Welcome to PuterBot: GUI Process Automation with Transformers! We are working with a dataset of user input events, screenshots, and window events. Our task is to generate the appropriate InputEvent(s) based on the previously recorded InputEvents and associated Screenshots, such that the task in the recording is accomplished, while accounting for differences in screen resolution, window size, application behavior, etc.

Given a new Screenshot, we want to generate the appropriate InputEvent(s) based on the previously recorded InputEvents, where each Screenshot is taken immediately before its associated InputEvent. We need to account for differences in screen resolution, window size, application behavior, etc. InputEvents contain raw mouse and keyboard data which have been aggregated to remove unnecessary events.

The dataset consists of the following entities:

1. Recording: Contains information about the screen dimensions, platform, and other metadata.
2. InputEvent: Represents a user input event such as a mouse click or key press. Each InputEvent has an associated Screenshot taken immediately before the event.
3. Screenshot: Contains the PNG data of a screenshot taken during the recording.
4. WindowEvent: Represents a window event such as a change in window title, position, or size.

You can assume that you have access to the following functions:

• get_recording(): Gets the latest recording.
• get_events(recording): Returns a list of InputEvent objects for the given recording.

1. Fork this repository and clone it to your local machine.
2. Get puterbot up and running by following the instructions in puterbot/README.md
3. Implement a Python function generate_input_event(new_screenshot, recording), where:

• new_screenshot: A Screenshot object representing the new screenshot.
• recording: A Recording whose .screenshots property is a list of InputEvent objects from a previous recording, with each InputEvent having an associated Screenshot.

This function should return a new InputEvent object that can be used to replay the recording, taking into account differences in screen resolution, window size, and application behavior.

4. Integrate the Segment Anything library, HuggingFace GPT-J (or a similar transformer model), and PaddleOCR to assist in processing the screenshots and improving the generation of new InputEvents. These tools will help you determine the properties of the next InputEvent by segmenting the objects in the screenshots, generating textual prompts for GPT-J, and extracting text information from the images, respectively. Follow the installation instructions provided in their READMEs to set up the libraries in your environment.

5. Write unit tests for your implementation.

Your submission will be evaluated based on the following criteria:

1. Functionality : Your implementation should correctly generate the new InputEvent objects based on the provided data and the Segment Anything, GPT-J, and/or PaddleOCR libraries.

2. Code Quality : Your code should be well-structured, clean, and easy to understand.

3. Scalability : Your solution should be efficient and scale well with large datasets.

4. Testing : Your tests should cover various edge cases and scenarios to ensure the correctness of your implementation.

1. Commit your changes to your forked repository.

2. Create a pull request to the original repository with your changes.

3. In your pull request, include a brief summary of your approach, any assumptions you made, and how you integrated the SegmentAnything, GPT-J, and PaddleOCR libraries.

4. Bonus: interacting with ChatGPT and/or other language transformer models in order to generate code and/or evaluate design decisions is encouraged. If you choose to do so, please include the full transcript.

Here are some stubs and suggestions to help you get started with your implementation:

1. Set up your Python environment and install the required libraries (Segment Anything, HuggingFace Transformers, and PaddleOCR).

2. Create a new file, gui_process_automation.py, and import the necessary libraries:

from segment_anything import SamPredictor, sam_model_registry
from transformers import GPTJForCausalLM, GPT2Tokenizer
from paddleocr import PaddleOCR

3. Initialize the models:

# Initialize Segment Anything model
sam = sam_model_registry["<model_type>"](checkpoint="<path/to/checkpoint>")
sam_predictor = SamPredictor(sam)

# Initialize GPT-J model
tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-j-6B")
model = GPTJForCausalLM.from_pretrained("EleutherAI/gpt-j-6B")

# Initialize PaddleOCR model
ocr = PaddleOCR()

4. Create the generate_input_event function:

def generate_input_event(new_screenshot, recording):
    # TODO: Implement the function to generate a new InputEvent based on the new Screenshot and the previous Recording
    pass

5. In the generate_input_event function, you may want to follow these steps:

a. Use the Segment Anything library to segment the objects in the new and previous screenshots.

b. Use the PaddleOCR library to extract text information from the new and previous screenshots.

c. Generate textual prompts based on the segmented objects and extracted text, and use the GPT-J model to predict the next InputEvent properties.

d. Create a new InputEvent object based on the predicted properties and return it.

e. Bonus: Use the HuggingFace transformers library to extract features from Screenshots and InputEvents and generate InputEvent replay sequences directly (end-to-end).

6. Write unit tests for your implementation in a separate file, test_gui_process_automation.py.

Once you have implemented the generate_input_event function and written unit tests, commit your changes to your forked repository, create a pull request, and provide a brief summary of your approach, assumptions, and library integrations.

We hope that these stubs and suggestions will help you get started with your implementation. Good luck!

Please submit any issues to https://github.com/MLDSAI/puterbot/issues with the following information:

• Problem description (please include any relevant console output and/or screenshots)
• Steps to reproduce (please help others to help you!)