Update app.py

app.py

@@ -75,36 +75,39 @@ kor_model = AutoModelForSequenceClassification.from_pretrained(
 )
 
 
-def builder(lang, text):
+def builder(Lang, Text):
     percent_kor, percent_eng = 0, 0
-    text_list = text.split(' ')
+    text_list = Text.split(' ')
 
 
     # [ output_1 ]
-    if lang == 'Default':
-        pred = LANGUAGE.predict_lang(text)
+    if Lang == 'Default':
+        pred = LANGUAGE.predict_lang(Text)
         if '__label__en' in pred[0]:
-            lang = 'Eng'
+            Lang = 'Eng'
             idx = pred[0].index('__label__en')
-            percent_eng = pred[1][idx]
+            p_eng = pred[1][idx]
         if '__label__ko' in pred[0]:
-            lang = 'Kor'
+            Lang = 'Kor'
             idx = pred[0].index('__label__ko')
-            percent_kor = pred[1][idx]
+            p_kor = pred[1][idx]
+        # Normalize Percentage
+        percent_kor = p_kor / (p_kor+p_eng)
+        percent_eng = p_eng / (p_kor+p_eng)
 
-    if lang == 'Eng':
+    if Lang == 'Eng':
         model = eng_model
         tokenizer = eng_tokenizer
         if percent_eng==0: percent_eng=1
 
-    if lang == 'Kor':
+    if Lang == 'Kor':
         model = kor_model
         tokenizer = kor_tokenizer
         if percent_kor==0: percent_kor=1
         
 
     # [ output_2 ]
-    inputs = tokenized_data(tokenizer, text)
+    inputs = tokenized_data(tokenizer, Text)
     model.eval()
     with torch.no_grad():
         logits = model(input_ids=inputs['input_ids'], 
@@ -139,7 +142,7 @@ def builder(lang, text):
             output_analysis.append( (word, None) )
     
 
-    return [ {'Kor': percent_kor, 'Eng': percent_eng, 'Other': 1-(percent_kor+percent_eng)}, 
+    return [ {'Kor': percent_kor, 'Eng': percent_eng}, 
             {id2label[1]: output[0][1].item(), id2label[0]: output[0][0].item()}, 
             output_analysis ]
             
@@ -154,7 +157,7 @@ def builder(lang, text):
 
 
 
-demo = gr.Interface(builder, inputs=[gr.inputs.Dropdown(['Default', 'Eng', 'Kor']), "text"], 
+demo = gr.Interface(builder, inputs=[gr.inputs.Dropdown(['Default', 'Eng', 'Kor']), gr.Textbox(placeholder="리뷰를 입력하시오.")], 
                     outputs=[ gr.Label(num_top_classes=3, label='Lang'), 
                             gr.Label(num_top_classes=2, label='Result'),
                             gr.HighlightedText(label="Analysis", combine_adjacent=False)
@@ -162,8 +165,44 @@ demo = gr.Interface(builder, inputs=[gr.inputs.Dropdown(['Default', 'Eng', 'Kor'
                     # outputs='label',
                     title=title, description=description, examples=examples)
 
+with gr.Blocks() as demo1:
+    gr.Markdown(
+    """
+    <h1 align="center">
+    Movie Review Score Discriminator
+    </h1>
+    """)
+
+    with gr.Accordion("Open for More!"):
+        gr.Markdown(
+        """
+        내용은 아직 바꾸지 않았음 (형식만 참고)  
+        문제점 : 클리어 클릭이 원하는대로 안됨
+        It is a program that classifies whether it is positive or negative by entering movie reviews.  \
+        You can choose between the Korean version and the English version.  \
+        It also provides a version called ""Default"", which determines whether it is Korean or English and predicts it.
+        """)
+
+
+    with gr.Row():
+        with gr.Column():
+            inputs_1 = gr.inputs.Dropdown(['Default', 'Eng', 'Kor'])
+            inputs_2 = gr.Textbox(placeholder="리뷰를 입력하시오.")
+            with gr.Row():
+                btn2 = gr.Button("클리어")
+                btn = gr.Button("제출하기")
+        with gr.Column():
+            output_1 = gr.Label(num_top_classes=3, label='Lang')
+            output_2 = gr.Label(num_top_classes=2, label='Result')
+            output_3 = gr.HighlightedText(label="Analysis", combine_adjacent=False) \
+                .style(color_map={"+++": "#CF0000", "++": "#FF3232", "+": "#FFD4D4", "---": "#0004FE", "--": "#4C47FF", "-": "#BEBDFF"})
+    
+    btn.click(fn=builder, inputs=[inputs_1, inputs_2], outputs=[output_1, output_2, output_3])
+    gr.Examples(examples, inputs=[inputs_1, inputs_2])
+    
+
 
 if __name__ == "__main__":
     # print(examples)
     demo.launch()
-    # demo3.launch()
+    demo1.launch()