update

pages/{2_Evaluation_Demo.py → 1_Generation.py}

@@ -36,9 +36,6 @@ if st.sidebar.button("Reset Model Info"):
 if st.sidebar.button("Submit Model Info"):
     st.session_state.model_submitted = True
 
-
-
-
 # Ensure experiment settings are only shown if model info is submitted
 if st.session_state.model_submitted:
     df = None
@@ -54,6 +51,8 @@ if st.session_state.model_submitted:
 
         st.write('Data:', df)
 
+        # Button to add a new row
+
         st.session_state.occupation = st.text_input("Occupation", value=st.session_state.occupation)
         st.session_state.group_name = st.text_input("Group Name", value=st.session_state.group_name)
         st.session_state.privilege_label = st.text_input("Privilege Label", value=st.session_state.privilege_label)
@@ -73,25 +72,20 @@ if st.session_state.model_submitted:
                 df = process_scores(df, st.session_state.num_run, parameters, st.session_state.privilege_label, st.session_state.protect_label, agent, st.session_state.group_name, st.session_state.occupation)
                 st.session_state.data_processed = True  # Mark as processed
 
-            # Add ranks for each score within each row
-            ranks = df[['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score']].rank(axis=1,ascending=False)
-
-            df['Privilege_Rank'] = ranks['Privilege_Avg_Score']
-            df['Protect_Rank'] = ranks['Protect_Avg_Score']
-            df['Neutral_Rank'] = ranks['Neutral_Avg_Score']
 
             st.write('Processed Data:', df)
 
-            # use the data to generate a plot
-            st.write("Plotting the data")
+            # Allow downloading of the evaluation results
+            st.download_button(
+                label="Download Evaluation Results",
+                data=df.to_csv().encode('utf-8'),
+                file_name='generation_results.csv',
+                mime='text/csv',
+            )
+
+
 
-            test_results = statistical_tests(df)
-            print(test_results)
-            evaluation_results = result_evaluation(test_results)
-            print(evaluation_results)
 
-            for key, value in evaluation_results.items():
-                st.write(f"{key}: {value}")
 
 
         if st.button("Reset Experiment Settings"):

