initial commit of pptx to vo

end-to-end-use-cases/powerpoint-to-voiceover-transcript/.env.example

@@ -0,0 +1,20 @@
+# Copy this file to .env and fill in your actual values
+# Usage: cp .env.example .env
+# =============================================================================
+
+# =============================================================================
+# REQUIRED API CONFIGURATION
+# =============================================================================
+
+# Llama API Key (REQUIRED)
+# Get your API key from: https://www.llama-api.com/
+# This is essential for AI transcript generation
+LLAMA_API_KEY=your_llama_api_key_here
+
+# =============================================================================
+# SETUP INSTRUCTIONS
+# =============================================================================
+
+# 1. COPY THIS FILE:
+#    cp .env.example .env
+# 2. Fill in your API key

end-to-end-use-cases/powerpoint-to-voiceover-transcript/.gitignore

@@ -0,0 +1,187 @@
+# Environment variables and secrets
+.env
+.env.local
+.env.development
+.env.test
+.env.production
+*.env
+
+# PowerPoint files
+*.pptx
+*.ppt
+*.potx
+*.pot
+
+# Output directories and generated files
+output/
+slide_images/
+cache/
+logs/
+temp/
+*.log
+
+# Progress and state files
+progress.json
+*.progress
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+Pipfile.lock
+
+# poetry
+poetry.lock
+
+# pdm
+.pdm.toml
+
+# PEP 582
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# IDE and Editor files
+.vscode/
+.idea/
+*.swp
+*.swo
+*~
+
+# OS generated files
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Image files (generated from slides)
+*.png
+*.jpg
+*.jpeg
+*.gif
+*.bmp
+*.tiff
+*.svg
+
+# PDF files (intermediate conversion files)
+*.pdf
+
+# CSV and JSON output files
+*.csv
+*.json
+
+# Backup files
+*.bak
+*.backup
+*.old
+
+# Temporary files
+*.tmp
+*.temp
+
+# LibreOffice lock files
+.~lock.*
+
+# API keys and configuration with sensitive data
+api_keys.txt
+secrets.yaml
+credentials.json
+
+# Large model files or cached data
+*.model
+*.bin
+*.safetensors

