update

app.py

@@ -4,47 +4,92 @@ from io import StringIO
 from generation import process_scores
 from model import AzureAgent, GPTAgent
 
+# Initialize session state variables if they don't already exist
+def initialize_state():
+    if 'data_processed' not in st.session_state:
+        st.session_state.data_processed = False
+    if 'api_key' not in st.session_state:
+        st.session_state.api_key = ""
+    if 'endpoint_url' not in st.session_state:
+        st.session_state.endpoint_url = ""
+    if 'deployment_name' not in st.session_state:
+        st.session_state.deployment_name = ""
+    if 'temperature' not in st.session_state:
+        st.session_state.temperature = 0.5
+    if 'max_tokens' not in st.session_state:
+        st.session_state.max_tokens = 150
+    if 'group_name' not in st.session_state:
+        st.session_state.group_name = ""
+    if 'privilege_label' not in st.session_state:
+        st.session_state.privilege_label = ""
+    if 'protect_label' not in st.session_state:
+        st.session_state.protect_label = ""
+    if 'num_run' not in st.session_state:
+        st.session_state.num_run = 1
 
-# Streamlit app interface
-st.title('JobFair: A Benchmark for Fairness in LLM Employment Decision')
+initialize_state()
 
-# Streamlit app interface
+# Set up the Streamlit interface
+st.title('JobFair: A Benchmark for Fairness in LLM Employment Decision')
 st.sidebar.title('Model Settings')
 
-model_type = st.sidebar.radio("Select the type of agent", ('GPTAgent','AzureAgent'))
-api_key = st.sidebar.text_input("API Key", type="password")
-endpoint_url = st.sidebar.text_input("Endpoint URL")
-deployment_name = st.sidebar.text_input("Model Name")
-
-if model_type == 'GPTAgent':
-    api_version = st.sidebar.text_input("API Version", '2024-02-15-preview')  # Default API version
-
-# Model invocation parameters
-temperature = st.sidebar.slider("Temperature", min_value=0.0, max_value=1.0, value=0.5, step=0.01)
-max_tokens = st.sidebar.number_input("Max Tokens", min_value=1, max_value=1000, value=150)
-parameters = {"temperature": temperature, "max_tokens": max_tokens}
-
-group_name = st.text_input("Group Name")
-privilege_label = st.text_input("Privilege Name")
-protect_label = st.text_input("Protect Name")
-num_run = st.number_input("Number of runs", min_value=1, value=1)
-
-# File upload and data display
-uploaded_file = st.file_uploader("Choose a file")
-if uploaded_file is not None:
-    # Read data
-    data = StringIO(uploaded_file.getvalue().decode("utf-8"))
-    df = pd.read_csv(data)
-
-    # Process data button
-    if st.button('Process Data'):
-        if model_type == 'AzureAgent':
-            agent = AzureAgent(api_key, endpoint_url, deployment_name)
-        else:
-            agent = GPTAgent(api_key, endpoint_url, deployment_name, api_version)
-
-        # Show progressing bar
-        with st.spinner('Processing data...'):
-            df = process_scores(df,num_run,parameters,privilege_label,protect_label,agent,group_name)
-
-        st.write('Processed Data:', df)
+# Model selection and configuration
+model_type = st.sidebar.radio("Select the type of agent", ('GPTAgent', 'AzureAgent'))
+api_key = st.sidebar.text_input("API Key", type="password", value=st.session_state.api_key)
+endpoint_url = st.sidebar.text_input("Endpoint URL", value=st.session_state.endpoint_url)
+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 ''
+temperature = st.sidebar.slider("Temperature", 0.0, 1.0, st.session_state.temperature, 0.01)
+max_tokens = st.sidebar.number_input("Max Tokens", 1, 1000, st.session_state.max_tokens)
+
+# Reset buttons for model information
+if st.sidebar.button("Reset Model Info"):
+    st.session_state.api_key = ""
+    st.session_state.endpoint_url = ""
+    st.session_state.deployment_name = ""
+    st.session_state.temperature = 0.5
+    st.session_state.max_tokens = 150
+    st.experimental_rerun()
+
+submit_model_info = st.sidebar.button("Submit Model Info")
+
+# Data upload and processing with reset option
+if submit_model_info:
+    parameters = {"temperature": temperature, "max_tokens": max_tokens}
+
+    group_name = st.text_input("Group Name", value=st.session_state.group_name)
+    privilege_label = st.text_input("Privilege Name", value=st.session_state.privilege_label)
+    protect_label = st.text_input("Protect Name", value=st.session_state.protect_label)
+    num_run = st.number_input("Number of runs", min_value=1, value=st.session_state.num_run)
+    uploaded_file = st.file_uploader("Choose a file")
+
+    # Reset button for experiment settings
+    if st.button("Reset Experiment Settings"):
+        st.session_state.group_name = ""
+        st.session_state.privilege_label = ""
+        st.session_state.protect_label = ""
+        st.session_state.num_run = 1
+        st.session_state.data_processed = False
+        st.experimental_rerun()
+
+    if uploaded_file is not None:
+        data = StringIO(uploaded_file.getvalue().decode("utf-8"))
+        df = pd.read_csv(data)
+
+        process_button = st.button('Process Data')
+
+        if process_button and not st.session_state.data_processed:
+            # Initialize the correct agent based on model type
+            if model_type == 'AzureAgent':
+                agent = AzureAgent(api_key, endpoint_url, deployment_name)
+            else:
+                agent = GPTAgent(api_key, endpoint_url, deployment_name, api_version)
+
+            # Process data and display results
+            with st.spinner('Processing data...'):
+                df = process_scores(df, num_run, parameters, privilege_label, protect_label, agent, group_name)
+                st.session_state.data_processed = True  # Mark as processed
+
+            st.write('Processed Data:', df)
+        elif process_button and st.session_state.data_processed:
+            st.warning("Data already processed for this session. Reset or re-upload to process new data.")