From Text to Insights: Building an OCR App with Llama-3.2-Vision

Transform Images into Structured Markdown Using Llama-3.2 Multimodal

With this app, you can upload an image and seamlessly convert it into a well-structured markdown document, leveraging the powerful capabilities of the Llama-3.2 Multimodal Model.

Key Tools:

• Ollama: Run Llama-3.2 Vision locally for efficient processing.
• Streamlit: Build an intuitive and interactive user interface for smooth user interaction.

The entire code is available here: (https://github.com/martinkhristi/llama-ocr.git)

Now, let’s look at the code for our Llama-OC

Step 1: Get Started with Ollama

Ollama lets you run large language models (LLMs) locally, giving you full control over your data and how the models are used.

• Visit Ollama.com, choose your operating system, and follow the installation guide.

Step 2: Set Up Llama-3.2 Vision

Llama-3.2 Vision is a powerful multimodal model designed for tasks like visual recognition, image reasoning, captioning, and answering image-based questions.

• Download the model using the provided instructions.

ollama run llama3.2-vision        

Step 3: Install the Ollama Python Package

Next, you'll need to install the Python package for Ollama. This will enable seamless integration with your code.

• Use the following command to install the package:

pip install ollama        

Step 4: Use Llama-3.2 Vision in Your Code

You're all set!

Now, you can prompt Llama-3.2 Vision using Ollama with a simple snippet of code like this:

import ollama

response = ollama.chat(
    model='llama3.2-vision',
    messages=[{'role': 'user',
               'content': """
Extract all text from the uploaded image and convert it into a well-structured Markdown format.
Focus on maintaining readability and organization, using headings, bullet points, and code blocks wherever necessary to enhance clarity.
Ensure the content is accurate, concise, and adheres to Markdown standards."""}],
    images=[image_path]
)

print(response.message.content)        

All Set!

While this snippet is just the beginning, the complete Streamlit app is concise and straightforward, requiring only about 50 lines of code to bring everything together seamlessly!

this is post is inspired by Daily does of Data Science newsletter .

The entire code (along with the code for Streamlit) is available here:

(https://github.com/martinkhristi/llama-ocr.git)

that's wrap for today!