Create app.py

app.py

@@ -0,0 +1,131 @@
+import streamlit as st
+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+import plotly.express as px
+import base64
+
+# Function to set background image
+def set_background(png_file):
+    with open(png_file, "rb") as f:
+        data = f.read()
+    encoded = base64.b64encode(data).decode()
+    st.markdown(
+        f"""
+        <style>
+        .stApp {{
+            background: url(data:image/png;base64,{encoded});
+            background-size: cover;
+        }}
+        </style>
+        """,
+        unsafe_allow_html=True
+    )
+
+# Set the background image
+set_background('CAT.png')
+
+# Title of the app
+st.title("寵物醫院評分查詢")
+
+# User input for minimum rating
+min_rating = st.slider("請輸入想查詢的最低評分:", 1.0, 5.0, 3.5)
+
+# List of URLs to scrape
+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/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/c001138.html",
+    "https://www.tw-animal.com/pet/171211/c000484.html",
+    "https://www.tw-animal.com/pet/171211/c001089.html",
+    "https://www.tw-animal.com/pet/171211/c001252.html"
+]
+
+# Create an empty list to store the extracted data
+data_list = []
+
+# Scrape data when the button is pressed
+if st.button('開始爬取資料'):
+    st.write("正在爬取資料，請稍候...")
+    
+    # Loop through each URL and extract the data
+    for url in urls:
+        response = requests.get(url)
+        soup = BeautifulSoup(response.content, 'html.parser')
+
+        # Extract data
+        title = soup.find('h1', class_='t-intro__title').get_text(strip=True)
+        phone = soup.find('a', class_='t-font-large').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))
+
+        # Append the data to the list if rating meets the threshold
+        if rating >= min_rating:
+            data_list.append({
+                "標題": title,
+                "手機": phone,
+                "地址": address,
+                "評分": rating
+            })
+
+    # If data was scraped successfully
+    if data_list:
+        df1 = pd.DataFrame(data_list)
+
+        # Extract the region from the address (assuming region is part of the address)
+        df1['區域'] = df1['地址'].apply(lambda x: x.split()[0])
+
+        # Group by region and merge hospitals in the same region
+        grouped_df = df1.groupby('區域').agg({
+            '標題': lambda x: ' | '.join(x),
+            '手機': lambda x: ' | '.join(x),
+            '地址': lambda x: ' | '.join(x),
+            '評分': 'mean'  # Aggregation for average rating
+        }).reset_index()
+
+        # Display the dataframe
+        st.dataframe(df1)
+
+        # Display Plotly bar chart
+        bar_fig = px.bar(grouped_df, x='區域', y='評分', title="各區域寵物醫院統計", labels={'評分':'平均評分', '區域':'區域'})
+        st.plotly_chart(bar_fig)
+
+        # Display Plotly pie chart
+        pie_fig = px.pie(grouped_df, names='區域', values='評分', title="各區域寵物醫院比例")
+        st.plotly_chart(pie_fig)
+
+        # Sending notification to LINE
+        if st.button('發送前五筆資料到Line'):
+            msg = df1[:5].to_string(index=False)
+
+            token = "DzMHaXosskpjtGuIjuB7NIcQ5TIoptLz7l7VYzV3Wp4"  # Replace with your LINE Notify token
+
+            # Send message to LINE
+            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)
+
+            send_line_notify(token, msg)
+            st.success('資料已成功發送到 Line!')
+    else:
+        st.warning('沒有符合條件的資料。')
+