Internship Task

This repository contains the code and resources for an internship task focused on image processing and text extraction using machine learning models. Refer to the post and demonstration here

Folder Structure

The folder structure of the repository is as follows:

├── data
│   ├── input_images                # input images
│   ├── output                      # output results
│   └── segmented_objects           # segmented objects
├── internship_task.egg-info        # Package information
├── model_assets                    # Pre-trained model assets
│   ├── yolov8n.pt                 
│   └── yolov8s-seg.pt             
├── models                          # Model definitions
│   ├── identification_model.py     
│   ├── segmentation_model.py       
│   ├── summarization_model.py      
│   └── text_extraction_model.py    
├── README.md                       # Project documentation
├── requirements.txt                # Python dependencies   
├── setup.py                        # Setup script for the package
├── streamlit_app                   # Streamlit application
│   ├── app.py                      # Main application file
│   └── components                  # Components for Streamlit
├── tests                           # Unit tests
│   ├── test_identification.py      
│   ├── test_segmentation.py        
│   ├── test_summarization_model.py  
│   └── test_text_extraction.py     
└── utils                           # Utility scripts
    ├── data_mapping.py             
    ├── postprocessing.py           
    ├── preprocessing.py            
    └── visualization.py            

Installation

1. Clone the repository:

   git clone https://github.com/Lauel09/wasserstoff-AiInternTask.git
   
   cd wasserstoff-AiInternTask

2. Create a virtual environment and activate it:

   python3 -m venv venv
   source venv/bin/activate

3. Install the required dependencies:

   pip install -r requirements.txt

Usage

If you are running model files like:-

    python3 models/identification_model.py  # or any other model

Make sure to run from the root folder,i.e., wasserstoff-AiInternTask.

Not even the models, but also the tests and utils files should be run from the root folder, including the streamlit app as shown below.

Running the Streamlit App

To run the Streamlit application, use the following command:

    streamlit run streamlit_app/app.py

Streamlit app hosted on Huggingface Spaces: Wasserstoff Internship Task