Update app.py

app.py

@@ -10,24 +10,42 @@ import numpy as np
 import pandas as pd
 from collections import Counter
 from scipy import stats
+import torch
 from wordcloud import WordCloud
 from topic_translator import translate_topics
 from nltk.tokenize import sent_tokenize, word_tokenize
 from nltk.sentiment import SentimentIntensityAnalyzer
+from sklearn.cluster import KMeans
+
+# GPU 설정
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 # NLTK 필요 데이터 다운로드
-nltk.download('punkt')
-nltk.download('vader_lexicon')
+try:
+    nltk.download('punkt', quiet=True)
+    nltk.download('vader_lexicon', quiet=True)
+except Exception as e:
+    print(f"NLTK 데이터 다운로드 중 오류 발생: {e}")
 
 SAMPLE_TEXT = """
 The three reportedly discussed the Stargate Project, a large-scale AI initiative led by OpenAI, SoftBank, and U.S. software giant Oracle. The project aims to invest $500 billion over the next four years in building new AI infrastructure in the U.S. The U.S. government has shown a strong commitment to the initiative, with President Donald Trump personally announcing it at the White House the day after his inauguration last month. If Samsung participates, the project will lead to a Korea-U.S.-Japan AI alliance.
 The AI sector requires massive investments and extensive resources, including advanced models, high-performance AI chips to power the models, and large-scale data centers to operate them. Nvidia and TSMC currently dominate the AI sector, but a partnership between Samsung, SoftBank, and OpenAI could pave the way for a competitive alternative.
-"""  
+"""
+
+# WBGDocTopic 초기화 시 device 지정
+clf = wbgtopic.WBGDocTopic(device=device)
 
-clf = wbgtopic.WBGDocTopic()
+def safe_process(func):
+    def wrapper(*args, **kwargs):
+        try:
+            return func(*args, **kwargs)
+        except Exception as e:
+            print(f"Error in {func.__name__}: {str(e)}")
+            return None
+    return wrapper
 
+@safe_process
 def analyze_text_sections(text):
-    # 문단별 분석
     sentences = sent_tokenize(text)
     sections = [' '.join(sentences[i:i+3]) for i in range(0, len(sentences), 3)]
     section_topics = []
@@ -38,33 +56,37 @@ def analyze_text_sections(text):
     
     return section_topics
 
+@safe_process
 def calculate_topic_correlations(topics):
-    # 주제 간 상관관계 계산
     topic_scores = {}
     for topic in topics:
         topic_scores[topic['label']] = topic['score_mean']
     
+    if len(topic_scores) < 2:
+        return np.array([[1]]), list(topic_scores.keys())
+    
     correlation_matrix = np.corrcoef(list(topic_scores.values()))
     return correlation_matrix, list(topic_scores.keys())
 
+@safe_process
 def perform_sentiment_analysis(text):
-    # 감성 분석
     sia = SentimentIntensityAnalyzer()
     sentences = sent_tokenize(text)
     sentiments = [sia.polarity_scores(sent) for sent in sentences]
     return pd.DataFrame(sentiments)
 
+@safe_process
 def create_topic_clusters(topics):
-    # 주제 군집화
-    from sklearn.cluster import KMeans
+    if len(topics) < 3:
+        return np.zeros(len(topics))
+    
     X = np.array([[t['score_mean'], t['score_std']] for t in topics])
-    kmeans = KMeans(n_clusters=3, random_state=42)
+    kmeans = KMeans(n_clusters=min(3, len(topics)), random_state=42)
     clusters = kmeans.fit_predict(X)
     return clusters
 
-
+@safe_process
 def create_main_charts(topics):
-    # 1. 기본 막대 차트
     bar_fig = go.Figure()
     bar_fig.add_trace(go.Bar(
         x=[t['label'] for t in topics],
@@ -72,9 +94,14 @@ def create_main_charts(topics):
         name='관련도',
         marker_color='rgb(55, 83, 109)'
     ))
-    bar_fig.update_layout(title='주제 분석 결과', height=500)
+    bar_fig.update_layout(
+        title='주제 분석 결과',
+        height=500,
+        xaxis_title='주제',
+        yaxis_title='관련도 (%)',
+        template='plotly_white'
+    )
     
-    # 2. 레이더 차트
     radar_fig = go.Figure()
     radar_fig.add_trace(go.Scatterpolar(
         r=[t['score'] for t in topics],
@@ -82,10 +109,15 @@ def create_main_charts(topics):
         fill='toself',
         name='주제 분포'
     ))
-    radar_fig.update_layout(title='주제 레이더 차트')
+    radar_fig.update_layout(
+        title='주제 레이더 차트',
+        height=500,
+        template='plotly_white'
+    )
     
     return bar_fig, radar_fig
 
+@safe_process
 def create_correlation_heatmap(corr_matrix, labels):
     fig = go.Figure(data=go.Heatmap(
         z=corr_matrix,
@@ -93,72 +125,136 @@ def create_correlation_heatmap(corr_matrix, labels):
         y=labels,
         colorscale='Viridis'
     ))
-    fig.update_layout(title='주제 간 상관관계')
+    fig.update_layout(
+        title='주제 간 상관관계',
+        height=500,
+        template='plotly_white'
+    )
     return fig
 
