Update README.md

README.md

@@ -1,6 +1,6 @@
 ---
 title: Qstn Gui
-emoji: 🚀
+emoji: 💻
 colorFrom: red
 colorTo: red
 sdk: docker
@@ -12,6 +12,73 @@ short_description: GUI for the QSTN Framework
 license: mit
 ---
 
-## QSTN GUI
+# QSTN GUI
 
-This is the GUI for the Question Framework.
+This is the GUI for the QSTN Framework.
+
+# QSTN: A Modular Framework for Robust Questionnaire Inference with Large Language Models
+    
+<div align="center">
+
+![Overview](overview.svg)
+
+</div>
+
+  
+
+QSTN is a Python framework designed to facilitate the creation of robust inference experiments with Large Language Models based around questionnaires. It provides a full pipeline from perturbation of prompts, to choosing Response Generation Methods, inferencing and finally parsing of the output. QSTN supports both local inference with vllm and remote inference via the OpenAI API.
+
+Detailed information and guides are available in our [documentation](https://qstn.readthedocs.io/en/latest/). Tutorial notebooks can also be found in this [repository](https://github.com/dess-mannheim/QSTN/tree/main/docs/guides).
+
+## Installation
+
+To install the project and dependencies you can use `pip`.
+
+```bash
+pip install qstn
+```
+
+Or install this package from source:
+
+```bash
+pip install git+https://github.com/dess-mannheim/QSTN.git
+```
+
+## Getting Started
+
+Below you can find a minimum working example of how to use QSTN. It can be easily integrated into existing projects, requiring just three function calls to operate. Users familiar with vllm or the OpenAI API can use the same Model/Client calls and arguments. In this example reasoning and the generated response are automatically parsed. For more elaborate examples, see the [tutorial notebooks](https://github.com/dess-mannheim/QSTN/tree/main/docs/guides).
+
+```python
+import qstn
+import pandas as pd
+from vllm import LLM
+
+# 1. Prepare questionnaire and persona data
+questionnaires = pd.read_csv("hf://datasets/qstn/ex/q.csv")
+personas = pd.read_csv("hf://datasets/qstn/ex/p.csv")
+prompt = (
+    f"Please tell us how you feel about:\n"
+    f"{qstn.utilities.placeholder.PROMPT_QUESTIONS}"
+)
+interviews = [
+    qstn.prompt_builder.LLMPrompt(
+        questionnaire_source=questionnaires,
+        system_prompt=persona,
+        prompt=prompt,
+    ) for persona in personas.system_prompt]
+
+# 2. Run Inference
+model = LLM("Qwen/Qwen3-4B", max_model_len=5000)
+results = qstn.survey_manager.conduct_survey_single_item(
+    model, interviews, max_tokens=500
+)
+
+# 3. Parse Results
+parsed_results = qstn.parser.raw_responses(results)
+```
+
+## Citation
+
+Authors: Maximilian Kreutner, Jens Rupprecht, Georg Ahnert, Ahmed Salem, and Markus Strohmaier
+
+This package will soon have a arxiv paper.