LLM -> JSON (using Pydantic)

This is a simple Streamlit app that uses a Google Gemini Flash Multimodal LLM to extract text from a receipt image and outputs the result as a structured JSON format using Pydantic.

Features

• Upload a receipt image (JPG, JPEG, PNG).
• Extract readable text from the image using Google Gemini Flash Multimodal LLM.
• Present the extracted content in a structured JSON format.
• Use Pydantic models to ensure the output is clean and accurate.
• Easy-to-use Streamlit interface for interacting with the app.

Setup Instructions

1. Clone the repository

git clone https://github.com/stackmodel/llm-pydantic-json.git
cd llm-pydantic-json

2. install Dependencies:

   • Make sure you have Python 3.7 or higher installed. Then, create a virtual environment and install the dependencies:

     python -m venv env
     source env/bin/activate  # For Linux/macOS
     .\env\Scripts\activate   # For Windows
     pip install -r requirements.txt
     

3. Rename .env.example to .env file and populate the google gemini api key. You can obtain your API key from Google AI Studio.

4. Run the app using the following command: streamlit run app.py This will launch the app in your browser. Upload the sample.png file.