import streamlit as st
import requests
from bs4 import BeautifulSoup
import pandas as pd
import base64
import plotly.express as px

def set_background(image_file):
    with open(image_file, "rb") as image_file:
        encoded_string = base64.b64encode(image_file.read()).decode()
    st.markdown(
        f"""
        <style>
        .stApp {{
            background-image: url(data:image/png;base64,{encoded_string});
            background-size: cover;
            background-position: center top;
            padding-top: 100px;
        }}
        </style>
        """,
        unsafe_allow_html=True
    )

# 設定背景圖片
set_background('ddog.png')

# Streamlit app 標題
st.title('寵物診所資訊爬蟲')

# 網址列表
urls = [
    'https://www.tw-animal.com/pet/171211/c000196.html',
    'https://www.tw-animal.com/pet/171211/c000186.html',
    'https://www.tw-animal.com/pet/171211/c000081.html',
    'https://www.tw-animal.com/pet/171211/c000848.html',
    'https://www.tw-animal.com/pet/171211/c000045.html',
    'https://www.tw-animal.com/pet/171211/c001166.html',
    'https://www.tw-animal.com/pet/171211/c000773.html',
    'https://www.tw-animal.com/pet/171211/c001038.html',
    'https://www.tw-animal.com/pet/171211/c000741.html',
    'https://www.tw-animal.com/pet/171211/c001451.html',
    'https://www.tw-animal.com/pet/171211/c000102.html',
    'https://www.tw-animal.com/pet/171211/c000757.html',
    'https://www.tw-animal.com/pet/171211/c000703.html',
    'https://www.tw-animal.com/pet/171211/c000481.html',
    'https://www.tw-animal.com/pet/171211/c000971.html',
    'https://www.tw-animal.com/pet/171211/c000187.html',
    'https://www.tw-animal.com/pet/171211/c001357.html',
    'https://www.tw-animal.com/pet/171211/c001065.html',
    'https://www.tw-animal.com/pet/171211/c000165.html',
    'https://www.tw-animal.com/pet/171211/c000217.html',
    'https://www.tw-animal.com/pet/171211/c000802.html',
    'https://www.tw-animal.com/pet/171211/c001034.html',
    'https://www.tw-animal.com/pet/171211/c001453.html'
]

# 讓使用者輸入評分門檻
min_rating = st.slider("請選擇想篩選的最低評分", 0.0, 5.0, 4.5, 0.1)

# 當使用者按下「開始爬蟲」按鈕時開始抓取資料
if st.button('開始爬蟲'):
    all_data = []
    progress_bar = st.progress(0)
    status_text = st.empty()

    # 遍歷每個網址並抓取資料
    for i, url in enumerate(urls):
        progress = int((i + 1) / len(urls) * 100)
        progress_bar.progress(progress)
        status_text.text(f'正在處理第 {i+1} 個網址，共 {len(urls)} 個')

        response = requests.get(url)
        response.encoding = 'utf-8'
        soup = BeautifulSoup(response.text, 'html.parser')

        title = soup.find('h1', class_='t-intro__title').get_text(strip=True)
        phone = soup.find('a', href=lambda href: href and href.startswith('tel:')).get_text(strip=True)
        address = soup.find('a', class_='t-font-medium').get_text(strip=True)
        rating = float(soup.find('span', class_='t-intro__recommand').get_text(strip=True))
        
        # 提取地址中的區名
        district = address.split('區')[0] + '區' if '區' in address else '其他'

        # 只將符合評分條件的資料加入列表
        if rating >= min_rating:
            all_data.append({
                '標題': title,
                '手機': phone,
                '地址': address,
                '評分': rating,
                '區': district
            })

    # 轉換為 DataFrame
    df = pd.DataFrame(all_data)

    # 如果有符合條件的資料，顯示並繪圖
    if not df.empty:
        st.dataframe(df)

        # 以每個區的診所數量繪製動態圖表
        district_counts = df['區'].value_counts().reset_index()
        district_counts.columns = ['區', '診所數量']

        fig = px.bar(district_counts, x='區', y='診所數量', title='各區寵物診所數量')
        st.plotly_chart(fig)

        # 提供 CSV 下載功能
        csv = df.to_csv(index=False)
        st.download_button(
            label="下載 CSV 檔案",
            data=csv,
            file_name="pet_clinics.csv",
            mime="text/csv",
        )
    else:
        st.write(f"沒有找到評分大於或等於 {min_rating} 的資料。")

    # 清空進度條和狀態訊息
    progress_bar.empty()
    status_text.empty()

# LINE Notify 部分
st.header('傳送至 LINE Notify')
token = st.text_input("請輸入 LINE Notify 權杖")
if st.button('傳送至 LINE'):
    if 'df' in locals() and not df.empty:
        msg = df.to_string(index=False)
        
        def send_line_notify(token, msg):
            headers = {
                "Authorization": "Bearer " + token,
                "Content-Type": "application/x-www-form-urlencoded"
            }
            params = {
                "message": msg
            }
            r = requests.post("https://notify-api.line.me/api/notify", headers=headers, params=params)
            return r.status_code

        status_code = send_line_notify(token, msg)
        if status_code == 200:
            st.success('成功傳送至 LINE Notify!')
        else:
            st.error('傳送失敗，請檢查您的權杖是否正確。')
    else:
        st.warning('沒有資料可以傳送，請先執行爬蟲。')