end-to-end-use-cases/powerpoint-to-voiceover-transcript/README.md

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+# PowerPoint to Voiceover Transcript
+
+A production-ready tool that converts PowerPoint presentations into AI-generated voiceover transcripts using Meta's Llama vision models. Designed for creating professional narration content from slide decks.
+
+## Overview
+
+This system extracts speaker notes and visual content from PowerPoint files, then uses advanced AI vision models to generate natural-sounding transcripts optimized for human voiceover or text-to-speech systems. The generated transcripts include proper pronunciation of technical terms, numbers, and model names.
+
+### Key Features
+
+- **AI-Powered Analysis**: Uses Llama vision models to understand slide content and context
+- **Speech Optimization**: Converts numbers, decimals, and technical terms to spoken form
+- **Flexible Processing**: Supports both individual slides and batch processing
+- **Cross-Platform**: Works on Windows, macOS, and Linux
+- **Production Ready**: Comprehensive error handling, progress tracking, and retry logic
+
+## Quick Start
+
+### Prerequisites
+
+- Python 3.12+
+- LibreOffice (for PPTX conversion)
+- Llama API key
+
+### Installation
+
+#### Option 1: Using uv (Recommended - Faster)
+
+1. **Install uv (if not already installed):**
+   ```bash
+   # macOS/Linux
+   curl -LsSf https://astral.sh/uv/install.sh | sh
+
+   # Windows
+   powershell -c "irm https://astral.sh/uv/install.ps1 | iex"
+
+   # Or via pip
+   pip install uv
+   ```
+
+2. **Clone and install dependencies:**
+   ```bash
+   git clone <repository-url>
+   cd powerpoint-to-voiceover-transcript
+   uv sync
+   ```
+
+3. **Activate the virtual environment:**
+   ```bash
+   source .venv/bin/activate  # macOS/Linux
+   # or
+   .venv\Scripts\activate     # Windows
+   ```
+
+#### Option 2: Using pip (Traditional)
+
+1. **Clone and install dependencies:**
+   ```bash
+   git clone https://github.com/meta-llama/llama-cookbook.git
+   cd powerpoint-to-voiceover-transcript
+   pip install -e .
+   ```
+
+2. **Install LibreOffice:**
+   - **macOS**: `brew install --cask libreoffice`
+   - **Ubuntu**: `sudo apt-get install libreoffice`
+   - **Windows**: Download from [libreoffice.org](https://www.libreoffice.org/download/)
+
+3. **Set up environment:**
+   ```bash
+   cp .env.example .env
+   # Edit .env and add your LLAMA_API_KEY
+   ```
+
+4. **Configure your presentation:**
+   ```bash
+   # Edit config.yaml - update the pptx_file path
+   current_project:
+     pptx_file: "input/your_presentation_name"
+     extension: ".pptx"
+   ```
+
+### Basic Usage
+
+Run the main workflow notebook:
+```bash
+jupyter notebook pptx_to_vo_transcript.ipynb
+```
+
+Or use the Python API:
+```python
+from src.core.pptx_processor import pptx_to_images_and_notes
+from src.processors.transcript_generator import TranscriptProcessor
+
+# Convert PPTX and extract notes
+result = pptx_to_images_and_notes("presentation.pptx", "output/")
+
+# Generate transcripts
+processor = TranscriptProcessor()
+transcripts = processor.process_slides_dataframe(result['notes_df'], "output/")
+
+# Save results
+transcripts.to_csv("transcripts.csv", index=False)
+```
+
+## Project Structure
+
+```
+powerpoint-to-voiceover-transcript/
+├── README.md                     # This file
+├── config.yaml                   # Main configuration
+├── pyproject.toml                # Dependencies and project metadata
+├── uv.lock                       # uv dependency lock file
+├── pptx_to_vo_transcript.ipynb   # Main workflow notebook
+├── .env.example                  # Environment template
+├── input/                        # Place your PPTX files here
+└── src/
+    ├── config/
+    │   └── settings.py           # Configuration management
+    ├── core/
+    │   ├── file_utils.py         # File system utilities
+    │   ├── image_processing.py   # Image encoding for API
+    │   ├── llama_client.py       # Llama API integration
+    │   └── pptx_processor.py     # PPTX extraction and conversion
+    └── processors/
+        └── transcript_generator.py # AI transcript generation
+```
+
+## Configuration
+
+The system uses `config.yaml` for settings:
+
+```yaml
+# API Configuration
+api:
+  llama_model: "Llama-4-Maverick-17B-128E-Instruct-FP8"
+  max_retries: 3
+
+# Processing Settings
+processing:
+  default_dpi: 200
+  default_format: "png"
+  batch_size: 5
+
+# Your Project
+current_project:
+  pptx_file: "input/your_presentation"
+  extension: ".pptx"
+  output_dir: "output/"
+```
+
+## API Reference
+
+### Core Functions
+
+#### `pptx_to_images_and_notes(pptx_path, output_dir)`
+Converts PowerPoint to images and extracts speaker notes.
+
+**Returns:** Dictionary with `image_files`, `notes_df`, and `output_dir`
+
+#### `TranscriptProcessor()`
+Main class for generating AI transcripts.
+
+**Methods:**
+- `process_slides_dataframe(df, output_dir)` - Process all slides
+- `process_single_slide(image_path, speaker_notes)` - Process one slide
+
+### Speech Optimization
+
+The AI automatically converts technical content for natural speech:
+
+- **Decimals**: `3.2` → "three dot two"
+- **Model names**: `LLaMA-3.2` → "LLaMA three dot two"
+- **Abbreviations**: `LLM` → "L L M"
+- **Large numbers**: `70B` → "seventy billion"
+
+## Requirements
+
+### System Dependencies
+- **LibreOffice**: Required for PPTX to PDF conversion
+- **Python 3.12+**: Core runtime
+
+### Python Dependencies
+- `pandas>=2.3.1` - Data processing
+- `python-pptx>=1.0.2` - PowerPoint file handling
+- `pymupdf>=1.24.0` - PDF to image conversion
+- `llama-api-client>=0.1.0` - AI model access
+- `pillow>=11.3.0` - Image processing
+- `pyyaml>=6.0.0` - Configuration management
+
+See `pyproject.toml` for complete dependency list.
+
+## Output
+
+The system generates:
+
+1. **Slide Images**: High-resolution PNG/JPEG files
+2. **Notes DataFrame**: Structured data with slide metadata
+3. **AI Transcripts**: Speech-optimized voiceover content
+4. **CSV Export**: Complete results for further processing
+
+## Troubleshooting
+
+### Common Issues
+
+**"LibreOffice not found"**
+- Install LibreOffice or update paths in `config.yaml`
+
+**"API key not found"**
+- Set `LLAMA_API_KEY` in your `.env` file
+
+**"Permission denied"**
+- Ensure write permissions to output directories
+
+**"Invalid image format"**
+- Use supported formats: `png`, `jpeg`, `jpg`
+
+**"uv sync fails"**
+- Make sure you have Python 3.12+ installed
+- Try `uv python install 3.12` to install Python via uv
+
+
+---
+
+## **Ready to convert your presentations to professional voiceover content!** 🎙️

