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import pandas as pd
import streamlit as st
from util.evaluator import evaluator, write_evaluation_commentary
import os
# Predefined examples
examples = {
'good': {
'question': "What causes rainbows to appear in the sky?",
'explanation': "Rainbows appear when sunlight is refracted, dispersed, and reflected inside water droplets in the atmosphere, resulting in a spectrum of light appearing in the sky."
},
'bad': {
'question': "What causes rainbows to appear in the sky?",
'explanation': "Rainbows happen because light in the sky gets mixed up and sometimes shows colors when it's raining or when there is water around."
}
}
# Function to check password
def check_password():
def password_entered():
if password_input == os.getenv('PASSWORD'):
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.")
def batch_evaluate(uploaded_file):
df = pd.read_csv(uploaded_file)
eval = evaluator(model_name='gpt4-1106') # Assuming model name is fixed for simplicity
results = []
for _, row in df.iterrows():
question = row['question']
explanation = row['explanation']
scores = eval(question, explanation)
commentary = write_evaluation_commentary(scores)[["Principle", "Score"]].transpose().to_dict()
results.append({**{'Question': question, 'Explanation': explanation}, **commentary})
result_df = pd.DataFrame(results)
return result_df
# Title of the application
st.title('Natural Language Explanation Demo')
# Check if password has been validated
if not st.session_state.get('password_correct', False):
check_password()
else:
st.sidebar.success("Password Verified. Proceed with the demo.")
st.header("Batch Evaluation of Questions and Explanations")
uploaded_file = st.file_uploader("Upload CSV file with columns 'question' and 'explanation'", type='csv')
if uploaded_file is not None:
if st.button('Evaluate Explanations'):
result_df = batch_evaluate(uploaded_file)
st.write('### Evaluated Results')
st.dataframe(result_df)
csv = result_df.to_csv(index=False)
st.download_button(
label="Download evaluation results as CSV",
data=csv,
file_name='evaluated_results.csv',
mime='text/csv'
)
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