multilingual Invoice Parsing with LLaMA 4, OCR, and Python

Combining OCR for text extraction with LLMs for accurate, efficient document structuring.

🔍 Project Overview

This project demonstrates how Meta's LLaMA 4 is revolutionizing OCR and document parsing with its advanced multimodal and multilingual capabilities. By utilizing LLaMA 4, we explore its ability to extract structured data from real-world invoices, validate the results using Pydantic, and build an intuitive Streamlit app for user interaction.

The model excels in parsing invoices in multiple languages, including English, French, and Arabic, ensuring accurate and reliable outputs. This project showcases LLaMA 4 as a powerful tool for intelligent document processing, paving the way for smarter, AI-driven automation in various industries.

👀 Curious how it works? Dive into the full story here: https://medium.com/towards-artificial-intelligence/multilingual-invoice-parsing-project-with-llama-4-ocr-and-python-4649a62ba2dc

Invoice Parsing Results

Below are the results from parsing three invoices:

English Invoice

French Invoice

Arabic Invoice

🎯 Key Features

• Automated Invoice Parsing with LLaMA 4: Leverage LLaMA 4’s advanced multimodal capabilities to automate and enhance the invoice parsing process, extracting structured data efficiently.

• Structured Data Validation with Pydantic: Use Pydantic’s BaseModel to refine, validate, and ensure the output from LLaMA 4 is clean, structured, and reliable for further processing.

• Multilingual OCR Parsing: Unlock LLaMA 4’s versatility by parsing invoices in multiple languages, including English, French, and Arabic, demonstrating its robust multilingual understanding.

• Interactive Streamlit App: Build an intuitive, interactive Streamlit app for invoice parsing and deploy it to the cloud, providing a seamless user experience for document processing.

🚀 Getting Started

Prerequisites

• Python 3.11 or above 🐍
• Groq API for inference, which is currently available for free in its beta version with rate limits. You can obtain your API key here after creating an account: Groq API.

💻 Local Deployment

1. Clone the Repository

git clone https://github.com/Mouez-Yazidi/Multilingual-Invoice-Parsing-with-LLaMA-4.git
cd Multilingual-Invoice-Parsing-with-LLaMA-4

2. Add Environment Variables

• Create a .env file and add the following variables according to the credentials you obtained from the required platforms:

  GROQ_API_KEY=
  

3. Install Dependencies

Navigate to the local directory and install the necessary dependencies:

pip install -r requirements.txt

4. Running the App Locally

To run the app locally, execute the following command:

streamlit run app.py --environment local

You should now be able to access the app at http://localhost:8501 🌐.

🐳 Optional: Running with Docker

If you prefer running the app in a Docker container, follow these steps:

1. Make sure you have Docker installed 🐋.
2. Build the Docker image:

docker build -t InvoiceParsing -f Dockerfile  ..

3. Run the container:

docker run -p 8501:8501 InvoiceParsing streamlit run app.py --environment local

☁️ Streamlit Cloud Deployment

1. Prepare Your Repository

Ensure that your code is pushed to a GitHub repository 📂.

2. Link with Streamlit Cloud

• Visit Streamlit Cloud and sign in.
• Connect your GitHub repository 🔗.
• Choose your repository and branch.

3. Environment Variables

• Go to the "Advanced settings" section of your app.
• In the "Secrets" section, input any sensitive information, such as API keys or other credentials.
• Make sure to add this variables according to the credentials you obtained from the required platforms.

GROQ_API_KEY=""

Streamlit Cloud will:

• Install dependencies from cloud/requirements.txt 📦

🎉 You’re all set! Your app will now be live on Streamlit Cloud!