Update app.py

app.py

@@ -6,20 +6,19 @@ import json
 import time
 
 # --- Page Config ---
-st.set_page_config(page_title="Risk Command Center", page_icon="🚨", layout="wide")
+st.set_page_config(page_title="Risk Command Center Pro", page_icon="🚨", layout="wide")
 
-# --- Custom CSS (카드 디자인 & 대시보드 느낌) ---
+# --- CSS (카드 + 리포트 스타일) ---
 st.markdown("""
 <style>
     div[data-testid="stMetricValue"] { font-size: 24px; }
-    .risk-card { border: 1px solid #ddd; padding: 15px; border-radius: 10px; margin-bottom: 10px; }
-    .high-risk { border-left: 5px solid #ff4b4b; background-color: #ffeaea; }
-    .med-risk { border-left: 5px solid #ffa500; background-color: #fff5e6; }
-    .low-risk { border-left: 5px solid #09ab3b; background-color: #e6f9ec; }
+    .risk-card { background-color: #f9f9f9; padding: 15px; border-radius: 10px; border: 1px solid #ddd; margin-bottom: 10px; }
+    .report-box { background-color: #eef2f5; padding: 20px; border-radius: 10px; margin-top: 10px; }
+    .h-risk { color: #ff4b4b; font-weight: bold; }
 </style>
 """, unsafe_allow_html=True)
 
-# --- 국가 설정 (Dual Stream용) ---
+# --- 국가 설정 (Dual Stream) ---
 COUNTRY_CONFIG = {
     "🌏 Asia Pacific": {"South Korea": "kr-kr", "Japan": "jp-jp", "China": "cn-zh", "India": "in-en", "Australia": "au-en"},
     "🗽 North America": {"USA": "us-en", "Canada": "ca-en"},
@@ -27,79 +26,100 @@ COUNTRY_CONFIG = {
     "🕌 Middle East": {"Israel": "il-en", "Saudi Arabia": "sa-ar", "Turkey": "tr-tr"}
 }
 
+# --- 1. API Key Auto-Load (Secrets) ---
+try:
+    # Streamlit Cloud Settings > Secrets에 저장된 키를 가져옵니다.
+    api_key = st.secrets["HF_KEY"]
+except (FileNotFoundError, KeyError):
+    # 로컬 테스트거나 키가 없을 경우를 대비해 수동 입력창 남겨둠 (선택사항)
+    api_key = st.sidebar.text_input("HF Token (Secrets not found)", type="password")
+
 # --- Sidebar (Control Panel) ---
 with st.sidebar:
-    st.header("🎛️ Control Panel")
-    api_key = st.text_input("HF Token", type="password", help="Hugging Face API Token")
+    st.header("🎛️ Command Center")
+    if api_key:
+        st.success("🔐 Security Key Loaded")
     
-    st.subheader("Target Region")
-    region = st.selectbox("Continent", list(COUNTRY_CONFIG.keys()))
+    st.subheader("Target Scope")
+    region = st.selectbox("Region", list(COUNTRY_CONFIG.keys()))
     country = st.selectbox("Country", list(COUNTRY_CONFIG[region].keys()))
     region_code = COUNTRY_CONFIG[region][country]
     
     st.markdown("---")
-    refresh_btn = st.button("🔄 Refresh Dashboard", type="primary", use_container_width=True)
-    
-    st.info(f"Monitoring: {country}\nMode: Dual-Stream (En + Local)")
+    refresh_btn = st.button("🚀 Run Full Analysis", type="primary", use_container_width=True)
+    st.caption("Mode: Deep Intelligence (Global + Local)")
 
-# --- Main Dashboard Area ---
+# --- Main Logic ---
 st.title(f"🚨 Risk Command Center: {country}")
 
 if not api_key:
-    st.warning("⚠️ 사이드바에 Hugging Face Token을 입력하고 'Refresh'를 눌러주세요.")
+    st.error("🚫 HF_KEY not found in Secrets. Please add it in Streamlit Settings.")
     st.stop()
 
 if refresh_btn:
     client = InferenceClient(api_key=api_key)
     
-    # [Process 1] Data Collection (Global + Local)
+    # [Step 1] Dual-Stream Data Collection
     status_text = st.empty()
     progress_bar = st.progress(0)
     
     all_news = []
     try:
-        status_text.text("📡 Connecting to Global Satellites...")
+        status_text.text("📡 Scanning Global Spectrum...")
         with DDGS() as ddgs:
-            # Global Search
+            # 1. Global Stream
             for r in ddgs.news(f"{country} controversy", region="wt-wt", safesearch="off", max_results=5):
-                r['type'] = 'Global'
+                r['stream'] = 'Global 🌎'
                 all_news.append(r)
             progress_bar.progress(30)
             
