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import streamlit as st
# Load model directly
from transformers import (AutoTokenizer,
AutoModelForSequenceClassification,
TextClassificationPipeline)
tokenizer = AutoTokenizer.from_pretrained("Hello-SimpleAI/chatgpt-detector-roberta")
model = AutoModelForSequenceClassification.from_pretrained("Hello-SimpleAI/chatgpt-detector-roberta")
pipe = TextClassificationPipeline(model=model, tokenizer=tokenizer, return_all_scores=True)
def score_and_visualize(text):
prediction = pipe([text])
f_score = 0
f_label = ""
label_0 = prediction[0][0]['label']
score_0 = prediction[0][0]['score']
label_1 = prediction[0][1]['label']
score_1 = prediction[0][1]['score']
if score_0 > score_1:
f_score = (round(score_0))*100
f_label = label_0
else:
f_score = (round(score_1))*100
f_label = label_1
return f_score, f_label
def main():
st.title("Human vs ChatGPT Classification Model")
# Create an input text box
input_text = st.text_area("Enter your text", "")
# Create a button to trigger model inference
if st.button("Analyze"):
# Perform inference using the loaded model
score, label = score_and_visualize(input_text)
st.write("The input text is " ,label , " based.")
if __name__ == "__main__":
main() |