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

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+# PPTX to Transcript Configuration
+
+# API Configuration
+api:
+  llama_model: "Llama-4-Maverick-17B-128E-Instruct-FP8" # This notebook uses Llama API to access the model
+  max_retries: 3
+  retry_delay: 1
+  rate_limit_delay: 1
+
+# Processing Configuration
+processing:
+  default_dpi: 200
+  supported_formats: ["png", "jpeg", "jpg"]
+  default_format: "png"
+  batch_size: 5
+
+# File Paths
+paths:
+  default_output_dir: "slide_images"
+  cache_dir: "cache"
+  logs_dir: "logs"
+  temp_dir: "temp"
+
+# Current Project Settings
+current_project:
+  pptx_file: "input/your_powerpoint_file_name_here" # Powerpoint file name (without the ppt/pptx extension)
+  extension: ".pptx"
+  output_dir: "output/"
+
+# LibreOffice Paths (auto-detected, but can be overridden)
+libreoffice:
+  possible_paths:
+    - "/Applications/LibreOffice.app/Contents/MacOS/soffice"
+    - "/usr/bin/soffice"
+    - "/usr/local/bin/soffice"
+    # # Linux paths
+    # - "/snap/bin/libreoffice"
+    # - "/opt/libreoffice/program/soffice"
+    # # Windows paths
+    # - "C:\\Program Files\\LibreOffice\\program\\soffice.exe"
+    # - "C:\\Program Files (x86)\\LibreOffice\\program\\soffice.exe"
+    # - "C:\\Users\\%USERNAME%\\AppData\\Local\\Programs\\LibreOffice\\program\\soffice.exe"
+    # - "C:\\PortableApps\\LibreOfficePortable\\App\\libreoffice\\program\\soffice.exe"
+
+# Logging Configuration
+logging:
+  level: "INFO"
+  format: "%(asctime)s - %(levelname)s - %(message)s"
+  file_enabled: true
+  console_enabled: true
+
+# Progress Tracking
+progress:
+  save_interval: 5  # Save progress every N slides
+  progress_file: "progress.json"
+
+# Image Quality Settings
+image_quality:
+  jpeg_quality: 90
+  jpeg_optimize: true
+  png_compression: 6
+
+# Example System Prompt - Replace with your own, although this one is pretty good.
+system_prompt: |
+  You are a speech-aware GenAI expert who specializes in generating natural-sounding transcripts for human narration and text-to-speech systems.
+  You are also a GenAI expert specializing in LLaMA vision and language models at Meta.
+
+  Your task involves analyzing a PowerPoint slide image and its associated speaker notes to generate a complete, professional transcript suitable for voiceover narration. The voiceover will be recorded for an internal team, so clarity, tone, and spoken correctness are critical.
+  Your goal is to ensure that all technical terms, numbers, and abbreviations are rendered the way a human would say them out loud — clearly, naturally, and without confusion.
+
+  Please follow these detailed steps:
+
+  1. Extract all visual content from the slide image:
+     - Detect and extract all visible text elements including titles, headings, body text, callouts, labels, and captions.
+     - Preserve the top-to-bottom, left-to-right visual order to reflect how a human would naturally read the slide.
+     - Identify any diagrams, tables, or charts and include a brief verbal explanation only if necessary to communicate the slide's key message.
+     - Do not extract hyperlinks.
+
+  2. Combine the extracted text with the provided speaker notes to form a unified understanding of the slide's purpose and content.
+
+  3. Generate a professional voiceover transcript that:
+     - Sounds natural, confident, and informative, as if explaining the slide to an internal executive audience.
+     - Seamlessly blends slide content and speaker notes into a single narrative.
+     - Avoids non-verbal artifacts like slide numbers, bullet points, hyperlinks, or placeholder text such as "click here" or "see above."
+     - Does not include transitional fluff such as "Welcome to…" or "This slide shows…" — only speak the core informational content.
+     - Is suitable for a 1.5–3 minute spoken video.
+
+  4. Ensure the transcript:
+     - Flows logically, even if slide layout is fragmented.
+     - Expands acronyms or technical terms on first use (e.g., "LLM" becomes "Large Language Model, or LLM").
+     - Maintains a neutral, respectful, and professional tone appropriate for stakeholders.
+
+  5. Normalize all numbers, technical terms, and model names into naturally spoken form:
+
+     You must **rewrite all numbers, decimal points, and alphanumeric tokens** so they sound correct when read aloud or used with a text-to-speech system. Use the following phonetic transformation rules:
+
+     - **Decimal numbers**: Convert all numbers with a decimal (e.g., `3.2`) into the form: **"three dot two"**.
+       - Examples:
+         - `3.5` → "three dot five"
+         - `3.1` → "three dot one"
+         - `3.3` → "three dot three"
+         - `2.0` → "two dot oh"
+         - `4.0` → "four dot oh"
+
+     - **Model size suffixes**:
+       - `70B` → "seventy B"
+       - `10M` → "ten M"
+       - `2K` → "two K"
+       - `10B+` → "ten B plus"
+
+     - **Model names**: Break apart letters and digits where needed for natural clarity.
+       - `LLaMA-3.2` → "LLaMA three dot two"
+       - `LLaMA 4 Scout` → "LLaMA four Scout"
+
+     - **Large numbers**: Convert `17B` into "seventeen billion", `128` into "one hundred twenty-eight", etc.
+       - `16 experts` → "sixteen experts"
+       - `128 experts` → "one hundred twenty-eight experts"
+
+     - **Context windows or token counts**: Always use full expansion.
+       - `10M` → "ten million"
+       - `1T` → "one trillion"
+
+     - **Industry Abbreviations**: Break apart letters
+       - "LLM" → "L L M"
+       - "GPU" → "G P U"
+       - "AI" → "A I"
+
+     These spoken-form transformations **must be applied consistently across the entire transcript**.
+
+  Final Output:
+  - Do not leave any numeric or technical token in a written format that would confuse a voiceover or text-to-speech engine.
+  - Provide only the final transcript for voiceover—no markdown, no labels, no extra commentary.
+  - Check for numeric mispronunciations before final output

