Image Processing Using Llama 3.2 with Hugging Face Transformers

Introduction

Extracting insights from images has long been a challenge across industries like finance, healthcare, and law. Traditional methods, such as Optical Character Recognition (OCR), have struggled with complex layouts and contextual understanding.

Llama 3.2 Vision, an advanced AI model, enhances image processing capabilities like Visual Question Answering and OCR. By integrating this model with DigitalOcean’s cloud infrastructure, this tutorial provides a scalable and efficient way to implement AI-powered image processing.

In this tutorial, you will learn to set up Llama 3.2 Vision with DigitalOcean’s cloud infrastructure, and demonstrate how to use it for AI-powered image processing for extracting employee IDs and names from images. We will cover the installation and configuration steps, as well as provide examples of how to use the model for Visual Question Answering and OCR. By the end of this tutorial, you will have a solid understanding of how to leverage Llama 3.2 Vision for your image processing needs.

Before proceeding, ensure you have:

SSH into Your GPU Droplet

Connect to your server via SSH:

ssh root@your_server_ip

Install Python & Create a Virtual Environment

Run the following commands to set up a Python virtual environment:

apt install python3.10-venv -y python3.10 -m venv llama-env

Activate the Virtual Environment

source llama-env/bin/activate

Install PyTorch & Hugging Face CLI

pip install torch torchvision torchaudio pip install -U “huggingface_hub[cli]” huggingface-cli login

Install the Transformers Library

pip install –upgrade transformers

Install Flask & AWS SDK (Boto3)

Boto3 is required to interact with DigitalOcean Spaces, which is S3-compatible.

pip install flask boto3

Install MySQL Connector for Python

pip install mysql-connector-python

Install Nginx to serve your Flask application:

sudo apt install nginx -y

Application Folder Structure

Organize your project as follows:

llama-webapp/ ├── app.py ├── static/ │ └── styles.css └── templates/ └── index.html

Python Code for the Application

Below is the Flask app (app.py) that loads the Llama 3.2 model, processes uploaded images, and extracts employee details.

import os import json import requests from PIL import Image from flask import Flask, request, render_template, session from transformers import MllamaForConditionalGeneration, AutoProcessor import boto3 import torch import mysql.connector import re app = Flask(__name__) app.secret_key = “your_secret_key” model_id = “meta-llama/Llama-3.2-11B-Vision-Instruct” model = MllamaForConditionalGeneration.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map=”auto” ) processor = AutoProcessor.from_pretrained(model_id) SPACE_NAME = “your_space_name” SPACE_REGION = “your_region” ACCESS_KEY = “your_access_key” SECRET_KEY = “your_secret_key” s3 = boto3.client( “s3″, region_name=SPACE_REGION, endpoint_url=f”https://{SPACE_REGION}.digitaloceanspaces.com”, aws_access_key_id=ACCESS_KEY, aws_secret_access_key=SECRET_KEY ) DB_HOST = “your_mysql_host” DB_PORT = 25060 DB_NAME = “your_database_name” DB_USER = “your_username” DB_PASSWORD = “your_password” def get_db_connection(): try: conn = mysql.connector.connect( host=DB_HOST, port=DB_PORT, database=DB_NAME, user=DB_USER, password=DB_PASSWORD ) print(” Database connection successful!”) return conn except Exception as e: print(f” Error connecting to the database: {e}”) return None def extract_employee_details(image_path): “””Extracts Employee Name and ID using Llama 3.2 Vision AI.””” try: image = Image.open(image_path) prompt = ( “Extract the Employee ID and Name from the given image. ” “Provide output in valid JSON format with keys: ’employee_id’ and ’employee_name’.” ) messages = [{“role”: “user”, “content”: [{“type”: “image”}, {“type”: “text”, “text”: prompt}]}] input_text = processor.apply_chat_template(messages, add_generation_prompt=True) inputs = processor(image, input_text, return_tensors=”pt”).to(model.device) output = model.generate(**inputs, max_new_tokens=1024) raw_result = processor.decode(output[0]) json_match = re.search(r”{.*?}”, raw_result, re.DOTALL) extracted_data = json.loads(json_match.group(0)) if json_match else {} employee_id = extracted_data.get(“employee_id”, “”).strip() employee_name = extracted_data.get(“employee_name”, “”).strip() return employee_id, employee_name except Exception as e: return None, None @app.route(“/”, methods=[“GET”, “POST”]) def index(): “””Handles image uploads, extracts Employee Name & ID, and stores it in MySQL.””” result = None image_url = session.get(“image_url”) if request.method == “POST”: image_file = request.files.get(“image”) if not image_file: return “Error: Please upload an image.”, 400 filename = image_file.filename image_path = os.path.join(“/tmp”, filename) image_file.save(image_path) employee_id, employee_name = extract_employee_details(image_path) return render_template(“index.html”, result=result, image_url=image_url) if __name__ == “__main__”: app.run(host=”0.0.0.0″, port=5000)

