CMU Interactive Data Science Final Project

• Online URL: Too large to deploy, please see instrunction below for local deployment
• Team members:
  • Contact person: jingyua4@andrew.cmu.edu
  • chuhanf@andrew.cmu.edu
  • jiechenx@andrew.cmu.edu
• Track: Model

Work distribution

Update towards the end of the project.

Jingyuan Li

• streamlit framework construction
• algorithm design, model investigation
• presentation introduction
• introduction, related works sections in report

Chuhan Feng

• model testing, selection
• model tuning
• model introduction in presentation
• report model section in report

Jiecheng Xu

• model integration
• application deployment
• demo video
• results and discussion, conclusion sections in report

Deliverables

Proposal

• The URL at the top of this readme needs to point to your application online. It should also list the names of the team members.
• A completed proposal. The contact should submit it as a PDF on Canvas.

Design review

• Develop a prototype of your project.
• Create a 5 minute video to demonstrate your project and lists any question you have for the course staff. The contact should submit the video on Canvas.

Final deliverables

• All code for the project should be in the repo.
• A 5 minute video demonstration.
• Update Readme according to Canvas instructions.
• A detailed project report. The contact should submit the video and report as a PDF on Canvas.

Deployment Instruction

• Please clone the entire github repo to your local repository, all the dependencies are recorded in the requirement.txt (NOT gector.requirement.txt).
• Please make sure you are using python 3.7 (python 3.8 will likely raise errors on the code) and correctly installed all the required dependencies using pip3 install -r requirement.txt
• In order for the model to load, please download the check point directly using this link and place the downloaded checkpoint file in the main project directory.
• The downloading chould take a while, since the model check point is over 500 Mb.
• Nevigate to the project directory, run streamlit run streamlit_app.py to open the steamlit application. Please Note: The first time running the application would take a while since the models will need to download nlp corpus and package spicific data. The downloading process can be tracked using consoles.