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

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "6c33ba3a",
+   "metadata": {},
+   "source": [
+    "# PowerPoint to Voiceover Transcript Generator\n",
+    "\n",
+    "This notebook demonstrates the complete workflow for converting PowerPoint presentations into AI-generated voiceover transcripts using Llama 4 Maverick through the Llama API.\n",
+    "\n",
+    "## Overview\n",
+    "\n",
+    "This 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. **Transcript Generation**: Uses Llama vision models to create natural-sounding voiceover content\n",
+    "4. **Speech Optimization**: Converts numbers, technical terms, and abbreviations to spoken form\n",
+    "5. **Results Export**: Saves transcripts in multiple formats for further use\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": null,
+   "id": "21a962b2",
+   "metadata": {},
+   "outputs": [],
+   "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": null,
+   "id": "71c1c8bd",
+   "metadata": {},
+   "outputs": [],
+   "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.transcript_generator import process_slides, TranscriptProcessor\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(\"- 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": null,
+   "id": "53781172",
+   "metadata": {},
+   "outputs": [],
+   "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']}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9386e035",
+   "metadata": {},
+   "outputs": [],
+   "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": "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": null,
+   "id": "644ee94c",
+   "metadata": {},
+   "outputs": [],
+   "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 AI Transcripts\n",
+    "\n",
+    "Use the Llama vision model to analyze each slide image and generate natural-sounding voiceover transcripts.\n",
+    "\n",
+    "This process:\n",
+    "- Analyzes slide visual content using AI vision\n",
+    "- Combines slide content with speaker notes\n",
+    "- Generates speech-optimized transcripts\n",
+    "- Converts numbers and technical terms to spoken form"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fe564b99",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"PROCESSING: Starting AI transcript generation...\")\n",
+    "print(f\"- Processing {len(notes_df)} slides\")\n",
+    "print(f\"- Using model: {config['api']['llama_model']}\")\n",
+    "print(\"- This may take several minutes...\")\n",
+    "\n",
+    "# Initialize processor and generate transcripts\n",
+    "processor = TranscriptProcessor()\n",
+    "processed_df = processor.process_slides_dataframe(\n",
+    "    df=notes_df,\n",
+    "    output_dir=output_dir\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():,}\")"
+   ]
+  },
+  {
+   "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": null,
+   "id": "8463ac3a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"PROCESSING: Saving results in multiple formats...\")\n",
+    "\n",
+    "# Create output directory\n",
+    "os.makedirs(output_dir, exist_ok=True)\n",
+    "\n",
+    "# Save complete results with all metadata\n",
+    "output_file = f\"{output_dir}processed_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",
+    "transcript_only = processed_df[['slide_number', 'slide_title', 'ai_transcript']]\n",
+    "transcript_file = f\"{output_dir}transcripts_only.csv\"\n",
+    "transcript_only.to_csv(transcript_file, index=False)\n",
+    "print(f\"- SUCCESS: Transcripts only saved to {transcript_file}\")\n",
+    "\n",
+    "# Save as JSON for API integration\n",
+    "json_file = f\"{output_dir}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\"- 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\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8728d2ac",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# Completion Summary\n",
+    "\n",
+    "## Successfully Generated:\n",
+    "- **Slide Images**: High-resolution images for AI analysis\n",
+    "- **AI Transcripts**: Speech-optimized voiceover content\n",
+    "- **Multiple Formats**: CSV, JSON exports for different use cases\n",
+    "- **Analysis**: Visual insights into content distribution and quality\n",
+    "\n",
+    "## Output Files:\n",
+    "- `processed_transcripts.csv` - Complete dataset with all metadata\n",
+    "- `transcripts_only.csv` - Just slide numbers, titles, and transcripts\n",
+    "- `transcripts.json` - JSON format for API integration\n",
+    "- Individual slide images in PNG/JPEG format\n",
+    "\n",
+    "## Next Steps:\n",
+    "1. **Review** generated transcripts for accuracy and tone\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",
+    "\n",
+    "## Tips for Better Results:\n",
+    "- **Rich Speaker Notes**: Slides with detailed notes generate better transcripts\n",
+    "- **Clear Visuals**: High-contrast slides with readable text work best\n",
+    "- **Consistent Style**: Maintain consistent formatting across your presentation\n",
+    "- **Review & Edit**: Always review AI-generated content before final use\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
+}

end-to-end-use-cases/powerpoint-to-voiceover-transcript/pyproject.toml

@@ -0,0 +1,16 @@
+[project]
+name = "pptx-to-transcript"
+version = "0.1.0"
+description = "Convert PowerPoint presentations to transcripts with AI-powered analysis"
+requires-python = ">=3.12"
+dependencies = [
+    "pandas>=2.3.1",
+    "pillow>=11.3.0",
+    "python-pptx>=1.0.2",
+    "pymupdf>=1.24.0",
+    "numpy>=2.3.2",
+    "python-dotenv>=1.0.0",
+    "tqdm>=4.66.0",
+    "llama-api-client>=0.1.0",
+    "pyyaml>=6.0.0",
+]

end-to-end-use-cases/powerpoint-to-voiceover-transcript/src/__init__.py

@@ -0,0 +1,22 @@
+"""
+PPTX to Transcript - A tool for converting PowerPoint presentations to AI-generated transcripts.
+
+This package provides functionality to:
+- Extract speaker notes and slide content from PPTX files
+- Convert slides to images
+- Generate AI-powered transcripts using Llama models
+"""
+
+__version__ = "0.1.0"
+__author__ = "Yuce Dincer"
+
+from .core.pptx_processor import extract_pptx_notes, pptx_to_images_and_notes
+from .processors.transcript_generator import process_slides
+from .config.settings import load_config
+
+__all__ = [
+    "extract_pptx_notes",
+    "pptx_to_images_and_notes",
+    "process_slides",
+    "load_config"
+]

end-to-end-use-cases/powerpoint-to-voiceover-transcript/src/config/__init__.py

@@ -0,0 +1,5 @@
+"""Configuration management for PPTX to Transcript."""
+
+from .settings import load_config, get_config
+
+__all__ = ["load_config", "get_config"]

