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This project utilizes Streamlit and Gemini to create a web application for extracting information from invoice images.

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Vision Pro

Description

This project utilizes Streamlit and Google's Generative AI to create a web application for extracting information from invoice images.

Installation

Before running the application, make sure you have the necessary dependencies installed. You can install them using the following command:

poetry install

This will install all the required dependencies specified in the pyproject.toml file.

Usage

To run the application, execute the following command:

poetry run streamlit run app.py 

This will start a local server where you can access the ML Invoice Extractor app in your web browser.

Dependencies

  • dotenv: For loading environment variables from a .env file.
  • streamlit: A Python library for creating interactive web apps.
  • PIL: The Python Imaging Library, used for working with images.
  • google.generativeai: Google's Generative AI library for generating content.

Configuration

Ensure that you have set up your environment variables correctly, particularly the GOOGLE_API_KEY, which is required for accessing Google's Generative AI models.

Functionality

  1. Vision Pro: Allows users to input prompts and upload images of invoices.
  2. Image Processing: Parses the uploaded image to extract relevant information.
  3. Response Generation: Utilizes Google's Generative AI to generate a response based on the input prompt and uploaded image.

Contributing

Contributions are welcome! Feel free to open an issue or submit a pull request to suggest improvements or report bugs.

About

This project utilizes Streamlit and Gemini to create a web application for extracting information from invoice images.

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