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import streamlit as st | |
from transformers import pipeline | |
from ModelDriver import * | |
import numpy as np | |
# Add a title | |
st.title('GPT Detection Demo') | |
# Add 4 options for 4 models | |
ModelOption = st.sidebar.selectbox( | |
'Which Model do you want to use?', | |
('RobertaSentinel', 'RobertaClassifier'), | |
) | |
DatasetOption = st.sidebar.selectbox( | |
'Which Dataset do you want to use?', | |
('OpenGPT', 'CSAbstract'), | |
) | |
text = st.text_area('Enter text here', '') | |
if st.button('Generate'): | |
if ModelOption == 'RobertaSentinel': | |
if DatasetOption == 'OpenGPT': | |
result = RobertaSentinelOpenGPTInference(text) | |
elif DatasetOption == 'CSAbstract': | |
result = RobertaSentinelCSAbstractInference(text) | |
elif ModelOption == 'RobertaClassifier': | |
if DatasetOption == 'OpenGPT': | |
result = RobertaClassifierOpenGPTInference(text) | |
elif DatasetOption == 'CSAbstract': | |
result = RobertaClassifierCSAbstractInference(text) | |
Prediction = "Human Written" if not np.argmax(result) else "Machine Generated" | |
print(f"Prediction: {Prediction} ") | |
print(f"Probabilty:", max(result)) | |
st.write(result) | |