end-to-end-use-cases/powerpoint-to-voiceover-transcript/src/config/settings.py

@@ -0,0 +1,147 @@
+"""Configuration management using YAML files."""
+
+import os
+import yaml
+from pathlib import Path
+from typing import Dict, Any, Optional
+
+# Global config cache
+_config_cache: Optional[Dict[str, Any]] = None
+
+
+def load_config(config_path: Optional[str] = None) -> Dict[str, Any]:
+    """
+    Load configuration from YAML file.
+
+    Args:
+        config_path: Path to config file. If None, looks for config.yaml in project root.
+
+    Returns:
+        Dictionary containing configuration settings
+
+    Raises:
+        FileNotFoundError: If config file doesn't exist
+        yaml.YAMLError: If config file is invalid YAML
+    """
+    global _config_cache
+
+    if config_path is None:
+        # Look for config.yaml in project root (parent of src directory)
+        current_dir = Path(__file__).parent.parent.parent
+        config_file_path = current_dir / "config.yaml"
+    else:
+        config_file_path = Path(config_path)
+
+    if not config_file_path.exists():
+        raise FileNotFoundError(f"Configuration file not found: {config_file_path}")
+
+    try:
+        with open(str(config_file_path), 'r', encoding='utf-8') as f:
+            config = yaml.safe_load(f)
+
+        # Cache the config
+        _config_cache = config
+
+        # Load environment variables for API keys
+        _load_env_variables(config)
+
+        return config
+
+    except yaml.YAMLError as e:
+        raise yaml.YAMLError(f"Invalid YAML in config file {config_file_path}: {e}")
+
+
+def get_config() -> Dict[str, Any]:
+    """
+    Get cached configuration. Loads from default location if not already loaded.
+
+    Returns:
+        Dictionary containing configuration settings
+    """
+    global _config_cache
+
+    if _config_cache is None:
+        _config_cache = load_config()
+
+    return _config_cache
+
+
+def _load_env_variables(config: Dict[str, Any]) -> None:
+    """
+    Load environment variables and add them to config.
+
+    Args:
+        config: Configuration dictionary to update
+    """
+    # Load API key from environment
+    llama_api_key = os.getenv('LLAMA_API_KEY')
+    if llama_api_key:
+        if 'api' not in config:
+            config['api'] = {}
+        config['api']['llama_api_key'] = llama_api_key
+
+
+def get_system_prompt() -> str:
+    """
+    Get the system prompt from configuration.
+
+    Returns:
+        System prompt string
+    """
+    config = get_config()
+    return config.get('system_prompt', '')
+
+
+def get_api_config() -> Dict[str, Any]:
+    """
+    Get API configuration settings.
+
+    Returns:
+        Dictionary containing API settings
+    """
+    config = get_config()
+    return config.get('api', {})
+
+
+def get_processing_config() -> Dict[str, Any]:
+    """
+    Get processing configuration settings.
+
+    Returns:
+        Dictionary containing processing settings
+    """
+    config = get_config()
+    return config.get('processing', {})
+
+
+def get_paths_config() -> Dict[str, Any]:
+    """
+    Get file paths configuration.
+
+    Returns:
+        Dictionary containing path settings
+    """
+    config = get_config()
+    return config.get('paths', {})
+
+
+def get_libreoffice_paths() -> list:
+    """
+    Get possible LibreOffice installation paths.
+
+    Returns:
+        List of possible LibreOffice paths
+    """
+    config = get_config()
+    return config.get('libreoffice', {}).get('possible_paths', [])
+
+
+def get_image_quality_config() -> Dict[str, Any]:
+    """
+    Get image quality configuration settings.
+
+    Returns:
+        Dictionary containing image quality settings
+    """
+    config = get_config()
+    return config.get('image_quality', {})

end-to-end-use-cases/powerpoint-to-voiceover-transcript/src/core/__init__.py

@@ -0,0 +1,14 @@
+"""Core functionality for PPTX to Transcript processing."""
+
+from .image_processing import encode_image
+from .pptx_processor import extract_pptx_notes, pptx_to_images_and_notes
+from .llama_client import LlamaClient
+from .file_utils import check_libreoffice
+
+__all__ = [
+    "encode_image",
+    "extract_pptx_notes",
+    "pptx_to_images_and_notes",
+    "LlamaClient",
+    "check_libreoffice"
+]

end-to-end-use-cases/powerpoint-to-voiceover-transcript/src/core/file_utils.py

@@ -0,0 +1,53 @@
+"""File system utilities for PPTX to Transcript."""
+
+import shutil
+from pathlib import Path
+from ..config.settings import get_libreoffice_paths
+
+
+def check_libreoffice() -> str:
+    """
+    Find LibreOffice soffice binary.
+
+    Returns:
+        Path to the soffice binary as a string
+
+    Raises:
+        FileNotFoundError: If LibreOffice is not found
+    """
+    # Get possible paths from config
+    possible_paths = get_libreoffice_paths()
+
+    # Add shutil.which result to the list
+    which_result = shutil.which("soffice")
+    if which_result:
+        possible_paths.insert(1, which_result)  # Insert after the first path
+
+    for path in possible_paths:
+        if path and Path(path).exists():
+            return str(path)
+
+    raise FileNotFoundError(
+        "LibreOffice not found! Please install LibreOffice or update the paths in config.yaml"
+    )
+
+
+def ensure_directory_exists(directory_path: Path) -> None:
+    """
+    Ensure a directory exists, creating it if necessary.
+
+    Args:
+        directory_path: Path to the directory
+    """
+    directory_path.mkdir(parents=True, exist_ok=True)
+
+
+def get_project_root() -> Path:
+    """
+    Get the project root directory.
+
+    Returns:
+        Path to the project root directory
+    """
+    # Go up from src/core to project root
+    return Path(__file__).parent.parent.parent

end-to-end-use-cases/powerpoint-to-voiceover-transcript/src/core/image_processing.py

