dockerfile

Dockerfile

@@ -0,0 +1,23 @@
+# base image
+FROM ubuntu:latest
+
+# set environment variables
+ENV PYTHONUNBUFFERED=1
+
+# set working directory
+ENV DockerHOME=/home/app/TracEX
+RUN mkdir -p $DockerHOME 
+WORKDIR $DockerHOME
+
+# copy source files
+COPY . $DockerHOME 
+
+# expose port
+EXPOSE 8000
+
+# install dependencies
+RUN apt-get update && apt-get install -y python3 graphviz python3-pip
+RUN pip install --break-system-packages --no-cache-dir -r requirements.txt
+
+# start server  
+CMD ["python3", "tracex_project/manage.py", "runserver", "0.0.0.0:8000"]

README.md

@@ -1,16 +1,48 @@
 # TracEX
 
+[![GitHub stars](https://img.shields.io/github/stars/bptlab/TracEX)](https://github.com/bptlab/TracEX)
+[![GitHub open issues](https://img.shields.io/github/issues/bptlab/TracEX)](https://github.com/bptlab/TracEX/issues)
+[![GitHub closed pull requests](https://img.shields.io/github/issues-closed/bptlab/TracEX)](https://github.com/bptlab/TracEX/issues)
+[![GitHub open pull requests](https://img.shields.io/github/issues-pr/bptlab/TracEX)](https://github.com/bptlab/TracEX/issues)
 
-## Key Points
+TracEX aims to extract event logs from unstructured text, specifically written patient experiences known as patient journeys. By leveraging Large Language Models (LLMs), TracEX can automatically identify and extract relevant events, activities, timestamps and further information from natural langauge text. This enables healthcare professionals and researchers to gain valuable insights into patient experiences, treatment pathways, and potential areas for improvement in healthcare delivery.
 
-This bachelorproject focuses on event log extraction from patient journeys using large-language models.
+This project was initiated and completed as part of the team's bachelor's degree under the supervision of the Business Process Technology chair at the Hasso Plattner Institute. The project was conducted in cooperation with [mamahealth](https://www.mamahealth.com/).
 
-Our project partner is mamahealth. More information about them can be found here: [mamahealth](https://www.mamahealth.io/)
+## Key Features
+- **Extraction Pipeline**: A robust pipeline to clean, process, and extract data from natural language text.
+- **Patient Journey Generator**: Generates comprehensive patient journeys based on randomized cohort data.
+- **Database**: Stores patient journeys and related extraction results for easy access and analysis.
+- **Metrics and Evaluation Tool**: Evaluates the accuracy and effectiveness of the extraction process and allows for analysis of exctraction results.
+- **Intuitive UI**: User-friendly interface for you to interact with the tool and visualize results.
 
-More information about the project will be released soon.
+## Requirements
+To run TracEX successfully, it is essential to obtain an OpenAI API key with adequate credits. TracEX integrates the OpenAI API to leverage Large Language Models (LLMs) for extracting relevant information from unstructured text. Without a valid API key and sufficient balance, the extraction process cannot be performed.
 
+## Installation using Docker
+**Option 1: Using a Pre-built Docker Image** \
+The easiest way to run a local instance of TracEX is using the provided Docker image.
 
