\n",
-    "- Maintains consistent terminology throughout the presentation\n",
-    "- Converts numbers and technical terms to spoken form"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "id": "fe564b99",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PROCESSING: Starting AI transcript generation with unified processor...\n",
-      "- Processing 102 slides\n",
-      "- Using model: Llama-4-Maverick-17B-128E-Instruct-FP8\n",
-      "- Mode: Narrative Continuity\n",
-      "- Context window: 5 previous slides\n",
-      "- Using previous transcripts as context for narrative continuity\n",
-      "- This may take several minutes...\n",
-      "Processing 102 slides with narrative continuity...\n",
-      "Using context window of 5 previous slides\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Processing slides: 100%|██████████████████████| 102/102 [06:03<00:00,  3.56s/it]"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Context information saved to: output/narrative_context\n",
-      "\n",
-      "SUCCESS: Transcript generation completed!\n",
-      "- Generated 102 transcripts\n",
-      "- Average length: 1137 characters\n",
-      "- Total words: 16,950\n",
-      "- Context information saved to: output/narrative_context/\n",
-      "- Average context slides used: 4.9\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"PROCESSING: Starting AI transcript generation with unified processor...\")\n",
-    "print(f\"- Processing {len(notes_df)} slides\")\n",
-    "print(f\"- Using model: {config['api']['llama_model']}\")\n",
-    "print(f\"- Mode: {'Narrative Continuity' if USE_NARRATIVE else 'Standard Processing'}\")\n",
-    "if USE_NARRATIVE:\n",
-    "    print(f\"- Context window: {CONTEXT_WINDOW_SIZE} previous slides\")\n",
-    "    print(f\"- Using previous transcripts as context for narrative continuity\")\n",
-    "print(\"- This may take several minutes...\")\n",
-    "\n",
-    "# Generate transcripts using the unified processor\n",
-    "processed_df = processor.process_slides_dataframe(\n",
-    "    df=notes_df,\n",
-    "    output_dir=output_dir,\n",
-    "    save_context=True  # Only saves context if USE_NARRATIVE=True\n",
-    ")\n",
-    "\n",
-    "print(f\"\\nSUCCESS: Transcript generation completed!\")\n",
-    "print(f\"- Generated {len(processed_df)} transcripts\")\n",
-    "print(f\"- Average length: {processed_df['ai_transcript'].str.len().mean():.0f} characters\")\n",
-    "print(f\"- Total words: {processed_df['ai_transcript'].str.split().str.len().sum():,}\")\n",
-    "\n",
-    "if USE_NARRATIVE:\n",
-    "    print(f\"- Context information saved to: {output_dir}narrative_context/\")\n",
-    "    print(f\"- Average context slides used: {processed_df['context_slides_used'].mean():.1f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5cff4b70",
-   "metadata": {},
-   "source": [
-    "### Step 3: Save Results\n",
-    "\n",
-    "Save results in multiple formats for different use cases."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "id": "8463ac3a",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "PROCESSING: Saving results in multiple formats...\n",
-      "- SUCCESS: Complete results saved to output/narrative_transcripts.csv\n",
-      "- SUCCESS: Clean transcripts saved to output/narrative_transcripts_clean.csv\n",
-      "- SUCCESS: JSON format saved to output/narrative_transcripts.json\n",
-      "\n",
-      "Export Summary:\n",
-      "- Processing mode: Narrative Continuity\n",
-      "- Total slides processed: 102\n",
-      "- Slides with speaker notes: 41\n",
-      "- Total transcript words: 16,950\n",
-      "- Average transcript length: 1137 characters\n",
-      "- Estimated reading time: 113.0 minutes\n",
-      "- Average context slides per slide: 4.9\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"PROCESSING: Saving results in multiple formats...\")\n",
-    "\n",
-    "# Create output directory\n",
-    "os.makedirs(output_dir, exist_ok=True)\n",
-    "\n",
-    "# Determine file prefix based on processing mode\n",
-    "file_prefix = \"narrative\" if USE_NARRATIVE else \"standard\"\n",
-    "\n",
-    "# Save complete results with all metadata\n",
-    "output_file = f\"{output_dir}{file_prefix}_transcripts.csv\"\n",
-    "processed_df.to_csv(output_file, index=False)\n",
-    "print(f\"- SUCCESS: Complete results saved to {output_file}\")\n",
-    "\n",
