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raj-tomar001
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uploaded app.py
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app.py
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import gradio as gr
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from transformers import DebertaTokenizer, DebertaForSequenceClassification
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from transformers import pipeline
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save_path_abstract = '/home/raj.tomar/Downloads/MGT/experiments/fine-tuned-deberta/'
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model_abstract = DebertaForSequenceClassification.from_pretrained(save_path_abstract)
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tokenizer_abstract = DebertaTokenizer.from_pretrained(save_path_abstract)
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classifier_abstract = pipeline('text-classification', model=model_abstract, tokenizer=tokenizer_abstract)
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save_path_essay = '/home/raj.tomar/Downloads/MGT/experiments/fine-tuned-deberta/'
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model_essay = DebertaForSequenceClassification.from_pretrained(save_path_essay)
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tokenizer_essay = DebertaTokenizer.from_pretrained(save_path_essay)
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classifier_essay = pipeline('text-classification', model=model_essay, tokenizer=tokenizer_essay)
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def update(name, uploaded_file, radio_input):
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if uploaded_file is not None:
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return f"{name}, you uploaded a file named {uploaded_file.name}."
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else:
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if radio_input == 'Scientific Abstract':
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data = classifier_abstract(name)[0]['label']
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if data == 'LABEL_0':
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return "human_text"
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if data == 'LABEL_1':
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return "machine_text"
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if data == 'LABEL_2':
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return "human-written | machine-polished"
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if data == 'LABEL_3':
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return "machine-generated | machine-humanized"
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else:
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if radio_input == 'Student Essay':
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data = classifier_essay(name)[0]['label']
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if data == 'LABEL_0':
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return "human_text"
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if data == 'LABEL_1':
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return "machine_text"
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if data == 'LABEL_2':
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return "human-written | machine-polished"
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if data == 'LABEL_3':
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return "machine-generated | machine-humanized"
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# return "Hold on!"
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with gr.Blocks() as demo:
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gr.Markdown(
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"""
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<style>
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.gr-button-secondary {
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width: 100px;
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height: 30px;
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padding: 5px;
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}
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.gr-row {
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display: flex;
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align-items: center;
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gap: 10px;
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}
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.gr-block {
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padding: 20px;
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}
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.gr-markdown p {
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font-size: 16px;
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}
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</style>
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<span style='font-family: Arial, sans-serif; font-size: 20px;'>Was this text written by <strong>human</strong> or <strong>AI</strong>?</span>
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<p style='font-family: Arial, sans-serif;'>Try detecting one of our sample texts:</p>
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"""
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)
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with gr.Row():
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for sample in ["Machine-Generated", "Human-Written", "Machine-Humanized", "Machine - Polished"]:
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gr.Button(sample, variant="outline")
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with gr.Row():
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radio_button = gr.Radio(['Scientific Abstract', 'Student Essay'], label = 'Text Type', info = 'We have specialized models that work on domain-specific text.')
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with gr.Row():
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input_text = gr.Textbox(placeholder="Paste your text here...", label="", lines=10)
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file_input = gr.File(label="Upload File")
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#file_input = gr.File(label="", visible=False) # Hide the actual file input
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with gr.Row():
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check_button = gr.Button("Check Origin", variant="primary")
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clear_button = gr.ClearButton([input_text, file_input, radio_button], variant='stop')
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#upload_button = gr.Button("Upload File", variant="secondary")
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out = gr.Textbox(label="OUTPUT", placeholder="", lines=2)
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clear_button.add(out)
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check_button.click(fn=update, inputs=[input_text, file_input, radio_button], outputs=out)
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#upload_button.click(lambda: None, inputs=[], outputs=[]).then(fn=update, inputs=[input_text, file_input], outputs=out)
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# Adding JavaScript to simulate file input click
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gr.Markdown(
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"""
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<script>
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document.addEventListener("DOMContentLoaded", function() {
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const uploadButton = Array.from(document.getElementsByTagName('button')).find(el => el.innerText === "Upload File");
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if (uploadButton) {
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uploadButton.onclick = function() {
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document.querySelector('input[type="file"]').click();
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};
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}
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});
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</script>
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"""
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)
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demo.launch(share=True)
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