Update README.md

README.md

@@ -1,16 +1,31 @@
 # Performancer and AnnoXplorer
+This repository contains the code for the Performancer and AnnoXplorer tools that are part of the Interactive Text Mining Environment of the EU-Project InTaVia. See public Deliverable 4.7 for a detailed description of the tools.
 
-## setup
-- install node modules via npm (package.json): in ./frontend/static: npm install
-- install python packages via pip-tools  (requirements.in):
-    - install pip-tools
-    - in ./backend: pip-compile, pip-sync
-- generate test data in ./backend/preprocessing: 
-    - py preprocessing.py doc
-    - py preprocessing.py sent
-- run webpack: in ./frontend/static: npm run build
-- run flask server (./backend/app.py)
-- brushing on bars in Performancer opens AnnoXplorer with the selected texts
+The prototype consists of a JavaScript frontend and a Flask backend. To run it locally, an installation of Node.js, npm, and Python (version 3.10 or greater) is required.
 
-## documentation
-see deliverable 4.2
+The Performancer interface is the entry point into the application to allows for distant reading and comparison of annotated text corpora and NLP pipelines. When a number of data points in one of the charts is brushed, the AnnoXplorer interface is opened in a new tab, giving the possibility for close reading and detailed analysis of the brushed annotations and texts.
+
+The first step of building and running the prototype is installing all necessary npm and pip packages for the project. To build the frontend, navigate to the _./frontend/static_ directory with a terminal or command line tool.
+
+- To install the npm packages for the frontend, run the following command:
+  `npm install`
+
+- After the successful installation, build the frontend application by running the following command:
+
+  `npm run build`
+
+- To install all necessary pip packages for the backend and start a Flask development server, navigate to the ./backend folder and run the following command:
+
+  `pip install -r requirements.txt`
+
+- Next, to generate the test data for the prototype, navigate to the ./backend/preprocessing folder and run the following two commands:
+
+  `py preprocessing.py doc`
+
+  `py preprocessing.py sent`
+
+- Finally, to run the prototype navigate to the ./backend folder and start a Flask development server with the following command:
+
+  `flask run`
+
+While the development server is running, the application can be accessed locally at _localhost:5000_.