@@ -0,0 +1,31 @@
+"""Image processing utilities for PPTX to Transcript."""
+
+import base64
+from typing import Union
+from pathlib import Path
+
+
+def encode_image(image_path: Union[str, Path]) -> str:
+    """
+    Encode an image file as a base64 string.
+
+    Args:
+        image_path: Path to the image file
+
+    Returns:
+        Base64-encoded image data as a string
+
+    Raises:
+        FileNotFoundError: If the image file doesn't exist
+        IOError: If there's an error reading the image file
+    """
+    image_path = Path(image_path)
+
+    if not image_path.exists():
+        raise FileNotFoundError(f"Image file not found: {image_path}")
+
+    try:
+        with open(image_path, "rb") as image_file:
+            return base64.b64encode(image_file.read()).decode("utf-8")
+    except IOError as e:
+        raise IOError(f"Error reading image file {image_path}: {e}")

end-to-end-use-cases/powerpoint-to-voiceover-transcript/src/core/llama_client.py

@@ -0,0 +1,130 @@
+"""Llama API client wrapper for PPTX to Transcript."""
+
+from typing import Optional, Any, Union
+from llama_api_client import LlamaAPIClient
+from .image_processing import encode_image
+from ..config.settings import get_api_config, get_system_prompt
+
+
+class LlamaClient:
+    """Wrapper for Llama API client with configuration management."""
+
+    def __init__(self, api_key: Optional[str] = None):
+        """
+        Initialize Llama client.
+
+        Args:
+            api_key: API key for Llama. If None, will be loaded from config/environment.
+        """
+        api_config = get_api_config()
+
+        if api_key is None:
+            api_key = api_config.get('llama_api_key')
+
+        if not api_key:
+            raise ValueError("Llama API key not found. Set LLAMA_API_KEY environment variable or provide api_key parameter.")
+
+        self.client = LlamaAPIClient(api_key=api_key)
+        self.model = api_config.get('llama_model', 'Llama-4-Maverick-17B-128E-Instruct-FP8')
+
+    def generate_transcript(
+        self,
+        image_path: str,
+        speaker_notes: str = "",
+        system_prompt: Optional[str] = None,
+        stream: bool = False
+    ) -> Union[str, Any]:
+        """
+        Generate transcript from slide image and speaker notes.
+
+        Args:
+            image_path: Path to the slide image
+            speaker_notes: Speaker notes for the slide
+            system_prompt: Custom system prompt. If None, uses default from config.
+            stream: Whether to stream the response
+
+        Returns:
+            Generated transcript text if not streaming, otherwise the response object
+        """
+        if system_prompt is None:
+            system_prompt = get_system_prompt()
+
+        encoded_image = encode_image(image_path)
+
+        response = self.client.chat.completions.create(
+            model=self.model,
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": f"Speaker Notes: {speaker_notes}",
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:image/png;base64,{encoded_image}",
+                            },
+                        },
+                    ],
+                },
+            ],
+            stream=stream,
+        )
+
+        if stream:
+            return response
+        else:
+            return response.completion_message.content.text
+
+    def run(
+        self,
+        image_path: str,
+        system_prompt: str,
+        user_prompt: str,
+        stream: bool = False
+    ) -> Union[str, Any]:
+        """
+        Legacy method for backward compatibility with notebook code.
+
+        Args:
+            image_path: Path to the image file
+            system_prompt: System prompt for the chat completion
+            user_prompt: User prompt (speaker notes)
+            stream: Whether to stream the response
+
+        Returns:
+            Response from the chat completion
+        """
+        encoded_image = encode_image(image_path)
+
+        response = self.client.chat.completions.create(
+            model=self.model,
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "text",
+                            "text": f"Speaker Notes: {user_prompt}",
+                        },
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": f"data:image/png;base64,{encoded_image}",
+                            },
+                        },
+                    ],
+                },
+            ],
+            stream=stream,
+        )
+
+        if stream:
+            for chunk in response:
+                print(chunk.event.delta.text, end="", flush=True)
+        else:
+            return response

end-to-end-use-cases/powerpoint-to-voiceover-transcript/src/core/pptx_processor.py

