Auto-Generate Presentation Notes: Vision Instruct Models on DigitalOcean

Want to create killer presentations without the hours of manual work summarizing slides? Learn how to use AI-powered Vision Instruct models on DigitalOcean to generate concise notes directly from your slides.

DigitalOcean and Hugging Face have teamed up to bring advanced multi-modal AI to developers. This means you can easily process both images and text, opening up a world of possibilities for automating tasks and streamlining workflows.

What are Vision Instruct Models?

Vision Instruct models are cutting-edge AI that understand both visual and textual information. They're perfect for tasks like:

• Analyzing images and videos.
• Creating images from text descriptions.
• Generating captions for images.
• Answering questions about visual content.
• Powering multimodal chatbots.

These models simplify complex AI tasks and are ideal for developers, data scientists, and anyone looking to boost productivity.

What You'll Achieve

In this guide, you'll learn how to:

• Convert a PDF presentation into individual slide images.
• Use a Python script to interact with a Vision Instruct model hosted on DigitalOcean.
• Automatically generate context-aware summaries for each slide.
• Improve presentation quality by aligning your talking points with the visuals.

Prerequisites

Before you start, make sure you have the following:

• A Linux or Mac-based developer laptop (Windows users can use a VM or Cloud Instance).
• Python 3.10 or newer. It's recommended to use a virtual environment.
• Install necessary libraries: pip install huggingface_hub.
• Install ImageMagick for converting PDFs to images.
• A presentation in PDF format (example provided below).

Automation: A Powerful Use Case

Manually summarizing slides is tedious. Vision Instruct models automate this process by quickly analyzing slide images and your presentation abstract. This boosts efficiency and is perfect for educators, professionals, or anyone seeking high-quality presentations without spending extra time.

Beyond slide summarization, imagine:

• Generating alt-text for image accessibility.
• Automating content tagging for digital libraries.
• Creating previews for image-heavy reports.

By using Vision Instruct models, you simplify repetitive tasks and build the foundation for integrated, AI-driven processes across your projects – a huge time saver on tedious tasks.

Step 1: Deploying Your Vision Instruct Model

Deploying a Vision Instruct model on DigitalOcean is extremely simple:

1. Create a GPU Droplet on DigitalOcean.
2. Select the Vision Instruct model from pre-built options.
3. That's it! Your model is ready.

Step 2: Converting Slides to Images

Download this sample presentation from an NVIDIA GTC session: [Crack the AI Black Box: Practical Techniques for Explainable AI [S74147]](https://developer.nvidia.com/gtc/2024/session/s74147).

Use ImageMagick to convert your PDF slide deck to PNG images:

magick your_presentation.pdf slide_%03d.png

This will output images like slide_001.png, slide_002.png, etc.

Move these images into a subfolder called slides_images in your project's working directory.

Next, upload the entire folder to a DigitalOcean Spaces bucket and make sure the folder’s permissions are set to Public. This guarantees your Python application can access the images via direct URLs.

Step 3: Generating Summaries with the Vision Instruct Model

Use the following Python script to interact with your hosted Vision Instruct model and generate summaries based on the images and abstract.

If you're using the sample NVIDIA GTC session, use this abstract:

Artificial Intelligence often operates in ways that are challenging to interpret, creating a gap in trust and transparency. Explainable AI (XAI) bridges this gap by providing strategies to demystify complex models, enabling stakeholders to understand how decisions are made. We'll explore foundational XAI concepts and offer practical methods to bring interpretability into developing and deploying AI systems, ensuring better decision-making and accountability. You'll learn actionable techniques for explaining AI behavior, from feature attributions and decision-path analyses to scenario-based insights. Through a live demonstration, you'll see how to apply these methods to real-world problems, enabling you to effectively diagnose, debug, and optimize your models. In the end, you'll have a clear roadmap for integrating XAI practices into your workflows to build trust and confidence in AI-powered solutions.

Before you run the script, remember to replace the placeholders in the script with:

• The IP address of your 1-click Model/Droplet.
• Your Bearer Token (found by logging into your Droplet).
• The FQDN for your Spaces Bucket.
• The Session Abstract.

