pip install -r requirements.txt If requirements.txt is missing, install manually: pip install streamlit onnxruntime pillow matplotlib numpy 🚀 How to Run the App Clone the Repository git clone https://github.com/tomferdi/Handwritten-math-equation-conversion cd Handwritten-math-equation-conversion (Optional) Download the ONNX Model To enable prediction, you need a file named model.onnx. This file is not included in the repository. If available, place it in the root directory of the project (same folder as app.py). If you don't include model.onnx, the app will still open but cannot perform prediction.  Add Input Images Place handwritten math equation images inside the test_images/ folder.  You can edit the file path inside app.py if you want to change which image is loaded:   img_path = "test_images/your_image.png" Note: The model expects grayscale images. The exact image dimensions required by the model are not documented, but using a square image (like 256x256 pixels) is a safe choice.  Run the Streamlit App  streamlit run app.py