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import streamlit as st
import pandas as pd
from io import StringIO
from util.generation import process_scores_single
from util.model import AzureAgent, GPTAgent
# Set up the Streamlit interface
st.title('Result Generation')
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", "counterfactual_label", "num_run",
"uploaded_file"]
defaults = [False, "", "https://safeguard-monitor.openai.azure.com/", "gpt35-1106", 0.5, 150, False, "Gender",
"Programmer", "Male", 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
# 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
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.counterfactual_label = st.text_input("Counterfactual Label", value=st.session_state.counterfactual_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_single(df, st.session_state.num_run, parameters, st.session_state.counterfactual_label,
agent, st.session_state.group_name,
st.session_state.occupation)
st.session_state.data_processed = True # Mark as processed
st.write('Processed Data:', df)
# Allow downloading of the evaluation results
st.download_button(
label="Download Generation Results",
data=df.to_csv().encode('utf-8'),
file_name='generation_results.csv',
mime='text/csv',
)
if st.button("Reset Experiment Settings"):
st.session_state.occupation = "Programmer"
st.session_state.group_name = "Gender"
st.session_state.counterfactual_label = "Male"
st.session_state.num_run = 1
st.session_state.data_processed = False
st.session_state.uploaded_file = None
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