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توضیحات

Code What-If-Tool

ویژگی	مقدار
سیستم عامل	-
نام فایل	codewit-semeru-0.1.4
نام	codewit-semeru
نسخه کتابخانه	0.1.4
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	-
ایمیل نویسنده	Ignat Miagkov <iamiagkov@wm.edu>, Duke Tran <dtran@wm.edu>, Eli Svoboda <essvoboda@wm.edu>
آدرس صفحه اصلی	-
آدرس اینترنتی	https://pypi.org/project/codewit-semeru/
مجوز	-

# What-If-Code-Tool ## Visualization Tool for Code Generation Model Analysis ![demo-gif](Artifacts/codewit_demo.gif) ## Main Idea [Google WIT](https://github.com/PAIR-code/what-if-tool) was the main inspiration for this project. Our goal is to create a similar tool purely for focusing on ML models revolving around software engineering design and principles, such as code completion and code generation. [BertViz](https://github.com/jessevig/bertviz) is a good first example for where our tool will go. We hope to support a full dashboard of several views that researchers would find helpful in order to analyze their models. This would probably include newly generated word count charts, probability distributions for new tokens, and attention views. ## Development - Pip tool: user can install this tool from pip/conda and utilize with their NLP model - Python Backend: user designates dataset and model as parameters for our tool. Our tool then runs the model and produces some vector dataset in its object. - Jupyter-Dash Frontend: Jupyter-Dash allows for easy creation for data dashboard. Provides ability for easy callback methods with just Python.  ## Development Plans  - [x] Code concept groupings view: categorize each of the tokens generated in output based on what type they are in code language (declaration, assignment, functions, etc.) - [x] Display some statistics about the generated output with specific model (median, max, min, etc.) - [x] Dynamics re-execution of pipeline when: - [ ] User edits # of tokens - [ ] User edits # of input sequences - [x] User changes model - [x] User selects new descriptive statistic - [ ] Implement bertviz attention models inside app with Dash if possible ## Current Diagrams ### Components UML ![Components](Artifacts/component-diagram-updated.png) ### Sequence Diagram ![Sequence Diagram](Artifacts/sequence-diagram-updated.png) ## Supported Features - [ ] 4 different views to visually classify code generation models (ind. token, token distrubtion, python token types, token type distribtuion) - [ ] 4 pre-trained models for code generation from Hugging Face (GPT2, CodeGen, CodeParrot, GPT-Neo) - [ ] Descriptive stats for datasets with many input sequences - [ ] Dynamic re-execution on user inputs ## Installation First prototype is currently available on PyPi. User will need to generate their own Hugging Face API token. ``` %pip install codewit-semeru %load_ext autoreload %autoreload 2 ``` ``` %pip install datasets from datasets.load import load_dataset import pandas as pd DATA_LEN = 1024 NUM_DATA = 20 dataset = load_dataset("code_x_glue_cc_code_completion_line", "python", split="train") pruned_dataset = [] for i, input_seq in enumerate(dataset): temp = input_seq["input"] # type: ignore if len(temp) <= DATA_LEN: pruned_dataset.append(temp) if len(pruned_dataset) >= NUM_DATA: break pd.DataFrame(pruned_dataset).describe() ``` ``` import os os.environ["HF_API_TOKEN"] = "{Insert token here}" from codewit_semeru import WITCode WITCode("codeparrot/codeparrot-small", pruned_dataset) ``` These lines can be run directly from your notebook. Python 3.8 is required. First chunk installs pip module, load auto-reload function. Second chunk loads up the CodeXGlue Code Completion dataset to be utilized with our tool. The last block is the actual implementaion in notebook to run our tool. User needs to supply their own api token to query HF models. ### Build and Run Docker Image Start docker Navigate to project folder and run ```docker-compose up -d --build``` to build image Navigate to ```localhost:8888``` to run jupyter notebook. password is ```wit``` To stop docker container run ```docker-compose down``` ### Build and Run Docker Image Start docker Navigate to project folder and run ```docker-compose up -d --build``` to build image Navigate to ```localhost:8888``` to run jupyter notebook. password is ```wit``` To stop docker container run ```docker-compose down```

نیازمندی

مقدار	نام
-	jupyter-dash
-	pandas
-	python-dotenv
-	torch
-	transformers

زبان مورد نیاز

مقدار	نام
>=3.8	Python

نحوه نصب

نصب پکیج whl codewit-semeru-0.1.4:

pip install codewit-semeru-0.1.4.whl

نصب پکیج tar.gz codewit-semeru-0.1.4:

pip install codewit-semeru-0.1.4.tar.gz