pages/1_Generation_Demo.py

@@ -1,126 +0,0 @@
-import streamlit as st
-import pandas as pd
-from io import StringIO
-from util.generation import process_scores
-from util.model import AzureAgent, GPTAgent
-from util.analysis import statistical_tests, result_evaluation
-
-# Set up the Streamlit interface
-st.title('JobFair: A Benchmark for Fairness in LLM Employment Decision')
-st.sidebar.title('Model Settings')
-
-# Define a function to manage state initialization
-def initialize_state():
-    keys = ["model_submitted", "api_key", "endpoint_url", "deployment_name", "temperature", "max_tokens",
-            "data_processed", "group_name","occupation", "privilege_label", "protect_label", "num_run", "uploaded_file"]
-    defaults = [False, "", "https://safeguard-monitor.openai.azure.com/", "gpt35-1106", 0.5, 150, False,"Gender", "Programmer", "Male", "Female", 1, None]
-    for key, default in zip(keys, defaults):
-        if key not in st.session_state:
-            st.session_state[key] = default
-
-initialize_state()
-
-# Model selection and configuration
-model_type = st.sidebar.radio("Select the type of agent", ('GPTAgent', 'AzureAgent'))
-st.session_state.api_key = st.sidebar.text_input("API Key", type="password", value=st.session_state.api_key)
-st.session_state.endpoint_url = st.sidebar.text_input("Endpoint URL", value=st.session_state.endpoint_url)
-st.session_state.deployment_name = st.sidebar.text_input("Model Name", value=st.session_state.deployment_name)
-api_version = '2024-02-15-preview' if model_type == 'GPTAgent' else ''
-st.session_state.temperature = st.sidebar.slider("Temperature", 0.0, 1.0, st.session_state.temperature, 0.01)
-st.session_state.max_tokens = st.sidebar.number_input("Max Tokens", 1, 1000, st.session_state.max_tokens)
-
-if st.sidebar.button("Reset Model Info"):
-    initialize_state()  # Reset all state to defaults
-    st.experimental_rerun()
-
-if st.sidebar.button("Submit Model Info"):
-    st.session_state.model_submitted = True
-
-
-def add_row(df):
-    # Add a new row with default or empty values at the end of the DataFrame
-    new_row = pd.DataFrame([{col: "" for col in df.columns}])
-    return pd.concat([df, new_row], ignore_index=True)
-
-def remove_row(df, index):
-    # Remove a row based on the index provided
-    return df.drop(index, errors='ignore').reset_index(drop=True)
-
-
-# Ensure experiment settings are only shown if model info is submitted
-if st.session_state.model_submitted:
-    df = None
-    file_options = st.radio("Choose file source:", ["Upload", "Example"])
-    if file_options == "Example":
-        df = pd.read_csv("prompt_test.csv")
-    else:
-        st.session_state.uploaded_file = st.file_uploader("Choose a file")
-        if st.session_state.uploaded_file is not None:
-            data = StringIO(st.session_state.uploaded_file.getvalue().decode("utf-8"))
-            df = pd.read_csv(data)
-    if df is not None:
-
-        st.write('Data:', df)
-
-        # Button to add a new row
-        if st.button('Add Row'):
-            df = add_row(df)
-            st.session_state.uploaded_file = StringIO(
-                df.to_csv(index=False))  # Update the session file after modification
-
-        # Input for row index to remove
-        row_to_remove = st.number_input('Enter row index to remove', min_value=0, max_value=len(df) - 1, step=1,
-                                        format='%d')
-        if st.button('Remove Row'):
-            df = remove_row(df, row_to_remove)
-            st.session_state.uploaded_file = StringIO(
-                df.to_csv(index=False))  # Update the session file after modification
-
-        st.session_state.occupation = st.text_input("Occupation", value=st.session_state.occupation)
-        st.session_state.group_name = st.text_input("Group Name", value=st.session_state.group_name)
-        st.session_state.privilege_label = st.text_input("Privilege Label", value=st.session_state.privilege_label)
-        st.session_state.protect_label = st.text_input("Protect Label", value=st.session_state.protect_label)
-        st.session_state.num_run = st.number_input("Number of Runs", 1, 10, st.session_state.num_run)
-
-        if st.button('Process Data') and not st.session_state.data_processed:
-            # Initialize the correct agent based on model type
-            if model_type == 'AzureAgent':
-                agent = AzureAgent(st.session_state.api_key, st.session_state.endpoint_url, st.session_state.deployment_name)
-            else:
-                agent = GPTAgent(st.session_state.api_key, st.session_state.endpoint_url, st.session_state.deployment_name, api_version)
-
-            # Process data and display results
-            with st.spinner('Processing data...'):
-                parameters = {"temperature": st.session_state.temperature, "max_tokens": st.session_state.max_tokens}
-                df = process_scores(df, st.session_state.num_run, parameters, st.session_state.privilege_label, st.session_state.protect_label, agent, st.session_state.group_name, st.session_state.occupation)
-                st.session_state.data_processed = True  # Mark as processed
-
-            # Add ranks for each score within each row
-            ranks = df[['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score']].rank(axis=1,ascending=False)
-
-            df['Privilege_Rank'] = ranks['Privilege_Avg_Score']
-            df['Protect_Rank'] = ranks['Protect_Avg_Score']
-            df['Neutral_Rank'] = ranks['Neutral_Avg_Score']
-
-            st.write('Processed Data:', df)
-
-            # use the data to generate a plot
-            st.write("Plotting the data")
-
-            test_results = statistical_tests(df)
-            print(test_results)
-            evaluation_results = result_evaluation(test_results)
-            print(evaluation_results)
-
-            for key, value in evaluation_results.items():
-                st.write(f"{key}: {value}")
-
-
-        if st.button("Reset Experiment Settings"):
-            st.session_state.occupation = "Programmer"
-            st.session_state.group_name = "Gender"
-            st.session_state.privilege_label = "Male"
-            st.session_state.protect_label = "Female"
-            st.session_state.num_run = 1
-            st.session_state.data_processed = False
-            st.session_state.uploaded_file = None

pages/2_Evaluation.py

@@ -0,0 +1,41 @@
+import streamlit as st
+import pandas as pd
+from io import StringIO
+from util.analysis import statistical_tests, result_evaluation
+
+def app():
+    st.title('Data Evaluation for Fairness in LLM Employment Decision')
+
+    # Allow users to upload a CSV file with processed results
+    uploaded_file = st.file_uploader("Upload your processed CSV file", type="csv")
+    if uploaded_file is not None:
+        data = StringIO(uploaded_file.getvalue().decode('utf-8'))
+        df = pd.read_csv(data)
+
+        # Add ranks for each score within each row
+        ranks = df[['Privilege_Avg_Score', 'Protect_Avg_Score', 'Neutral_Avg_Score']].rank(axis=1, ascending=False)
+
+        df['Privilege_Rank'] = ranks['Privilege_Avg_Score']
+        df['Protect_Rank'] = ranks['Protect_Avg_Score']
+        df['Neutral_Rank'] = ranks['Neutral_Avg_Score']
+
+        st.write('Uploaded Data:', df)
+
+        # Display button to perform evaluation if data is uploaded
+        if st.button('Evaluate Data'):
+            with st.spinner('Evaluating data...'):
+                test_results = statistical_tests(df)
+                evaluation_results = result_evaluation(test_results)
+                st.write('Evaluation Results:', evaluation_results)
+
+                # Allow downloading of the evaluation results
+                results_df = pd.DataFrame.from_dict(evaluation_results, orient='index', columns=['Value'])
+                st.download_button(
+                    label="Download Evaluation Results",
+                    data=results_df.to_csv().encode('utf-8'),
+                    file_name='evaluation_results.csv',
+                    mime='text/csv',
+                )
+
+if __name__ == "__main__":
+    app()