Create app.py

app.py

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+import nltk
+nltk.download('punkt')
+
+import pandas as pd
+import gradio as gr
+
+from nltk import sent_tokenize
+from transformers import pipeline
+
+detector = pipeline(task='text-classification', model='yikang0131/argugpt-detector-sent')
+
+def predict_doc(doc):
+    sents = sent_tokenize(doc)
+    data = {'sentence': [], 'label': [], 'score': []}
+    res = []
+    for sent in sents:
+        label, score = predict_one_sent(sent)
+        data['sentence'].append(sent)
+        data['score'].append(score)
+        if label == 'LABEL_0':
+            res.append((sent, 'Human'))
+            data['label'].append('Human')
+        else: 
+            res.append((sent, 'Machine'))
+            data['label'].append('Machine')
+    df = pd.DataFrame(data)
+    df.to_csv('result.csv')
+    return res, df, 'result.csv'
+
+
+def predict_one_sent(sent):
+    res = detector(sent)[0]
+    return res['label'], res['score']
+
+
+iface = gr.Interface(
+    fn=predict_doc,
+    inputs=[
+        gr.Textbox(
+            label='Essay input',
+            info="Please enter essay in the textbox",
+            lines=5
+        )
+    ],
+    outputs=[
+        gr.HighlightedText(
+            label='Labeled Result',
+            show_legend=True
+        ).style(color_map={'Machine': 'red', 'Human': 'green'}),
+        gr.DataFrame(
+            label='Table with Probability Score',
+            max_rows=10
+        ),
+        gr.File(
+            label='CSV file storing data with all sentences'
+        )
+    ],
+    theme=gr.themes.Base()
+)
+
+iface.launch()