update

app.py

@@ -1,16 +1,61 @@
 import streamlit as st
 
 st.set_page_config(
-    page_title="app",
+    page_title="JobFair: Fairness Benchmark",
     page_icon="👋",
 )
 
-st.title('JobFair: A Benchmark for Fairness in LLM Employment Decision')
-st.write("Welcome to JobFair! This benchmark is designed to evaluate the fairness of language models in employment decision-making. ")
-
-st.sidebar.success("Select a demo above.")
+st.title('JobFair: A Benchmark for Fairness in LLM Employment Decision-Making')
+st.write("Welcome to JobFair! This benchmark is designed to evaluate the fairness of language models in employment decision-making. Our goal is to provide a comprehensive tool for analyzing potential biases in how language models score resumes and make hiring recommendations.")
 
 st.markdown(
     """
+    ## About JobFair
+
+    The JobFair benchmark enables users to:
+    - **Upload and process** resumes to be evaluated by language models.
+    - **Analyze fairness** through various statistical tests, correlations, and divergences.
+    - **Download detailed evaluation results** for further review and reporting.
+
+    ### Key Features
+
+    - **Fairness Analysis**: Perform a variety of statistical tests to uncover potential biases in language model evaluations.
+    - **Comprehensive Reporting**: Generate detailed reports on the fairness of LLMs, including visualizations and downloadable data.
+    - **User-Friendly Interface**: Easily upload data, run analyses, and download results through an intuitive web interface.
+
+    ### How to Use
+
+    1. **Upload Data**: Start by uploading a CSV file containing the resumes and their respective scores.
+    2. **Run Evaluations**: Use the provided tools to perform statistical analyses and visualize the results.
+    3. **Download Results**: Export the analysis results for further examination and reporting.
+
+    We hope JobFair helps you in making more informed and fair employment decisions using language models.
     """
-)
+)
+
+# Sidebar content
+st.sidebar.title("Demos")
+
+st.sidebar.subheader("Injection Demo")
+st.sidebar.markdown(
+    """
+    In this demo, you can upload a dataset of resumes and use our language models to process and score them based on various parameters.
+
+    - **Model Settings**: Configure your model settings by selecting the type of agent (GPTAgent or AzureAgent), and specifying the API key, endpoint URL, model name, temperature, and max tokens.
+    - **Data Upload**: Choose to upload your own CSV file or use an example dataset.
+    - **Process Data**: Enter the relevant details such as occupation, group name, privilege label, and protect label. Specify the number of runs and process the data to get the model's scores.
+    - **Download Results**: After processing, download the generated results as a CSV file.
+    """
+)
+
+st.sidebar.subheader("Evaluation Demo")
+st.sidebar.markdown(
+    """
+    In this demo, you can evaluate the fairness of the scores generated by the language models.
+
+    - **Upload Results**: Upload the CSV file containing the processed results from the injection demo.
+    - **Statistical Tests**: Perform a variety of statistical tests to evaluate potential biases in the scores.
+    - **Correlations and Divergences**: Calculate correlations and divergences to further analyze the fairness of the results.
+    - **Download Evaluation**: Download the comprehensive evaluation results for further analysis.
+    """
+)