#!/usr/bin/env python3 
import os
from huggingface_hub import InferenceClient
# ------------------------------------------------------------------------------ 
# Configuration 
# ------------------------------------------------------------------------------ 
# Change this to the IP address/URL where your inference server is running 
BASE_URL = "http://<REPLACE WITH YOUR 1-CLICK MODEL IP>/v1" 
 # Provide your token via the environment variable BEARER_TOKEN, or hardcode here 
API_KEY = "<REPLACE WITH YOUR BEARER_TOKEN>" # os.getenv("BEARER_TOKEN") 
 # Directory containing your local slides, but assume they're *already* uploaded somewhere 
 # so we won't directly read from here. Instead, we just build a URL for each slide name. 
IMAGES_DIR = "./slides_images" 
 # Example URL prefix where your images are hosted 
 # In practice, you might dynamically generate or retrieve these URLs 
IMAGE_URL_PREFIX = "<YOUR UNIQUE DIGITALOCEAN BUCKET NAME>/slides_images" 
 # Abstract text describing the overall presentation 
ABSTRACT_TEXT = ( 
 "<REPLACE WITH A SESSION ABSTRACT FOR YOUR SLIDES>" 
) 
 # Initialize the inference client 
client = InferenceClient ( 
base_url = BASE_URL, 
api_key = API_KEY
) 
 # ------------------------------------------------------------------------------ 
 # Helper Function 
 # ------------------------------------------------------------------------------ 
 def generate_slide_summary ( slide_file: str, slide_number: int, abstract_text: str) -> str: 
 """
Sends the abstract text and an image URL to the InferenceClient's chat endpoint.
The returned string is the summary generated by the remote model.
""" 
 # Construct the final URL to the hosted slide image 
 # e.g., https://my-image-bucket.example.com/slides/slide_1.png 
slide_url = f" { IMAGE_URL_PREFIX} / { slide_file} " 
 # Build the chat messages. Instead of base64 data, we pass a hosted URL. 
messages = [ 
 { 
 "role": "user", 
 "content": [ 
 { 
 "type": "text", 
 "text": f"Presentation Abstract: { abstract_text} " 
} 
], 
}, 
 { 
 "role": "user", 
 "content": [ 
 { 
 "type": "image_url", 
 "image_url": { 
 "url": slide_url
} 
}, 
 { 
 "type": "text", 
 "text": ( 
 f"Slide number { slide_number}. " 
 "Please summarize this slide based on the context of the abstract." 
), 
} 
], 
} 
] 
 # Request a completion from the inference endpoint 
response = client. chat. completions. create ( 
messages = messages, 
temperature = 0.7, 
top_p = 0.95, 
max_tokens = 150, 
) 
 # Extract the model's reply 
 return response [ "choices"] [ 0] [ "message"] [ "content"] 
 # ------------------------------------------------------------------------------ 
 # Main Routine 
 # ------------------------------------------------------------------------------ 
 def main (): 
 # Look for any PNG slides in the local directory, but assume they're all uploaded 
 # to your hosting location. The local directory listing is just so we can parse 
 # the filenames and build URLs. 
slide_images = sorted ( 
 [ f for f in os. listdir ( IMAGES_DIR) if f. lower (). endswith ( ".png")] 
) 
 if not slide_images: 
 print ( "No slide images found in the specified directory.") 
 return 
 # For each slide, generate a summary 
 for idx, slide_file in enumerate ( slide_images, start = 1): 
 print ( f"\n--- Generating summary for { slide_file} ---") 
slide_summary = generate_slide_summary ( slide_file, idx, ABSTRACT_TEXT) 
 print ( f"Summary:\n { slide_summary} ") 
 if __name__ == "__main__": 
main ()

Once executed, the script outputs session notes on a slide-by-slide basis, describing each slide:

--- Generating summary for slide_004.png ---
Summary:
Slide 5 illustrates flawed data, which is a key challenge in AI/ML. The slide features two main bullet points:
 1. AI/ML only as good as the data: This highlights the importance of data quality in AI/ML models, emphasizing that the accuracy and reliability of AI/ML outputs directly depend on the quality of the input data.
 2. Real-world examples of flawed data: This section lists real-world examples of flawed data, including:
- Recruiter AI + male-skewed: This example illustrates how biased data can lead to discriminatory outcomes, such as in recruitment AI systems that favor male candidates due to biased data.
- Offensive AI Chatbot: This example highlights how flawed data can result in offensive or inappropriate responses from AI chat
--- Generating summary for slide_005.png ---
Summary:
This slide introduces the topic of Explainable AI ( XAI) by highlighting the importance of trust, transparency, debugging, improvement, compliance, and ethics. It emphasizes that XAI helps bridge the gap between AI decision-making processes and stakeholders ' understanding of those processes. The slide also lists key goals of XAI, which include interpretability, accountability, and fairness, as well as bias detection.
The slide serves as an introduction to the course, setting the stage for the practical methods and techniques that will be covered in the subsequent slides. By emphasizing the importance of trust, transparency, and accountability in AI decision-making, the slide establishes the context for the course' s focus on XAI and its applications.

FAQs

1. What is the purpose of Vision Instruct models? Vision Instruct models excel at handling multi-modal tasks, integrating both visual and textual data. They generate summaries, descriptions, and captions from images and more, making them powerful tools for AI applications like image captioning and visual Q&A.

2. How do I convert PDFs to images? Use ImageMagick. It's an open-source software made for editing and manipulating images, which gives you all the tools needed to convert PDFs into popular formats (PNG, JPEG, etc.).

3. What's the role of Hugging Face’s InferenceClient? The InferenceClient lets you easily communicate with the remotely hosted Vision Instruct model. It enables automatic, context-aware summaries for each slide.

4. How do I align talking points with visuals? Understand the content and message of each slide. Vision Instruct models provide concise summaries, to guide you in crafting talking points that are relevant and accurately support your visuals for a cohesive and engaging presentation.

5. Can Vision Instruct models do more than slide summarization? Absolutely! They're great for image captioning, visual Q&A, alt-text generation, automated content tagging, image-heavy report previews, and more.

6. How to deploy a Vision Instruct model to DigitalOcean: Create a GPU Droplet, then select the Vision Instruct model to automatically set up the necessary environment.

7. What are the benefits of using Vision Instruct models for slide summarization? Vision Instruct models provide numerous benefits for slide summarization. They dramatically reduce the manual effort required to create summaries, saving time and boosting productivity. These models generate highly accurate, context-aware summaries meaning each slide’s essential messages are effectively captured.