@@ -0,0 +1,208 @@
+"""PPTX processing functionality for extracting notes and converting to images."""
+
+import pandas as pd
+import subprocess
+import io
+import fitz  # PyMuPDF
+from PIL import Image
+from pptx import Presentation
+from typing import Dict, Any, Union
+from pathlib import Path
+
+from .file_utils import check_libreoffice, ensure_directory_exists
+from ..config.settings import get_processing_config, get_image_quality_config
+
+
+def extract_pptx_notes(pptx_path: Union[str, Path]) -> pd.DataFrame:
+    """
+    Extract speaker notes from PPTX file.
+
+    Args:
+        pptx_path: Path to PPTX file
+
+    Returns:
+        DataFrame with columns:
+            - slide_number: Slide number (1-indexed)
+            - slide_title: Title of the slide (first line of text, max 100 chars)
+            - slide_text: Text content of the slide
+            - speaker_notes: Speaker notes for the slide
+            - has_notes: Whether the slide has speaker notes
+            - notes_word_count: Number of words in the speaker notes
+            - slide_text_word_count: Number of words in the slide text
+            - image_filename: Suggested filename for the slide image (e.g. "slide-001.png")
+    """
+    pptx_path = Path(pptx_path)
+    processing_config = get_processing_config()
+    default_format = processing_config.get('default_format', 'png')
+
+    prs = Presentation(pptx_path)
+    slides_data = []
+
+    for slide_num, slide in enumerate(prs.slides, 1):
+        # Extract slide text (from shapes on the slide)
+        slide_text = []
+        slide_title = ""
+
+        for shape in slide.shapes:
+            if hasattr(shape, "text") and shape.text.strip():
+                text = shape.text.strip()
+                slide_text.append(text)
+
+                # Try to identify title (usually the first text or largest text)
+                if not slide_title and text:
+                    slide_title = text.split('\n')[0][:100]  # First line, max 100 chars
+
+        # Extract speaker notes
+        notes_text = ""
+        if slide.has_notes_slide:
+            notes_slide = slide.notes_slide
+            if notes_slide.notes_text_frame:
+                notes_text = notes_slide.notes_text_frame.text.strip()
+
+        slides_data.append({
+            'slide_number': slide_num,
+            'slide_title': slide_title,
+            'slide_text': '\n'.join(slide_text),
+            'speaker_notes': notes_text,
+            'has_notes': bool(notes_text),
+            'notes_word_count': len(notes_text.split()) if notes_text else 0,
+            'slide_text_word_count': len('\n'.join(slide_text).split()) if slide_text else 0,
+            'image_filename': f"slide-{slide_num:03d}.{default_format}"
+        })
+
+    return pd.DataFrame(slides_data)
+
+
+def pptx_to_images_and_notes(
+    pptx_path: Union[str, Path],
+    output_dir: Union[str, Path],
+    dpi: Union[int, None] = None,
+    fmt: Union[str, None] = None,
+    extract_notes: bool = True
+) -> Dict[str, Any]:
+    """
+    Convert PPTX to images and extract notes.
+
+    Args:
+        pptx_path: Path to PPTX file
+        output_dir: Directory to save images and notes
+        dpi: Image resolution (uses config default if None)
+        fmt: Image format - "png" or "jpeg" (uses config default if None)
+        extract_notes: Whether to extract speaker notes
+
+    Returns:
+        Dict with image_files, notes_df, and output_dir
+
+    Raises:
+        FileNotFoundError: If PPTX file or LibreOffice not found
+        RuntimeError: If conversion fails
+    """
+    pptx_path = Path(pptx_path).resolve()
+    output_dir = Path(output_dir).resolve()
+
+    # Load configuration
+    processing_config = get_processing_config()
+    image_quality_config = get_image_quality_config()
+
+    if dpi is None:
+        dpi = processing_config.get('default_dpi', 200)
+    if fmt is None:
+        fmt = processing_config.get('default_format', 'png')
+
+    # Validate format
+    supported_formats = processing_config.get('supported_formats', ['png', 'jpeg', 'jpg'])
+    if fmt not in supported_formats:
+        raise ValueError(f"Unsupported format '{fmt}'. Supported formats: {supported_formats}")
+
+    ensure_directory_exists(output_dir)
+
+    print(f"Processing: {pptx_path.name}")
+
+    # Step 1: Extract notes if requested
+    notes_df = pd.DataFrame()
+    if extract_notes:
+        print("Extracting speaker notes...")
+        try:
+            notes_df = extract_pptx_notes(pptx_path)
+            # Update image filenames based on actual format
+            notes_df['image_filename'] = notes_df['slide_number'].apply(
+                lambda x: f"slide-{x:03d}.{fmt}"
+            )
+
+            notes_with_content = notes_df[notes_df['has_notes']]
+            print(f"Found notes on {len(notes_with_content)} of {len(notes_df)} slides")
+
+            # Save df to CSV
+            csv_file = output_dir / f"{pptx_path.stem}_notes.csv"
+            notes_df.to_csv(csv_file, index=False, encoding='utf-8')
+            print(f"Notes df saved to: {csv_file}")
+
+        except Exception as e:
+            print(f"Warning: Could not extract notes: {e}")
+            notes_df = pd.DataFrame()
+
+    # Step 2: Convert to images
+    soffice = check_libreoffice()
+
+    # Clean previous outputs
+    for f in output_dir.glob("slide*"):
+        if f.is_file():
+            f.unlink()
+
+    # Convert PPTX → PDF
+    pdf_path = output_dir / f"{pptx_path.stem}.pdf"
+
+    print("Converting to PDF...")
+    try:
+        subprocess.run([
+            soffice,
+            "--headless",
+            "--convert-to", "pdf",
+            "--outdir", str(output_dir),
+            str(pptx_path)
+        ], check=True, capture_output=True, text=True)
+    except subprocess.CalledProcessError as e:
+        raise RuntimeError(f"Failed to convert PPTX to PDF: {e}")
+
+    if not pdf_path.exists():
+        raise FileNotFoundError(f"PDF was not created: {pdf_path}")
+
+    # Convert PDF → Images
+    print(f"Converting to {fmt.upper()} images at {dpi} DPI...")
+    doc = fitz.open(str(pdf_path))
+
+    for page_num in range(len(doc)):
+        page = doc.load_page(page_num)
+        zoom = dpi / 72
+        mat = fitz.Matrix(zoom, zoom)
+        pix = page.get_pixmap(matrix=mat)
+
+        image_path = output_dir / f"slide-{page_num+1:03d}.{fmt}"
+
+        if fmt.lower() == "png":
+            pix.save(str(image_path))
+        elif fmt.lower() in ["jpg", "jpeg"]:
+            img_data = pix.tobytes("png")
+            pil_img = Image.open(io.BytesIO(img_data))
+            pil_img = pil_img.convert("RGB")
+
+            # Get quality settings from config
+            quality = image_quality_config.get('jpeg_quality', 90)
+            optimize = image_quality_config.get('jpeg_optimize', True)
+
+            pil_img.save(str(image_path), "JPEG", quality=quality, optimize=optimize)
+
+        pix = None
+
+    doc.close()
+
+    # Summary
+    image_files = sorted(list(output_dir.glob(f"slide*.{fmt}")))
+    print(f"\nSuccessfully processed {len(image_files)} slides")
+    print(f"Images saved to: {output_dir}")
+
+    return {
+        'image_files': image_files,
+        'notes_df': notes_df,
+        'output_dir': output_dir
+    }

