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