-            # Local Search
+            # 2. Local Stream
             status_text.text(f"📡 Intercepting Local Signals ({region_code})...")
-            for r in ddgs.news(f"{country}", region=region_code, safesearch="off", max_results=6):
-                r['type'] = 'Local'
+            for r in ddgs.news(f"{country}", region=region_code, safesearch="off", max_results=7):
+                r['stream'] = 'Local 🏠'
                 all_news.append(r)
             progress_bar.progress(60)
             
     except Exception as e:
-        st.error(f"Data Collection Error: {e}")
+        st.error(f"Intel Collection Failed: {e}")
         st.stop()
 
     df = pd.DataFrame(all_news).drop_duplicates(subset=['title'])
     
-    # [Process 2] Qwen Multi-Issue Analysis
-    status_text.text("🧠 Qwen-72B is profiling top 3 risks...")
+    # [Step 2] Qwen Deep Analysis (One-Shot Complex Prompt)
+    status_text.text("🧠 Qwen-72B is processing Deep Intelligence Report...")
     
-    news_feed = "\n".join([f"[{row['type']}] {row['title']} ({row['date']})" for i, row in df.iterrows()])
+    news_feed = "\n".join([f"[{row['stream']}] {row['title']} ({row['source']})" for i, row in df.iterrows()])
     
+    # 프롬프트: 리스트(Cards)와 리포트(Deep Analysis)를 한 번에 요청
     system_prompt = f"""
-    You are a Head of Trust & Safety. Analyze the news for {country}.
-    Identify the TOP 3 DISTINCT social media risks.
-    
-    OUTPUT FORMAT: Strictly a JSON LIST of 3 objects.
-    [
-        {{
-            "title": "Short Headline (Max 10 words)",
-            "risk_score": 0-100,
-            "platform": "Primary Platform (TikTok/YouTube/Meta)",
-            "violation": "Main Violation Type (e.g. Hate Speech)",
-            "summary": "2 sentence briefing.",
-            "action": "One specific moderation action."
-        }},
-        ... (Total 3 items)
-    ]
+    You are a Strategic Risk Analyst. Analyze the news for {country}.
+    Generate a JSON object with two parts: "top_risks" (List of 3) and "deep_report" (Object).
+    
+    OUTPUT FORMAT (Strict JSON):
+    {{
+        "viral_velocity": 0-100 (Score based on number of sources and urgency),
+        "top_risks": [
+            {{
+                "title": "Short Headline",
+                "risk_score": 0-100,
+                "platform": "TikTok/YouTube/Meta",
+                "violation": "Hate/Violence/Misinfo...",
+                "action": "Brief Action"
+            }},
+            ... (3 items)
+        ],
+        "deep_report": {{
+            "executive_summary": "Comprehensive summary of the situation (3-4 sentences).",
+            "local_vs_global": "Contrast between local and global media tone.",
+            "platform_strategy": {{
+                "TikTok": "Specific moderation advice",
+                "YouTube": "Specific moderation advice",
+                "Meta": "Specific moderation advice"
+            }},
+            "critical_keywords": ["Tag1", "Tag2"]
+        }}
+    }}
     """
     
     try:
@@ -109,11 +129,12 @@ if refresh_btn:
                 {"role": "system", "content": system_prompt},
                 {"role": "user", "content": news_feed}
             ],
-            max_tokens=2000,
-            temperature=0.3
+            max_tokens=2500,
+            temperature=0.25
         )
         content = response.choices[0].message.content.replace("```json", "").replace("```", "").strip()
-        risks = json.loads(content)
+        data = json.loads(content)
+        
         progress_bar.progress(100)
         time.sleep(0.5)
         status_text.empty()
@@ -123,48 +144,67 @@ if refresh_btn:
         st.error(f"Analysis Failed: {e}")
         st.stop()
 
-    # --- [Dashboard View] ---
+    # --- [UI Render] ---
     
-    # 1. Top Level Metrics
-    total_risk = sum([r['risk_score'] for r in risks]) // 3
+    # [Section 1] KPI Board (화제성 증폭 지표 부활!)
     kpi1, kpi2, kpi3 = st.columns(3)
-    kpi1.metric("Avg Risk Level", f"{total_risk}/100", delta="High" if total_risk > 60 else "Normal", delta_color="inverse")
-    kpi2.metric("Active Signals", f"{len(df)} Articles")
-    kpi3.metric("Primary Threat", risks[0]['violation'])
-
-    st.divider()
-    st.subheader("🔥 Top 3 Critical Issues")
+    
+    # Velocity 게이지
+    vel_score = data['viral_velocity']
+    vel_color = "inverse" if vel_score > 70 else "normal"
+    vel_label = "🔥 Viral Outbreak" if vel_score > 80 else "📈 Climbing" if vel_score > 50 else "🟢 Stable"
+    
+    kpi1.metric("Viral Velocity", f"{vel_score}/100", vel_label, delta_color=vel_color)
+    kpi2.metric("Active Sources", f"{len(df)} Channels")
+    kpi3.metric("Primary Risk", data['top_risks'][0]['violation'])
 
