Update app.py

app.py

@@ -1,35 +1,65 @@
 import json
 import gradio as gr
 import spaces
-
 import wbgtopic
+from topic_translator import translate_topics  # 새로 만들 파일입니다
 
-SAMPLE_TEXT = """A growing literature attributes gender inequality in labor market outcomes in part to the reduction in female labor supply after childbirth, the child penalty. However, if social norms constrain married women’s activities outside the home, then marriage can independently reduce employment, even in the absence childbearing. Given the correlation in timing between childbirth and marriage, conventional estimates of child penalties will conflate these two effects. The paper studies the marriage penalty in South Asia, a context featuring conservative gender norms and low female labor force participation. The study introduces a split-sample, pseudo-panel approach that allows for the separation of marriage and child penalties even in the absence of individual-level panel data. Marriage reduces women’s labor force participation in South Asia by 12 percentage points, whereas the marginal penalty of childbearing is small. Consistent with the central roles of both opportunity costs and social norms, the marriage penalty is smaller among cohorts with higher education and less conservative gender attitudes."""
+SAMPLE_TEXT = """A growing literature attributes gender inequality in labor market outcomes in part to the reduction in female labor supply after childbirth, the child penalty..."""
 
 clf = wbgtopic.WBGDocTopic()
 
+def process_results(results):
+    # 결과를 보기 좋게 가공
+    if not results or not results[0]:
+        return []
+    
+    # 첫 번째 문서의 결과만 사용 (단일 문서 기준)
+    topics = results[0]
+    
+    # 상위 5개 주제만 선택
+    top_topics = sorted(topics, key=lambda x: x['score_mean'], reverse=True)[:5]
+    
+    # 각 주제의 점수를 퍼센트로 변환하고 한글로 번역
+    formatted_topics = []
+    for topic in top_topics:
+        formatted_topic = {
+            'label': translate_topics.get(topic['label'], topic['label']),
+            'score': round(topic['score_mean'] * 100, 1),
+            'confidence': round((1 - topic['score_std']) * 100, 1)
+        }
+        formatted_topics.append(formatted_topic)
+    
+    return formatted_topics
 
 @spaces.GPU(enable_queue=True, duration=50)
 def fn(inputs):
-    return clf.suggest_topics(inputs)
-
+    raw_results = clf.suggest_topics(inputs)
+    return process_results(raw_results)
 
-with gr.Blocks(title="WBG-Document-Topic-Classifier") as demo:
-
-    text = gr.Textbox(
-        value=SAMPLE_TEXT, label="Text", placeholder="Enter your text here"
-    )
-
-    output = gr.JSON(label="Suggested topics")
-    submit_btn = gr.Button("Submit")
-
-    # Submitting
-    text.submit(
-        fn=fn, inputs=[text], outputs=output
-    )
+# Gradio 인터페이스 생성
+with gr.Blocks(title="문서 주제 분석기") as demo:
+    gr.Markdown("## 📚 문서 주제 분석기")
+    gr.Markdown("문서를 입력하면 관련된 주제들을 분석하여 보여줍니다.")
+    
+    with gr.Row():
+        text = gr.Textbox(
+            value=SAMPLE_TEXT,
+            label="분석할 텍스트",
+            placeholder="여기에 분석할 텍스트를 입력하세요",
+            lines=5
+        )
+    
+    with gr.Row():
+        submit_btn = gr.Button("분석 시작", variant="primary")
+    
+    with gr.Row():
+        output = gr.JSON(label="분석 결과")
+    
+    # 이벤트 연결
     submit_btn.click(
-        fn=fn, inputs=[text], outputs=output
+        fn=fn,
+        inputs=[text],
+        outputs=output
     )
 
-demo.queue(default_concurrency_limit=5)
-demo.launch(debug=True)
+demo.launch(debug=True)