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| import streamlit as st | |
| import pandas as pd | |
| from io import StringIO | |
| from util.injection import process_scores_multiple | |
| from util.model import AzureAgent, GPTAgent | |
| from util.prompt import PROMPT_TEMPLATE | |
| import os | |
| st.title('Result Generation') | |
| def check_password(): | |
| def password_entered(): | |
| # if password_input == os.getenv('PASSWORD'): | |
| if password_input == " ": | |
| st.session_state['password_correct'] = True | |
| else: | |
| st.error("Incorrect Password, please try again.") | |
| password_input = st.text_input("Enter Password:", type="password") | |
| submit_button = st.button("Submit", on_click=password_entered) | |
| if submit_button and not st.session_state.get('password_correct', False): | |
| st.error("Please enter a valid password to access the demo.") | |
| # 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", "occupation_submitted","sample_size","charateristics","proportion","prompt_template"] | |
| defaults = [False, "", "https://safeguard-monitor.openai.azure.com/", "gpt35-1106", 0.0, 300, False, "Gender", | |
| "Programmer", "Male", "Female", 1, None, False,2,"This candidate's performance during the internship at our institution was evaluated to be at the 50th percentile among current employees.", 1.0 ,PROMPT_TEMPLATE] | |
| for key, default in zip(keys, defaults): | |
| if key not in st.session_state: | |
| st.session_state[key] = default | |
| def change_column_value(df_old, df_change, here_column, switch_to_column, common_column='Resume'): | |
| merged_df = df_old.merge(df_change, on=common_column, how='left') | |
| df_old[here_column] = merged_df[switch_to_column] | |
| return df_old | |
| if not st.session_state.get('password_correct', False): | |
| check_password() | |
| else: | |
| st.sidebar.success("Password Verified. Proceed with the demo.") | |
| st.sidebar.title('Model Settings') | |
| 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 | |
| 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("resume_subsampled.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: | |
| categories = list(df["Occupation"].unique()) | |
| st.session_state.occupation = st.selectbox("Occupation", options=categories, index=categories.index(st.session_state.occupation) if st.session_state.occupation in categories else 0) | |
| st.session_state.prompt_template = st.text_area("Prompt Template", value=st.session_state.prompt_template) | |
| st.session_state.sample_size = st.number_input("Sample Size", 2, len(df), st.session_state.sample_size) | |
| 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) | |
| #st.session_state.charateristics = st.text_area("Characteristics", value=st.session_state.charateristics) | |
| df = df[df["Occupation"] == st.session_state.occupation] | |
| if file_options == "Example": | |
| st.session_state.proportion = st.slider("Proportion", 0.2, 1.0, float(st.session_state.proportion), 0.2) | |
| df_chunked = pd.read_csv("resume_chunked.csv") | |
| column_switch_to = f'{st.session_state.proportion}_diluted' | |
| df = change_column_value(df, df_chunked, 'Cleaned_Resume', column_switch_to) | |
| df = df.sample(n=st.session_state.sample_size, random_state=42) | |
| st.write('Data:', df) | |
| 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) | |
| with st.spinner('Processing data...'): | |
| parameters = {"temperature": st.session_state.temperature, "max_tokens": st.session_state.max_tokens} | |
| preprocessed_df = process_scores_multiple(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.prompt_template) | |
| st.session_state.data_processed = True # Mark as processed | |
| st.write('Processed Data:', preprocessed_df) | |
| # Allow downloading of the evaluation results | |
| st.download_button( | |
| label="Download Generation Results", | |
| data=preprocessed_df.to_csv().encode('utf-8'), | |
| file_name=f'{st.session_state.occupation}.csv', | |
| mime='text/csv', | |
| ) | |
| if st.button("Reset Experiment Settings"): | |
| st.session_state.sample_size = 2 | |
| st.session_state.charateristics = "This candidate's performance during the internship at our institution was evaluated to be at the 50th percentile among current employees." | |
| 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.prompt_template = PROMPT_TEMPLATE | |
| st.session_state.num_run = 1 | |
| st.session_state.data_processed = False | |
| st.session_state.uploaded_file = None | |
| st.session_state.proportion = 1.0 | |