-    # 2. 3-Column Card Layout
+    # [Section 2] 3-Card Overview
+    st.subheader("⚡ High-Priority Threats (Top 3)")
     cols = st.columns(3)
     
-    for i, risk in enumerate(risks):
+    for i, risk in enumerate(data['top_risks']):
         with cols[i]:
-            # 리스크 레벨에 따른 스타일링
             score = risk['risk_score']
-            color_class = "high-risk" if score >= 75 else "med-risk" if score >= 50 else "low-risk"
-            emoji = "🔴" if score >= 75 else "🟠" if score >= 50 else "🟢"
+            border_color = "#ff4b4b" if score >= 80 else "#ffa500" if score >= 50 else "#4caf50"
             
-            # 카드 디자인 (Container)
             with st.container(border=True):
-                st.markdown(f"**{emoji} Risk Score: {score}**")
-                st.markdown(f"### {risk['title']}")
-                st.caption(f"**{risk['platform']}** | {risk['violation']}")
-                st.markdown("---")
-                st.info(risk['summary'])
-                st.markdown(f"**🛡️ Action:** {risk['action']}")
+                st.markdown(f"<h3 style='color:{border_color}'>{risk['risk_score']} <span style='font-size:16px; color:gray'>/ 100</span></h3>", unsafe_allow_html=True)
+                st.markdown(f"**{risk['title']}**")
+                st.caption(f"{risk['platform']} | {risk['violation']}")
+                st.markdown(f"🛡️ *{risk['action']}*")
 
-    # 3. Data Table (Bottom)
+    # [Section 3] Deep Intelligence Report (상세 분석 부활!)
     st.divider()
-    with st.expander("📰 Raw Intelligence Feed (Evidence)", expanded=False):
-        st.dataframe(df[['type', 'title', 'source', 'date']], use_container_width=True, hide_index=True)
+    st.markdown("### 📑 Deep Intelligence Report")
+    
+    report = data['deep_report']
+    
+    # 탭으로 깔끔하게 정리
+    tab1, tab2, tab3, tab4 = st.tabs(["📝 Executive Summary", "⚖️ Local vs Global", "🛡️ Platform Strategy", "📰 Evidence"])
+    
+    with tab1:
+        st.info(report['executive_summary'])
+        st.markdown("#### 🚨 Critical Keywords")
+        st.write(" ".join([f"`#{k}`" for k in report['critical_keywords']]))
+        
+    with tab2:
+        st.success(f"💡 **Insight:** {report['local_vs_global']}")
+        st.caption("This analysis compares the tone of domestic media versus international coverage.")
+        
+    with tab3:
+        p_cols = st.columns(3)
+        with p_cols[0]:
+            st.markdown("**🎵 TikTok Strategy**")
+            st.warning(report['platform_strategy'].get('TikTok', '-'))
+        with p_cols[1]:
+            st.markdown("**▶️ YouTube Strategy**")
+            st.warning(report['platform_strategy'].get('YouTube', '-'))
+        with p_cols[2]:
+            st.markdown("**♾️ Meta Strategy**")
+            st.warning(report['platform_strategy'].get('Meta', '-'))
+            
+    with tab4:
+        st.dataframe(df[['stream', 'title', 'source', 'date']], use_container_width=True, hide_index=True)
 
 else:
-    # 초기 화면 (대기 상태)
-    st.info("👈 사이드바에서 'Refresh Dashboard' 버튼을 눌러 모니터링을 시작하세요.")
-    st.markdown("""
-    ### Dashboard Capabilities
-    * **Global + Local Dual Scan:** 영어권 뉴스(외신)와 현지 언어 뉴스를 동시 분석.
-    * **Top 3 Risk Clustering:** 단일 이슈가 아닌, 중요도 순 상위 3개 이슈 병렬 표출.
-    * **Dashboard View:** 카드형 UI로 한눈에 리스크 파악.
-    """)
+    st.info("👋 Ready to Scan. Press 'Run Full Analysis' in the sidebar.")