-    "# Save transcript-only version for voiceover work\n",
-    "if USE_NARRATIVE:\n",
-    "    transcript_only = processed_df[['slide_number', 'slide_title', 'ai_transcript', 'context_slides_used']]\n",
-    "else:\n",
-    "    transcript_only = processed_df[['slide_number', 'slide_title', 'ai_transcript']]\n",
-    "\n",
-    "transcript_file = f\"{output_dir}{file_prefix}_transcripts_clean.csv\"\n",
-    "transcript_only.to_csv(transcript_file, index=False)\n",
-    "print(f\"- SUCCESS: Clean transcripts saved to {transcript_file}\")\n",
-    "\n",
-    "# Save as JSON for API integration\n",
-    "json_file = f\"{output_dir}{file_prefix}_transcripts.json\"\n",
-    "processed_df.to_json(json_file, orient='records', indent=2)\n",
-    "print(f\"- SUCCESS: JSON format saved to {json_file}\")\n",
-    "\n",
-    "# Summary statistics\n",
-    "total_words = processed_df['ai_transcript'].str.split().str.len().sum()\n",
-    "reading_time = total_words / 150  # Assuming 150 words per minute\n",
-    "\n",
-    "print(f\"\\nExport Summary:\")\n",
-    "print(f\"- Processing mode: {'Narrative Continuity' if USE_NARRATIVE else 'Standard Processing'}\")\n",
-    "print(f\"- Total slides processed: {len(processed_df)}\")\n",
-    "print(f\"- Slides with speaker notes: {processed_df['has_notes'].sum()}\")\n",
-    "print(f\"- Total transcript words: {total_words:,}\")\n",
-    "print(f\"- Average transcript length: {processed_df['ai_transcript'].str.len().mean():.0f} characters\")\n",
-    "print(f\"- Estimated reading time: {reading_time:.1f} minutes\")\n",
-    "\n",
-    "if USE_NARRATIVE and 'context_slides_used' in processed_df.columns:\n",
-    "    print(f\"- Average context slides per slide: {processed_df['context_slides_used'].mean():.1f}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8728d2ac",
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-    "---\n",
-    "# Completion Summary\n",
-    "\n",
-    "## Successfully Generated:\n",
-    "- **Unified Processing**: Single processor handles both standard and narrative modes\n",
-    "- **Flexible Configuration**: Easy switching between processing modes\n",
-    "- **Speech-Optimized Transcripts**: Natural-sounding voiceover content\n",
-    "- **Multiple Formats**: CSV, JSON exports for different use cases\n",
-    "- **Context Analysis**: Detailed information about narrative flow (when enabled)\n",
-    "\n",
-    "## Output Files:\n",
-    "- `[mode]_transcripts.csv` - Complete dataset with metadata\n",
-    "- `[mode]_transcripts_clean.csv` - Clean transcripts for voiceover work\n",
-    "- `[mode]_transcripts.json` - JSON format for API integration\n",
-    "- `narrative_context/` - Context analysis files (narrative mode only)\n",
-    "- Individual slide images in PNG/JPEG format\n",
-    "\n",
-    "## Processing Modes:\n",
-    "\n",
-    "### Standard Mode (`USE_NARRATIVE = False`)\n",
-    "- **Best for**: Simple presentations, quick processing, independent slides\n",
-    "- **Features**: Fast execution, no context dependencies\n",
-    "- **Use cases**: Training materials, product demos, standalone slides\n",
-    "\n",
-    "### Narrative Mode (`USE_NARRATIVE = True`)\n",
-    "- **Best for**: Story-driven presentations, complex topics, educational content\n",
-    "- **Features**: Context awareness, smooth transitions, terminology consistency\n",
-    "- **Use cases**: Conference talks, educational courses, marketing presentations\n",
-    "\n",
-    "## Next Steps:\n",
-    "1. **Review** generated transcripts for accuracy and flow\n",
-    "2. **Edit** any content that needs refinement\n",
-    "3. **Create** voiceover recordings or use TTS systems\n",
-    "4. **Integrate** JSON data into your video production workflow\n",
-    "5. **Experiment** with different processing modes for optimal results\n",
-    "\n",
-    "## Tips for Better Results:\n",
-    "- **Rich Speaker Notes**: Detailed notes improve transcript quality in both modes\n",
-    "- **Clear Visuals**: High-contrast slides with readable text work best\n",
-    "- **Mode Selection**: Use narrative mode for complex presentations, standard for simple ones\n",
-    "- **Context Window**: Adjust context window size (3-7 slides) based on presentation complexity\n",
-    "- **Consistent Style**: Maintain consistent formatting across your presentation\n",
-    "\n",
-    "---"
-   ]
-  },
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