+@safe_process
 def create_topic_evolution(section_topics):
+    if not section_topics or len(section_topics) == 0:
+        return go.Figure()
+
     fig = go.Figure()
     for topic in section_topics[0]:
-        topic_scores = [topics[topic['label']]['score_mean'] 
-                       for topics in section_topics]
-        fig.add_trace(go.Scatter(
-            x=list(range(len(section_topics))),
-            y=topic_scores,
-            name=topic['label'],
-            mode='lines+markers'
-        ))
-    fig.update_layout(title='주제 변화 추이')
+        try:
+            topic_scores = [topics[topic['label']]['score_mean'] 
+                          for topics in section_topics]
+            fig.add_trace(go.Scatter(
+                x=list(range(len(section_topics))),
+                y=topic_scores,
+                name=topic['label'],
+                mode='lines+markers'
+            ))
+        except Exception as e:
+            print(f"Error processing topic {topic['label']}: {e}")
+            continue
+    
+    fig.update_layout(
+        title='주제 변화 추이',
+        xaxis_title='섹션',
+        yaxis_title='관련도',
+        height=500,
+        template='plotly_white'
+    )
     return fig
 
+@safe_process
 def create_confidence_gauge(topics):
     fig = go.Figure()
-    for topic in topics:
+    for i, topic in enumerate(topics):
         fig.add_trace(go.Indicator(
             mode="gauge+number",
             value=topic['confidence'],
             title={'text': topic['label']},
-            domain={'row': 0, 'column': 0}
+            domain={'row': 0, 'column': i, 'x': [i/len(topics), (i+1)/len(topics)]}
         ))
-    fig.update_layout(grid={'rows': 1, 'columns': len(topics)})
+    fig.update_layout(
+        grid={'rows': 1, 'columns': len(topics)},
+        height=400,
+        template='plotly_white'
+    )
     return fig
 
-
-def process_all_analysis(text):
-    # 기본 주제 분석
-    raw_results = clf.suggest_topics(text)
-    topics = process_results(raw_results)
+@safe_process
+def process_results(results):
+    if not results or not results[0]:
+        return []
     
-    # 추가 분석
-    section_topics = analyze_text_sections(text)
-    corr_matrix, labels = calculate_topic_correlations(topics)
-    sentiments = perform_sentiment_analysis(text)
-    clusters = create_topic_clusters(topics)
+    topics = results[0]
+    top_topics = sorted(topics, key=lambda x: x['score_mean'], reverse=True)[:5]
     
-    # 차트 생성
-    bar_chart, radar_chart = create_main_charts(topics)
-    heatmap = create_correlation_heatmap(corr_matrix, labels)
-    evolution_chart = create_topic_evolution(section_topics)
-    gauge_chart = create_confidence_gauge(topics)
+    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 {
-        'topics': topics,
-        'bar_chart': bar_chart,
-        'radar_chart': radar_chart,
-        'heatmap': heatmap,
-        'evolution': evolution_chart,
-        'gauge': gauge_chart,
-        'sentiments': sentiments.to_dict(),
-        'clusters': clusters.tolist()
-    }
+    return formatted_topics
+
+@spaces.GPU(enable_queue=True, duration=50)
+def process_all_analysis(text):
+    try:
+        # 기본 주제 분석
+        raw_results = clf.suggest_topics(text)
+        topics = process_results(raw_results)
+        
+        # 추가 분석
+        section_topics = analyze_text_sections(text)
+        corr_matrix, labels = calculate_topic_correlations(topics)
+        sentiments = perform_sentiment_analysis(text)
+        clusters = create_topic_clusters(topics)
+        
+        # 차트 생성
+        bar_chart, radar_chart = create_main_charts(topics)
+        heatmap = create_correlation_heatmap(corr_matrix, labels)
+        evolution_chart = create_topic_evolution(section_topics)
+        gauge_chart = create_confidence_gauge(topics)
+        
+        return {
+            'topics': topics,
+            'bar_chart': bar_chart,
+            'radar_chart': radar_chart,
+            'heatmap': heatmap,
+            'evolution': evolution_chart,
+            'gauge': gauge_chart,
+            'sentiments': sentiments.to_dict() if sentiments is not None else {},
+            'clusters': clusters.tolist() if clusters is not None else []
+        }
+    except Exception as e:
+        print(f"Analysis error: {str(e)}")
+        return {
+            'error': str(e),
+            'topics': [],
+            'bar_chart': go.Figure(),
+            'radar_chart': go.Figure(),
+            'heatmap': go.Figure(),
+            'evolution': go.Figure(),
+            'gauge': go.Figure(),
+            'sentiments': {},
+            'clusters': []
+        }
 
+# Gradio 인터페이스
 with gr.Blocks(title="고급 문서 주제 분석기") as demo:
     gr.Markdown("## 📊 고급 문서 주제 분석기")
+    gr.Markdown("문서를 입력하면 다양한 분석 결과를 시각화���여 보여줍니다.")
     
     with gr.Row():
-        text = gr.Textbox(value=SAMPLE_TEXT, label="분석할 텍스트", lines=5)
+        text = gr.Textbox(
+            value=SAMPLE_TEXT,
+            label="분석할 텍스트",
+            placeholder="여기에 분석할 텍스트를 입력하세요",
+            lines=8
+        )
     
     with gr.Row():
-        submit_btn = gr.Button("분석 시작")
+        submit_btn = gr.Button("분석 시작", variant="primary")
     
     with gr.Tabs():
         with gr.TabItem("주요 분석"):
@@ -186,4 +282,11 @@ with gr.Blocks(title="고급 문서 주제 분석기") as demo:
         outputs=[output, plot1, plot2, plot3, plot4, plot5, plot6]
     )
 
-demo.launch(debug=True)    
+if __name__ == "__main__":
+    demo.queue(concurrency_count=1)
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,
+        debug=True
+    )