import requests
from bs4 import BeautifulSoup
import pandas as pd
import streamlit as st
import plotly.express as px

# 定義函數來獲取並處理台南市立醫院的數據
def get_tmh_data():
    url_tmh = "https://www.tmh.org.tw/tmh2016/ImpBD.aspx?Kind=2"
    response_tmh = requests.get(url_tmh)
    response_tmh.encoding = 'utf-8'

    soup_tmh = BeautifulSoup(response_tmh.text, 'html.parser')
    table_tmh = soup_tmh.find('table', {'class': 'RegTable'})

    data_tmh = []
    rows_tmh = table_tmh.find_all('tr')

    headers_tmh = [th.text.strip() for th in rows_tmh[0].find_all('th')]

    for row in rows_tmh[1:]:
        cols = row.find_all('td')
        cols = [col.text.strip() for col in cols]
        data_tmh.append(cols)

    df_tmh = pd.DataFrame(data_tmh, columns=headers_tmh)

    # 更改台南市立醫院的列名
    df_tmh = df_tmh.rename(columns={
        '病床種類': '病床類別',
        '床位別數': '病床數',
        '住院人數': '佔床數'
    })

    df_tmh['醫院'] = '台南市立醫院'
    return df_tmh

# 定義函數來獲取並處理奇美醫院的數據
def get_chimei_data():
    url_chimei = "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="
    response_chimei = requests.get(url_chimei)
    response_chimei.encoding = 'utf-8'

    soup_chimei = BeautifulSoup(response_chimei.text, 'html.parser')
    table_chimei = soup_chimei.find('table', {'id': 'DG1'})

    data_chimei = []
    rows_chimei = table_chimei.find_all('tr')

    headers_chimei = [th.text.strip() for th in rows_chimei[0].find_all('td')]

    for row in rows_chimei[1:]:
        cols = row.find_all('td')
        cols = [col.text.strip() for col in cols]
        data_chimei.append(cols)

    df_chimei = pd.DataFrame(data_chimei, columns=headers_chimei)

    # 更改奇美醫院的列名
    df_chimei = df_chimei.rename(columns={
        '總床數': '病床數'
    })

    df_chimei['醫院'] = '奇美醫院'
    return df_chimei

# 定義函數來獲取並處理成大醫院的數據
def get_ncku_data():
    url_ncku = "https://web.hosp.ncku.edu.tw/nckm/Bedstatus/BedStatus.aspx"
    response_ncku = requests.get(url_ncku)
    response_ncku.encoding = 'utf-8'

    soup_ncku = BeautifulSoup(response_ncku.text, 'html.parser')
    table_ncku = soup_ncku.find('table', {'id': 'GV_EmgInsure'})

    data_ncku = []
    rows_ncku = table_ncku.find_all('tr')

    headers_ncku = [th.text.strip() for th in rows_ncku[0].find_all('th')]

    for row in rows_ncku[1:]:
        cols = row.find_all('td')
        cols = [col.text.strip() for col in cols]
        data_ncku.append(cols)

    df_ncku = pd.DataFrame(data_ncku, columns=headers_ncku)
    df_ncku['醫院'] = '成大醫院'

    # 計算佔床率並添加到成大醫院的 DataFrame
    df_ncku['佔床率'] = (df_ncku['佔床數'].astype(int) / df_ncku['病床數'].astype(int) * 100).round(2).astype(str) + '%'

    # 將佔床率移動到倒數第二欄
    cols = df_ncku.columns.tolist()
    cols.insert(-1, cols.pop(cols.index('佔床率')))
    df_ncku = df_ncku[cols]

    return df_ncku

# 主程序
st.title("醫院病床狀況")

# 多項選擇下拉式選單，預設選項為"請選擇"
options = st.multiselect(
    "選擇要查看的醫院",
    ["台南市立醫院", "奇美醫院", "成大醫院"],
    default=[]
)

# 查詢按鈕
if st.button("查詢"):
    if not options:
        st.warning("請選擇至少一個醫院進行查詢。")
    else:
        selected_data = []

        if "台南市立醫院" in options:
            df_tmh = get_tmh_data()
            selected_data.append(df_tmh)

        if "奇美醫院" in options:
            df_chimei = get_chimei_data()
            selected_data.append(df_chimei)

        if "成大醫院" in options:
            df_ncku = get_ncku_data()
            selected_data.append(df_ncku)

        if selected_data:
            df_combined = pd.concat(selected_data, ignore_index=True)
            st.write(df_combined)
            
            # 美化病床類別分布直條圖
            fig_bar = px.bar(df_combined, x='病床類別', y='病床數', color='醫院', title='病床類別分布',
                             text='病床數', barmode='group',
                             color_discrete_sequence=px.colors.qualitative.Pastel)
            fig_bar.update_traces(texttemplate='%{text:.2s}', textposition='outside')
            fig_bar.update_layout(uniformtext_minsize=8, uniformtext_mode='hide',
                                  xaxis_title='病床類別', yaxis_title='病床數', 
                                  title_x=0.5, template='plotly_white')
            st.plotly_chart(fig_bar)
            
            # 病床類別分布圓餅圖
            fig_pie_beds = px.pie(df_combined, names='病床類別', values='病床數', title='病床類別分布',
                                  color_discrete_sequence=px.colors.qualitative.Pastel)
            st.plotly_chart(fig_pie_beds)
            
            # 空床數分布圓餅圖
            fig_pie_empty = px.pie(df_combined, names='病床類別', values='空床數', title='空床數分布',
                                   color_discrete_sequence=px.colors.qualitative.Pastel)
            st.plotly_chart(fig_pie_empty)