1. Start the Flask application:

   python app.py

2. Open your browser and visit:

   http://your_server_ip:5000

3. Upload an image, extract employee details, and verify data storage in the database.

1. What is Llama 3.2, and how does it differ from previous versions?

Llama 3.2 is a state-of-the-art AI model developed by Meta (Facebook) that builds upon its predecessor, Llama 3. It offers improved natural language understanding, better performance in multimodal tasks (including image processing), and enhanced efficiency when integrated with Hugging Face Transformers.

2. Can Llama 3.2 process images directly?

Yes, Llama 3.2 introduces vision models (11B and 90B) that enable it to process and understand images directly, allowing for tasks like image captioning, object recognition, and scene interpretation

3. What are some common use cases of Llama 3.2 in image processing?

Llama 3.2 can assist in image processing tasks such as:

• Image Captioning: Generating descriptive text from images.
• Object Recognition: Identifying objects within an image when combined with a vision model.
• Text Extraction (OCR): Helping interpret extracted text from an image.
• Style Transfer & Image Editing: Assisting in AI-powered image generation and modification.

4. How do I set up Hugging Face Transformers to work with Llama 3.2?

You can install the required libraries and load the model using the following steps:

pip install transformers torch torchvision

Then, load the model with:

from transformers import AutoModelForCausalLM, AutoTokenizer model_name = “meta-llama/llama-3-2” tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name)

If working with images, you may also need transformers’s vision models like CLIP:

from transformers import CLIPProcessor, CLIPModel clip_model = CLIPModel.from_pretrained(“openai/clip-vit-base-patch32”) processor = CLIPProcessor.from_pretrained(“openai/clip-vit-base-patch32”)

5. How can I use Llama 3.2 for image captioning?

Llama 3.2’s vision model can generate high-quality captions for images:

from transformers import AutoProcessor, AutoModelForVision2Seq from PIL import Image model_name = “meta-llama/llama-3.2-vision” processor = AutoProcessor.from_pretrained(model_name) model = AutoModelForVision2Seq.from_pretrained(model_name) image = Image.open(“your_image.jpg”) inputs = processor(images=image, return_tensors=”pt”) output = model.generate(**inputs) caption = processor.batch_decode(output, skip_special_tokens=True)[0] print(“Generated Caption:”, caption)

6. Can I fine-tune Llama 3.2 Vision on my own dataset?

Yes, you can fine-tune Llama 3.2 Vision using Hugging Face’s transformers library with LoRA (Low-Rank Adaptation).

Example fine-tuning setup:

from peft import LoraConfig, get_peft_model config = LoraConfig( r=8, lora_alpha=32, target_modules=[“q_proj”, “v_proj”] ) fine_tuned_model = get_peft_model(model, config)

This allows efficient fine-tuning without retraining the entire model.

In this tutorial, you learned how to extract employee IDs and names from images using the Llama 3.2 Vision model. We integrated DigitalOcean Spaces for storing images and used a managed MySQL database for structured data storage. This solution provides an automated way to process and manage employee verification data with AI-powered efficiency.