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GPT3-wellbeingAssist

Main Problems:

Gain information about patients health status in between doctor's visits Put it in a structured format

Outcome:

Improve clinical trials effectiveness and patients well being

Given the patient’s answer….: Did it fulfill our question? [Yes, No] Do we need to ask a follow-up question? [Yes, No] What do we need to ask more specifically about? What are his symptoms and when did they occur? How can we categorize his answer (structure the data)?

Open Issues:

Legal aspect?

NEW INSTRUCTIONS

Setup

  • pip install pysqlite3

  • Add your OpenAI key to gpt3_config.yml in this format:

GPT3_API: ab-XXXXXXXXXXXXXXXXXXXXXXXX
  • Or you can add it via the streamlit app directly.
  • Install poetry. Follow the official site or this cookbook
  • Once poetry is installed, run poetry install. This will download all the packages needed (ideally in .venv) as well as setup the repository.
  • To run migrations: poetry run migrate

Running the app

  • To run the streamlit app: poetry run st-server or bash run.sh
  • You will now be able to view the application @ localhost:8000

Adding new primes/dataset

  • Create a *.yml or *.yaml file in datasets, this file would be available in the streamlit app in the dropdown

Running

[x] only works on streamlit run home.py when you cd into the app folder [ ] getting the script to work by just running bash script run.sh

If you use this project, do help out: Follow and Star the project

Screenshot

program running on experimental version

## ======================================
## DEPRECIATED
## ======================================
# Environment - Conda python3
conda env create --name envname --file=environment_droplet.yml
conda activate envname

# Install Openai and Flask 
pip install openai
pip install python-dotenv
pip install Flask
pip install fpdf

# Deactivate Environment
conda deactivate

# To run Flask
$ export FLASK_APP=backend_functions.py 

$ flask run

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