The Invoice Extractor project is designed to streamline the process of extracting crucial information from images, specifically focusing on invoices. Leveraging the capabilities of Gemini 1.5 and Google's Multimodal LLM, this application offers a robust solution for parsing invoice data with precision and efficiency. It serves as a complement to the concepts discussed in the accompanying YouTube video, offering a practical implementation of the discussed techniques.
The Invoice Extractor application is built with Python, utilizing Streamlit for the frontend interface. It offers a user-friendly experience for extracting and analyzing invoice data from images. With a combination of a multimodal LLM and intuitive design, users can easily upload images, extract relevant information, and visualize the results in a clear and concise manner.
To utilize the Invoice Extractor application, follow these steps:
- Clone the repository:
git clone https://github.com/Eduardovasquezn/invoice-extractor.git - Navigate to the project directory:
cd invoice-extractor - Create a virtual environment:
python -m venv venv venv/Scripts/activate - Install dependencies:
pip install -r requirements.txt - Set up environment variables: Create a
.envfile based on the.env.exampletemplate and fill in the required values. - Run the application: Execute
streamlit run src/app.pyin your terminal.
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This project is licensed under the MIT License, granting users the freedom to modify and distribute the software as per their requirements.