Automate Presentation Notes: Using Vision Instruct AI on DigitalOcean

Want to generate presentation notes and streamline your workflow? DigitalOcean's Vision Instruct models, powered by Hugging Face, let you effortlessly integrate AI into your projects. Follow this guide to automatically create summaries from your slides, saving you time and boosting efficiency.

What are Vision Instruct Models?

Vision Instruct AI models process both images and text. They're ideal for tasks requiring visual data analysis with textual instructions. This makes them perfect for:

• Analyzing images and videos.
• Generating image captions and text-to-image synthesis.
• Creating visual question answering systems.
• Building multimodal chatbots.

Vision Instruct excels at simplifying AI integration for developers, data scientists, and anyone automating tasks.

What You’ll Achieve

In this tutorial, you'll learn to:

• Convert a PDF presentation into individual image slides.
• Use a Python script to interact with a remote Hugging Face Vision Instruct model on DigitalOcean.
• Automatically create concise, context-aware summaries for each slide.

By the end, your talking points will align perfectly with your slides, enhancing presentation quality.

Prerequisites

Before you start you'll need:

• A Linux or Mac-based development computer (Windows users can use a VM or cloud instance).
• Python 3.10+ (using a virtual environment is recommended).
• The huggingface_hub library installed (pip install huggingface_hub).
• ImageMagick installed for PDF-to-image conversion.
• A slide deck in PDF format.

Streamline Workflows with AI Automation

Creating slide summaries manually is tedious, especially with a lot of content. Vision Instruct models streamline this by quickly interpreting slide images and abstracts. This saves valuable time for:

• Educators preparing lectures.
• Professionals crafting presentations.
• Anyone wanting high-quality materials.

Beyond summarization, Vision Instruct models can generate alt-text for accessibility, automate content tagging, and create image report previews. This flexibility extends the AI technology to multiple workflows, making complex visual data digestible. By using Vision Instruct, AI-driven processes can be more integrated.

Step 1: Deploy Your Vision Instruct Model on DigitalOcean

Deploying Vision Instruct on DigitalOcean is fast and easy, here's how:

1. Create a GPU Droplet: Choose a Droplet that fits your workload.
2. Select the Vision Instruct model: Choose the model that works for you.
3. That's it! Your model is ready!

Step 2: Converting Your Slides to Images

Use ImageMagick to convert your PDF slide deck into PNG images. Run this command in your terminal:

magick your_presentation.pdf slide_%03d.png

This creates images like slide_001.png, slide_002.png, etc. Move these images into a subfolder named slides_images in your project directory.

Then, upload the images to a DigitalOcean Spaces bucket and set the permissions to “Public”. This is necessary so your Python can access them using a direct URL.

Step 3: Generating Summaries with Vision Instruct

Use the following Python script to interact with your DigitalOcean-hosted Vision Instruct model. This script generates summaries using the uploaded images and an 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()

Before running the script:

1. Replace <REPLACE WITH YOUR 1-CLICK MODEL IP> with the IP address of your DigitalOcean Droplet.
2. Replace <REPLACE WITH YOUR BEARER_TOKEN> with your Bearer Token (available by logging into your Droplet).
3. Replace <YOUR UNIQUE DIGITALOCEAN BUCKET NAME> with the FQDN for your Spaces bucket.
4. Replace <REPLACE WITH A SESSION ABSTRACT FOR YOUR SLIDES> with the abstract of your presentation.

Example Output

After running the script, you'll get session notes for each slide. For example:

--- Generating summary for slide_004.png ---
Summary:
Slide 5 illustrates flawed data, which is a key challenge in AI/ML...

Maximize Efficiency with AI Slide Summaries

Vision Instruct provides an effective way to automate your slide summarization processes. By automating this you can create presentations much more efficiently and effectively.

FAQs About Vision Instruct AI

Below are some frequently asked questions regarding Vision Instruct AI.

1. What is the purpose of Vision Instruct models?

Vision Instruct models process and integrate both visual and textual data. Their primary purpose is to generate summaries, descriptions, or captions from images, making them powerful tools for image captioning, visual question answering, and image-text retrieval.

2. How do I convert a PDF presentation into individual slide images?

Use ImageMagick to convert PDFs into images. ImageMagick offers tools to convert PDF files into PNG, JPEG, and GIF formats. Refer to their documentation for conversion information.

3. What is the role of Hugging Face’s InferenceClient in this tutorial?

Hugging Face’s InferenceClient facilitates the interaction with remotely hosted Vision Instruct models. It automatically generates context-aware summaries for each slide, streamlining workflow integration.

4. How can I ensure my talking points align perfectly with my visual aids?

Vision Instruct models generate concise, context-aware summaries for each slide, which helps enhance presentation quality.

5. Can I use Vision Instruct models for other applications beyond slide summarization?

Yes, Vision Instruct models are suitable for image captioning, visual question answering, and image-text retrieval. They can generate alt-text for accessibility, and automate content tagging.

6. How do I deploy a Vision Instruct model on DigitalOcean?

DigitalOcean's one-click deploy option makes this possible in a few clicks. First, create a GPU Droplet. Then, select the Vision Instruct model from the DigitalOcean Marketplace.

7. What are the benefits of using Vision Instruct models for slide summarization?

Vision Instruct reduces manual effort and increases productivity while generating more accurate, context-aware summaries.