update

app.py

@@ -61,17 +61,20 @@ class GPTAgent:
 # Streamlit app interface
 st.title('JobFair: A Benchmark for Fairness in LLM Employment Decision')
 
-model_type = st.radio("Select the type of agent", ('AzureAgent', 'GPTAgent'))
-api_key = st.text_input("API Key", type="password")
-endpoint_url = st.text_input("Endpoint URL")
-deployment_name = st.text_input("Model Name")
-api_version = st.text_input("API Version")  # Default API version
+# Streamlit app interface
+st.sidebar.title('Model Settings')
 
-temperature = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.5, step=0.01)
-max_tokens = st.number_input("Max Tokens", min_value=1, max_value=1000, value=150)
-stop_sequences = st.text_input("Stop Sequences", "")
-parameters = {"temperature": temperature, "max_tokens": max_tokens, "stop": [stop_sequences] if stop_sequences else []}
+model_type = st.sidebar.radio("Select the type of agent", ('AzureAgent', 'GPTAgent'))
+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")
+api_version = st.sidebar.text_input("API Version", '2022-11-01')  # 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)
+stop_sequences = st.sidebar.text_input("Stop Sequences", "")
+parameters = {"temperature": temperature, "max_tokens": max_tokens, "stop": [stop_sequences] if stop_sequences else []}
 
 # File upload and data display
 uploaded_file = st.file_uploader("Choose a file")
@@ -79,7 +82,6 @@ if uploaded_file is not None:
     # Read data
     data = StringIO(uploaded_file.getvalue().decode("utf-8"))
     df = pd.read_csv(data)
-    st.write('Uploaded Data:', df.head())
 
     # Process data button
     if st.button('Process Data'):
@@ -89,14 +91,7 @@ if uploaded_file is not None:
             agent = GPTAgent(api_key, endpoint_url, deployment_name, api_version)
 
         # Example processing step (adapt as needed)
-        df['Response'] = df['prompt'].apply(lambda x: agent.invoke(x, parameters))
+        df['Response'] = df['Input Column'].apply(lambda x: agent.invoke(x, parameters))
 
-        st.write('Processed Data:', df.head())
-
-        # Generate download link
-        st.download_button(
-            label="Download processed data",
-            data=df.to_csv().encode('utf-8'),
-            file_name='processed_data.csv',
-            mime='text/csv',
-        )
+        # Display processed data
+        st.write('Processed Data:', df)