add app.py

app.py

@@ -0,0 +1,117 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
+
+save_path_abstract = 'ngocminhta/RoBERTa-MGT-Test'
+model_abstract = AutoModelForSequenceClassification.from_pretrained(save_path_abstract)
+tokenizer_abstract = AutoTokenizer.from_pretrained(save_path_abstract)
+
+classifier_abstract = pipeline('text-classification', model=model_abstract, tokenizer=tokenizer_abstract)
+
+save_path_essay = 'ngocminhta/RoBERTa-MGT-Test'
+model_essay = AutoModelForSequenceClassification.from_pretrained(save_path_essay)
+tokenizer_essay = AutoTokenizer.from_pretrained(save_path_essay)
+
+classifier_essay = pipeline('text-classification', model=model_essay, tokenizer=tokenizer_essay)
+
+def update(name, uploaded_file, radio_input):
+    scores = []
+    labels = ["human", "llm", "machine-polished", "machine-humanized"]
+
+    if uploaded_file is not None:
+        return f"{name}, you uploaded a file named {uploaded_file.name}."
+    else:
+        if radio_input == 'Scientific Abstract':
+            data = classifier_abstract(name)[0]
+            for i in range(4):
+                scores.append(data[i]['score'])
+            return f"""
+            Predicted label: {labels[scores.index(max(scores))]}.
+
+            Scores:
+            - Human: {scores[0]}
+            - LLM: {scores[1]}
+            - Machine-polised: {scores[2]}
+            - Machine-humanized: {scores[3]}"""
+
+        elif radio_input == 'Student Essay':
+            data = classifier_essay(name)[0]
+            for i in range(4):
+                scores.append(data[i]['score'])
+            return f"""
+            Predicted label: {labels[scores.index(max(scores))]}.
+
+            Scores:
+            - Human: {scores[0]}
+            - LLM: {scores[1]}
+            - Machine-polised: {scores[2]}
+            - Machine-humanized: {scores[3]}"""
+
+with gr.Blocks() as demo:
+    gr.Markdown(
+        """
+        <style>
+        .gr-button-secondary {
+            width: 100px;
+            height: 30px;
+            padding: 5px;
+        }
+        .gr-row {
+            display: flex;
+            align-items: center;
+            gap: 10px;
+        }
+        .gr-block {
+            padding: 20px;
+        }
+        .gr-markdown p {
+            font-size: 16px;
+        }
+        </style>
+        <span style='font-family: Arial, sans-serif; font-size: 20px;'>Was this text written by <strong>human</strong> or <strong>AI</strong>?</span>
+        <p style='font-family: Arial, sans-serif;'>Try detecting one of our sample texts:</p>
+        """
+    )
+    
+    with gr.Row():
+        for sample in ["Machine-Generated", "Human-Written", "Machine-Humanized", "Machine - Polished"]:
+            gr.Button(sample, variant="outline")
+
+    with gr.Row():
+        radio_button = gr.Radio(['Scientific Abstract', 'Student Essay'], label = 'Text Type', info = 'We have specialized models that work on domain-specific text.')
+    
+    with gr.Row():
+        input_text = gr.Textbox(placeholder="Paste your text here...", label="", lines=10)
+        file_input = gr.File(label="Upload File")
+
+    #file_input = gr.File(label="", visible=False)  # Hide the actual file input
+    
+    with gr.Row():
+        check_button = gr.Button("Check Origin", variant="primary")
+        clear_button = gr.ClearButton([input_text, file_input, radio_button], variant='stop')
+        #upload_button = gr.Button("Upload File", variant="secondary")
+    
+    out = gr.Textbox(label="OUTPUT", placeholder="", lines=2)
+    clear_button.add(out)
+    
+    check_button.click(fn=update, inputs=[input_text, file_input, radio_button], outputs=out)
+    #upload_button.click(lambda: None, inputs=[], outputs=[]).then(fn=update, inputs=[input_text, file_input], outputs=out)
+
+    # Adding JavaScript to simulate file input click
+    gr.Markdown(
+        """
+        <script>
+        document.addEventListener("DOMContentLoaded", function() {
+            const uploadButton = Array.from(document.getElementsByTagName('button')).find(el => el.innerText === "Upload File");
+            if (uploadButton) {
+                uploadButton.onclick = function() {
+                    document.querySelector('input[type="file"]').click();
+                };
+            }
+        });
+        </script>
+        """
+    )
+
+demo.launch(share=True)
+

requirements.txt

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+torch
+transformers