Create app.py

app.py

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+import streamlit as st
+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+import plotly.express as px
+
+def main():
+    st.set_page_config(page_title="奇美醫院病床占用率查詢", layout="wide")
+    
+    st.title("奇美醫院病床占用率查詢系統")
+    st.write("此應用程式顯示奇美醫院的即時病床占用情況")
+    
+    # 添加刷新按鈕
+    if st.button("刷新數據"):
+        st.cache_data.clear()
+    
+    # 獲取病床數據
+    df = get_hospital_bed_data()
+    
+    # 顯示數據
+    col1, col2 = st.columns([2, 3])
+    
+    with col1:
+        st.subheader("病床占用數據表")
+        st.dataframe(df, use_container_width=True)
+        
+        # 計算總數
+        total_beds = df['總床數'].sum()
+        total_occupied = df['佔床數'].sum()
+        total_available = df['空床數'].sum()
+        overall_rate = f"{(total_occupied / total_beds * 100):.2f}%" if total_beds > 0 else "0%"
+        
+        st.subheader("總計")
+        total_data = pd.DataFrame({
+            '項目': ['總病床數', '總佔床數', '總空床數', '整體佔床率'],
+            '數值': [total_beds, total_occupied, total_available, overall_rate]
+        })
+        st.dataframe(total_data, use_container_width=True, hide_index=True)
+    
+    with col2:
+        st.subheader("病床占用率視覺化")
+        
+        # 創建占用率條形圖
+        fig = px.bar(
+            df,
+            x='病床類別',
+            y='佔床數',
+            text='佔床率',
+            color='佔床率',
+            color_continuous_scale='RdYlGn_r',  # 紅(高)-黃-綠(低)色彩範圍
+            labels={'佔床數': '已占用病床數', '病床類別': '病床類型', '佔床率': '占用率'},
+            height=500
+        )
+        fig.update_layout(xaxis={'categoryorder': 'total descending'})
+        st.plotly_chart(fig, use_container_width=True)
+        
+        # 創建餅圖顯示空床/占床比例
+        pie_data = pd.DataFrame({
+            '狀態': ['已占用', '可用'],
+            '數量': [total_occupied, total_available]
+        })
+        pie_fig = px.pie(
+            pie_data, 
+            values='數量', 
+            names='狀態',
+            color='狀態',
+            color_discrete_map={'已占用': 'red', '可用': 'green'},
+            hole=0.4
+        )
+        st.plotly_chart(pie_fig, use_container_width=True)
+
+@st.cache_data(ttl=300)  # 資料快取5分鐘
+def get_hospital_bed_data():
+    """獲取奇美醫院病床占用數據"""
+    try:
+        # URL of the page to scrape
+        url = "https://www.chimei.org.tw/%E4%BD%94%E5%BA%8A%E7%8E%87%E6%9F%A5%E8%A9%A2/%E4%BD%94%E5%BA%8A%E7%8E%87%E6%9F%A5%E8%A9%A2.aspx?ihospital=10&ffloor="
+        
+        # Send HTTP request
+        response = requests.get(url)
+        response.encoding = 'utf-8'  # Set encoding to handle Chinese characters
+        
+        # Create BeautifulSoup object
+        soup = BeautifulSoup(response.text, 'html.parser')
+        
+        # Find the target table (DG1)
+        target_table = soup.find('table', {'id': 'DG1'})
+        
+        # Extract data from the table
+        data = []
+        if target_table:
+            rows = target_table.find_all('tr')
+            
+            # Skip the header row
+            for row in rows[1:]:
+                cols = row.find_all('td')
+                if len(cols) == 5:  # Ensure row has 5 columns
+                    bed_type = cols[0].text.strip()
+                    total_beds = int(cols[1].text.strip())
+                    occupied_beds = int(cols[2].text.strip())
+                    available_beds = int(cols[3].text.strip())
+                    occupancy_rate = cols[4].text.strip()
+                    
+                    data.append({
+                        '病床類別': bed_type,
+                        '總床數': total_beds,
+                        '佔床數': occupied_beds,
+                        '空床數': available_beds,
+                        '佔床率': occupancy_rate
+                    })
+        
+        # Create pandas DataFrame
+        df = pd.DataFrame(data)
+        return df
+    
+    except Exception as e:
+        st.error(f"獲取數據時發生錯誤: {e}")
+        return pd.DataFrame({
+            '病床類別': [],
+            '總床數': [],
+            '佔床數': [],
+            '空床數': [],
+            '佔床率': []
+        })
+
+if __name__ == "__main__":
+    main()