end-to-end-use-cases/powerpoint-to-voiceover-transcript/src/processors/__init__.py

@@ -0,0 +1,5 @@
+"""Processing modules for PPTX to Transcript."""
+
+from .transcript_generator import process_slides, TranscriptProcessor
+
+__all__ = ["process_slides", "TranscriptProcessor"]

end-to-end-use-cases/powerpoint-to-voiceover-transcript/src/processors/transcript_generator.py

@@ -0,0 +1,115 @@
+"""Transcript generation processor for PPTX slides."""
+
+from pathlib import Path
+from typing import Optional, Union
+
+import pandas as pd
+from tqdm import tqdm
+
+from ..config.settings import get_processing_config
+
+from ..core.llama_client import LlamaClient
+
+
+class TranscriptProcessor:
+    """Processor for generating transcripts from slide images and notes."""
+
+    def __init__(self, api_key: Optional[str] = None):
+        """
+        Initialize transcript processor.
+
+        Args:
+            api_key: Llama API key. If None, will be loaded from config/environment.
+        """
+        self.client = LlamaClient(api_key=api_key)
+        self.processing_config = get_processing_config()
+
+    def process_single_slide(
+        self,
+        image_path: Union[str, Path],
+        speaker_notes: str = "",
+        system_prompt: Optional[str] = None,
+    ) -> str:
+        """
+        Process a single slide to generate transcript.
+
+        Args:
+            image_path: Path to the slide image
+            speaker_notes: Speaker notes for the slide
+            system_prompt: Custom system prompt. If None, uses default from config.
+
+        Returns:
+            Generated transcript text
+        """
+        return self.client.generate_transcript(
+            image_path=str(image_path),
+            speaker_notes=speaker_notes,
+            system_prompt=system_prompt,
+            stream=False,
+        )
+
+    def process_slides_dataframe(
+        self,
+        df: pd.DataFrame,
+        output_dir: Union[str, Path],
+        system_prompt: Optional[str] = None,
+    ) -> pd.DataFrame:
+        """
+        Process slides from a DataFrame containing slide information.
+
+        Args:
+            df: DataFrame with slide information (from extract_pptx_notes)
+            output_dir: Directory containing slide images
+            system_prompt: Custom system prompt. If None, uses default from config.
+
+        Returns:
+            DataFrame with added 'ai_transcript' column
+        """
+        output_dir = Path(output_dir)
+        df_copy = df.copy()
+
+        for i in tqdm(range(len(df_copy)), desc="Processing slides"):
+            # Get data for current slide
+            slide_filename = df_copy.iloc[i]["image_filename"]
+            speaker_notes = (
+                df_copy.iloc[i]["speaker_notes"]
+                if pd.notna(df_copy.iloc[i]["speaker_notes"])
+                else ""
+            )
+
+            image_path = output_dir / slide_filename
+
+            # Generate transcript
+            transcript = self.process_single_slide(
+                image_path=image_path,
+                speaker_notes=speaker_notes,
+                system_prompt=system_prompt,
+            )
+
+            # Add to dataframe
+            df_copy.loc[i, "ai_transcript"] = transcript
+
+        return df_copy
+
+
+def process_slides(
+    df: pd.DataFrame,
+    output_dir: Union[str, Path] = "slide_images",
+    api_key: Optional[str] = None,
+    system_prompt: Optional[str] = None,
+) -> pd.DataFrame:
+    """
+    Legacy function for backward compatibility with notebook code.
+    Process slides from a DataFrame to generate transcripts.
+
+    Args:
+        df: DataFrame with slide information (from extract_pptx_notes)
+        output_dir: Directory containing slide images
+        api_key: Llama API key. If None, will be loaded from config/environment.
+        system_prompt: Custom system prompt. If None, uses default from config.
+
+    Returns:
+        DataFrame with added 'ai_transcript' column
+    """
+    processor = TranscriptProcessor(api_key=api_key)
+    return processor.process_slides_dataframe(df, output_dir, system_prompt)

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