-## Set Up Guide
+1. Install Docker: Ensure that you have Docker installed on your system. If you haven't installed it yet, please follow the official Docker installation guide for your operating system: Docker Installation.
+1. Download the Latest Docker Image: Download the latest TracEX Docker image from the provided link: docker image
+1. Load the Docker Image: Open a terminal or command prompt and navigate to the directory where you downloaded the Docker image file. Run the following command to load the image: `docker load -i tracex.tar`\
+Note: Depending on your system configuration, you may need to run this command with sudo privileges.
+1. Run the Docker Container: After the image is successfully loaded, run the following command to start the TracEX container: `docker run -p 8000:8000 tracex`\
+This command will start the container and map port 8000 from the container to port 8000 on your local machine. Again, you may need to use sudo depending on your system setup.
+1. Access TracEX: Open a web browser and navigate to http://localhost:8000/. This will bring you to the TracEX application, where you can enter your OpenAI Key and start extracting event logs.
+
+**Option 2: Building the Docker Image from Source** \
+Alternatively, you can build the Docker image from the TracEX source code.
+
+1. Clone the TracEX Repository: Open a terminal or command prompt and navigate to the directory where you want to clone the TracEX repository. Run the following command to clone the repository: git clone https://github.com/bptlab/TracEX
+1. Navigate to the TracEX Directory: Change your current directory to the cloned TracEX repository: `cd TracEX`
+1. Build the Docker Image: Run the following command to build the TracEX Docker image: `docker build -t tracex .`\
+Note: Depending on your system configuration, you may need to run this command with sudo privileges.
+1. Run the Docker Container: After the image is successfully built, run the following command to start the TracEX container: `docker run -p 8000:8000 tracex`\
+This command will start the container and map port 8000 from the container to port 8000 on your local machine. Again, you may need to use sudo depending on your system setup.
+1. Access TracEX: Open a web browser and navigate to http://localhost:8000/. This will bring you to the TracEX application, where you can enter your OpenAI Key and start extracting event logs.
+
+## Local Setup for Development
 
 ### Download
 
@@ -19,16 +51,31 @@ More information about the project will be released soon.
 ### Installation
 - navigate to the root directory of TracEX in your terminal
 - run `install-dependencies-unix.sh` or `install-dependencies-windows.ps1`, based on your operating system _(Using e.g. Terminal)_
-- run `python tracex/manage.py migrate` to update the database and apply all changes stored in the `migrations` folder
+- run `python tracex_project/manage.py migrate` to update the database and apply all changes stored in the `migrations` folder
 
 ### Execution
-
-**Web-App:**
-- Run `python tracex/manage.py runserver` in the root directory of TracEX _(Using e.g. Terminal)_
-
-**Command-Line Tool:**
-- Run `python command_line_tool.py` in the root directory of TracEX _(Using e.g. Terminal)_
+- Run `python tracex_project/manage.py runserver` in the root directory of TracEX _(Using e.g. Terminal)_
 
 ### Pre-Commit
 
 - If you intend on expanding the code, please run `pre-commit install` in the root directory of TracEX _(Using e.g. Terminal)_
+
+## Contributors
+
+The main contributors to the project are the six members of the [2023/24 Bachelor Project](https://hpi.de/fileadmin/user_upload/hpi/dokumente/studiendokumente/bachelor/bachelorprojekte/2023_24/BA-Projekt_FG_Weske_Event_Log_Extraction_from_Patient_Experiences.pdf) of Professor Weske's [Business Process Technology Chair](https://bpt.hpi.uni-potsdam.de) at the [Hasso Plattner Institute](https://hpi.de):
+
+- [Pit Buttchereit](https://github.com/PitButtchereit)
+- [Frederic Rupprecht](https://github.com/FR-SON)
+- [VanThang Nguyen](https://github.com/thangixd)
+- [Nils Schmitt](https://github.com/nils-schmitt)
+- [Soeren Schubert](https://github.com/soeren227)
+- [Trung-Kien Vu](https://github.com/tkv29)
+
+These six participants will push the project forward as part of their bachelor's degree until the summer of 2024.  
+At the same time our commitment to open source means that we are enabling -in fact encouraging- all interested parties to contribute and become part of its developer community. 
+
+## Project documentation
+
+In the project wiki, you can find detailed documentation that covers various aspects of TracEX.
+In the [architecture](https://github.com/bptlab/TracEX/wiki/Architecture) section, we provide an overview of the system's design and components. The [repository structure](https://github.com/bptlab/TracEX/wiki/Repository-Structure) is also outlined, making it easier for you to navigate and understand the organization of our codebase.
+Most importantly, we have dedicated a significant portion of the wiki to explaining our [pipeline frameworks](https://github.com/bptlab/TracEX/wiki/Pipelines), which are the core of TracEX. These frameworks are responsible for processing and transforming the unstructured patient journey data into structured event logs.

requirements.txt

@@ -7,3 +7,4 @@ pandas~=2.1.3
 numpy~=1.26.2
 jinja2~=3.1.4
 regex~=2024.5.15
+requests~=2.31.0