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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. There are 3 variations of the Roberta Classifier Model, The model was trained on CHEAT, GPABenchmark, OpenGPT datasets.You can choose dataset variation of the model on the sidebar.")
# st.write("Reference on how we built Roberta Sentinel: https://arxiv.org/abs/2305.07969")
# # Add 4 options for 4 models
# ModelOption = st.sidebar.selectbox(
# 'Which Model do you want to use?',
# ('RobertaClassifier'),
# )
DatasetOption = st.sidebar.selectbox(
'Select Input Text Domain',
('General Text', 'Computer Science Abstract', 'Scientific Abstract'),
)
text = st.text_area('Enter text here (max 512 words)', '', height=200)
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")
# if ModelOption == 'RobertaClassifier':
# if DatasetOption == 'OpenGPT':
# result = RobertaClassifierOpenGPTInference(text)
# st.write("Model: RobertaClassifierOpenGPT")
# elif DatasetOption == 'GPABenchmark':
# result = RobertaClassifierGPABenchmarkInference(text)
# st.write("Model: RobertaClassifierGPABenchmark")
# elif DatasetOption == 'CHEAT':
# result = RobertaClassifierCHEATInference(text)
# st.write("Model: RobertaClassifierCHEAT")
if DatasetOption == 'General Text':
result = RobertaClassifierOpenGPTInference(text)
st.write("Model: RobertaClassifierOpenGPT")
elif DatasetOption == 'Computer Science Abstract':
result = RobertaClassifierGPABenchmarkInference(text)
st.write("Model: RobertaClassifierGPABenchmark")
elif DatasetOption == 'Scientific Abstract':
result = RobertaClassifierCHEATInference(text)
st.write("Model: RobertaClassifierCHEAT")
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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