Update app.py

app.py

@@ -16,7 +16,7 @@ color_map = {
     '20%': green.c200,
     '30%': green.c100,
     '40%': green.c50,
-    '50%': None,
+    '50%': '#ffffff',
     '60%': red.c50,
     '70%': red.c100,
     '80%': red.c200,
@@ -31,23 +31,26 @@ def predict_doc(doc):
     res = []
     for sent in sents:
         prob = predict_one_sent(sent)
+
         data['sentence'].append(sent)
         data['score'].append(round(prob, 4))
+        if prob <= 0.5:
+            data['label'].append('Human')
+        else: data['label'].append('Machine')
+
         if prob < 0.1: label = '0%'
         elif prob < 0.2: label = '10%'
-        elif prob < 0.3: label = '20%',
-        elif prob < 0.4: label = '30%',
-        elif prob < 0.5: label = '40%',
-        elif prob < 0.6: label = '50%',
-        elif prob < 0.7: label = '60%',
-        elif prob < 0.8: label = '70%',
-        elif prob < 0.9: label = '80%',
+        elif prob < 0.3: label = '20%'
+        elif prob < 0.4: label = '30%'
+        elif prob < 0.5: label = '40%'
+        elif prob < 0.6: label = '50%'
+        elif prob < 0.7: label = '60%'
+        elif prob < 0.8: label = '70%'
+        elif prob < 0.9: label = '80%'
         elif prob < 1: label = '90%'
         else: label = '100%'
         res.append((sent, label))
-        if prob <= 0.5:
-            data['label'].append('Human')
-        else: data['label'].append('Machine')
+
     df = pd.DataFrame(data)
     df.to_csv('result.csv')
     overall_score = df.score.mean()
@@ -55,7 +58,7 @@ def predict_doc(doc):
     if overall_score <= 0.5: overall_label = 'Human'
     else: overall_label = 'Machine'
     sum_str = f'The essay is probably written by {overall_label}. The probability of being generated by AI is {overall_score}'
-    # print(sum_str)
+
     return sum_str, res, df, 'result.csv'
 
 
@@ -72,24 +75,32 @@ def predict_one_sent(sent):
 
 
 with gr.Blocks() as demo:
-    text_in = gr.Textbox(
-        lines=5, 
-        label='Essay input', 
-        info='Please enter the essay in the textbox'
-    )
-    button = gr.Button('Predict who writes this essay!')
-    summary = gr.Textbox(lines=1, label='Result summary')
-    sent_res = gr.HighlightedText(
-        label = 'Labeled Result'
-    ).style(color_map=color_map)
+    with gr.Row():
+        with gr.Column():
+            text_in = gr.Textbox(
+                lines=5, 
+                label='Essay input', 
+                info='Please enter the essay in the textbox'
+            )
+            btn = gr.Button('Predict who writes this essay!')
+
+        sent_res = gr.Highlight(
+            label='Labeled Result'
+        ).style(color_map=color_map)
+    
+    with gr.Row():
+        summary = gr.Text(
+            label='Result summary'
+        )
+        csv_f = gr.File(
+            label='CSV file storing data with all sentences.'
+        )
+
     tab = gr.DataFrame(
         label='Table with Probability Score',
-        max_rows=10
-    )
-    csv_f = gr.File(
-        label='CSV file storing data with all sentences.'
+        max_rows=100
     )
-    button.click(predict_doc, inputs=[text_in], outputs=[summary, sent_res, tab, csv_f], api_name='predict_doc')
+    btn.click(predict_doc, inputs=[text_in], outputs=[summary, sent_res, tab, csv_f], api_name='predict_doc')
 
 demo.launch()