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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')
st.write("This is a demo for GPT detection. You can use this demo to test the model. The model is trained on two datasets: OpenGPT and CSAbstract. You can choose the model and dataset in the sidebar.")
# 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 the model was trained on?',
('OpenGPT', 'CSAbstract'),
)
text = st.text_area('Enter text here (max 500 words)', '')
if st.button('Generate'):
if ModelOption == 'RobertaSentinel':
if DatasetOption == 'OpenGPT':
result = RobertaSentinelOpenGPTInference(text)
st.write("Model: RobertaSentinelOpenGPT")
elif DatasetOption == 'CSAbstract':
result = RobertaSentinelCSAbstractInference(text)
st.write("Model: RobertaSentinelCSAbstract")
elif ModelOption == 'RobertaClassifier':
if DatasetOption == 'OpenGPT':
result = RobertaClassifierOpenGPTInference(text)
st.write("Model: RobertaClassifierOpenGPT")
elif DatasetOption == 'CSAbstract':
result = RobertaClassifierCSAbstractInference(text)
st.write("Model: RobertaClassifierCSAbstract")
Prediction = "Human Written" if not np.argmax(result) else "Machine Generated"
st.write(f"Prediction: {Prediction} ")
st.write(f"Probabilty:", max(result))
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