Update app.py

app.py

@@ -44,12 +44,9 @@ def scrape_booking_hotel():
 
         for hotel in hotel_cards:
             try:
-                name_elem = hotel.find('div', {'data-testid': 'title', 'class': 'f6431b446c'})
+                name_elem = hotel.find('div', {'data-testid': 'title'})
                 name = name_elem.text.strip() if name_elem else "無資料"
-                price_elem = hotel.find('span', {
-                    'data-testid': 'price-and-discounted-price',
-                    'class': 'f6431b446c'
-                })
+                price_elem = hotel.find('span', {'data-testid': 'price-and-discounted-price'})
                 price = price_elem.text.strip() if price_elem else "無資料"
                 price = price.replace('TWD', '').replace(' ', '').replace(',', '').strip()
                 rating_container = hotel.find('div', {'class': 'a3b8729ab1'})
@@ -57,9 +54,9 @@ def scrape_booking_hotel():
                 rating = rating_elem.text.strip() if rating_elem else "無評分"
                 description_elem = hotel.find('div', {'data-testid': 'recommended-units'})
                 if description_elem:
-                    room_type = description_elem.find('h4', {'class': 'abf093bdfe'})
+                    room_type = description_elem.find('h4')
                     room_type = room_type.text.strip() if room_type else ""
-                    bed_info = description_elem.find('div', {'class': 'abf093bdfe'})
+                    bed_info = description_elem.find('div')
                     bed_info = bed_info.text.strip() if bed_info else ""
                     cancellation = description_elem.find('strong', text='可免費取消')
                     cancellation = "可免費取消" if cancellation else ""
@@ -89,15 +86,14 @@ def clean_rating(x):
 
 def create_price_rating_scatter(df):
     if df.empty:
-        st.warning("沒有可視化的有效數據。請檢查輸入資料。")
-        return None  # 無數據時返回空
-
+        st.warning("數據為空，無法生成圖表。")
+        return None
     fig = px.scatter(
         df,
         x='價格',
         y='評分',
         text='飯店名稱',
-        size='價格',  # 點的大小根據價格變化
+        size='價格',  # 點大小基於價格
         color='評分',
         title='台南飯店價格與評分關係圖',
         labels={'價格': '房價 (TWD)', '評分': '評分 (0-10)'}
@@ -106,6 +102,9 @@ def create_price_rating_scatter(df):
     return fig
 
 def create_price_distribution(df):
+    if df.empty:
+        st.warning("數據為空，無法生成圖表。")
+        return None
     fig = go.Figure()
     fig.add_trace(go.Histogram(
         x=df['價格'],
@@ -135,7 +134,7 @@ if mode == "資料爬取":
     if st.button("開始爬取"):
         df = scrape_booking_hotel()
         if not df.empty:
-            st.dataframe(df)
+            st.dataframe(df)  # 顯示數據
             df.to_csv('booking_hotels_tainan.csv', index=False, encoding='utf-8-sig')
             st.success("資料爬取成功，已儲存至 booking_hotels_tainan.csv")
         else:
@@ -145,10 +144,8 @@ elif mode == "資料視覺化":
     st.header("分析與視覺化")
     try:
         df = pd.read_csv('booking_hotels_tainan.csv', encoding='utf-8-sig')
-        
-        # 清理數據
         df['價格'] = pd.to_numeric(df['價格'], errors='coerce')  # 無效值轉為 NaN
-        df['評分'] = df['評分'].apply(clean_rating)  # 清理評分數據
+        df['評分'] = df['評分'].apply(clean_rating)  # 清理評分
         df = df.dropna(subset=['價格'])  # 移除價格為 NaN 的行
 
         # 顯示數據摘要
@@ -156,8 +153,12 @@ elif mode == "資料視覺化":
         st.write(f"價格缺失值數量：{df['價格'].isna().sum()}")
 
         # 繪製圖表
-        st.plotly_chart(create_price_rating_scatter(df))
-        st.plotly_chart(create_price_distribution(df))
+        scatter_fig = create_price_rating_scatter(df)
+        if scatter_fig:
+            st.plotly_chart(scatter_fig)
+        dist_fig = create_price_distribution(df)
+        if dist_fig:
+            st.plotly_chart(dist_fig)
     except Exception as e:
         st.error(f"讀取或分析資料時發生錯誤：{e}")