added pptx example

end-to-end-use-cases/powerpoint-to-voiceover-transcript/config.yaml

@@ -23,7 +23,7 @@ paths:
 
 # Current Project Settings
 current_project:
-  pptx_file: "input/your_powerpoint_file_name_here" # Powerpoint file name (without the ppt/pptx extension)
+  pptx_file: "input/All About Llamas" # Powerpoint file name (without the ppt/pptx extension)
   extension: ".pptx"
   output_dir: "output/"
 

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

@@ -0,0 +1,672 @@
+{
+ "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": 29,
+   "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": 30,
+   "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": 31,
+   "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": "markdown",
+   "id": "e11ef993-f0bc-4eba-82cb-e8d4b083196e",
+   "metadata": {},
+   "source": [
+    "#### Don't forget to update the config file with your pptx file name!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "9386e035",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "File Configuration:\n",
+      "- Input File: input/All About Llamas.pptx\n",
+      "- Output Directory: output/\n",
+      "- SUCCESS: Input file found (10.8 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": 33,
+   "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": 34,
+   "id": "644ee94c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PROCESSING: Converting PPTX to images and extracting notes...\n",
+      "Processing: All About Llamas.pptx\n",
+      "Extracting speaker notes...\n",
+      "Found notes on 10 of 10 slides\n",
+      "Notes df saved to: /Users/yucedincer/Desktop/Projects/llama-cookbook/end-to-end-use-cases/powerpoint-to-voiceover-transcript/output/All About Llamas_notes.csv\n",
+      "Converting to PDF...\n",
+      "Converting to PNG images at 200 DPI...\n",
+      "\n",
+      "Successfully processed 10 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 10 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 10 slides\n",
+      "- DataFrame shape: (10, 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>Llamas: Fascinating Animals of the Andes</td>\n",
+       "      <td>True</td>\n",
+       "      <td>34</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Introduction to Llamas</td>\n",
+       "      <td>True</td>\n",
+       "      <td>28</td>\n",
+       "      <td>25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Physical Characteristics</td>\n",
+       "      <td>True</td>\n",
+       "      <td>28</td>\n",
+       "      <td>33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Diet &amp; Habitat</td>\n",
+       "      <td>True</td>\n",
+       "      <td>24</td>\n",
+       "      <td>23</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Behavior &amp; Social Structure</td>\n",
+       "      <td>True</td>\n",
+       "      <td>31</td>\n",
+       "      <td>30</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   slide_number                               slide_title  has_notes  \\\n",
+       "0             1  Llamas: Fascinating Animals of the Andes       True   \n",
+       "1             2                    Introduction to Llamas       True   \n",
+       "2             3                  Physical Characteristics       True   \n",
+       "3             4                            Diet & Habitat       True   \n",
+       "4             5               Behavior & Social Structure       True   \n",
+       "\n",
+       "   notes_word_count  slide_text_word_count  \n",
+       "0                34                     14  \n",
+       "1                28                     25  \n",
+       "2                28                     33  \n",
+       "3                24                     23  \n",
+       "4                31                     30  "
+      ]
+     },
+     "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": 35,
+   "id": "fe564b99",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PROCESSING: Starting AI transcript generation with unified processor...\n",
+      "- Processing 10 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 10 slides with narrative continuity...\n",
+      "Using context window of 5 previous slides\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing slides: 100%|████████████████████████| 10/10 [00:28<00:00,  2.88s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Context information saved to: output/narrative_context\n",
+      "\n",
+      "SUCCESS: Transcript generation completed!\n",
+      "- Generated 10 transcripts\n",
+      "- Average length: 703 characters\n",
+      "- Total words: 1,019\n",
+      "- Context information saved to: output/narrative_context/\n",
+      "- Average context slides used: 3.5\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": 37,
+   "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: 10\n",
+      "- Slides with speaker notes: 10\n",
+      "- Total transcript words: 1,019\n",
+      "- Average transcript length: 703 characters\n",
+      "- Estimated reading time: 6.8 minutes\n",
+      "- Average context slides per slide: 3.5\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",
+   "metadata": {},
+   "source": [
+    "---\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",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7122cdf6-667e-4ae4-8ce7-67cfc32577c8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "promptTesting",
+   "language": "python",
+   "name": "prompttesting"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}