Update app.py

app.py

@@ -6,25 +6,21 @@ from folium.plugins import MarkerCluster, HeatMap
 import plotly.graph_objects as go
 import plotly.express as px
 from geopy.geocoders import Nominatim
+from geopy.exc import GeocoderInsufficientPrivileges
 import re
 import streamlit as st
+import time
 
 # Streamlit title and description
-st.title("米其林餐廳指南爬蟲")
-st.write("Extract restaurant data, visualize with a Sunburst chart and bar chart, and display locations on a map with heatmap.")
+st.title("米其林餐廳指南爬蟲與分析")
+st.write("提取餐廳數據，可視化區域分佈，並在地圖上顯示位置和推薦度熱力圖。")
 
 # Read data from Google Sheets
 sheet_id = "1xUfnD1WCF5ldqECI8YXIko1gCpaDDCwTztL17kjI42U"
-df1 = pd.read_csv(f"https://docs.google.com/spreadsheets/d/{sheet_id}/export?format=csv")
-
-# Convert "網址" column to a Python list
-urls = df1["網址"].tolist()
-
-# Create a DataFrame to store all restaurant data
-df = pd.DataFrame(columns=["Store Name", "Address", "Phone", "Latitude", "Longitude", "Region"])
+df = pd.read_csv(f"https://docs.google.com/spreadsheets/d/{sheet_id}/export?format=csv")
 
 # Initialize Nominatim geocoder
-geolocator = Nominatim(user_agent="my_app")
+geolocator = Nominatim(user_agent="my_unique_app/3.0")
 
 # Function to extract region (區域) from the address using regex
 def extract_region(address):
@@ -34,147 +30,138 @@ def extract_region(address):
     else:
         return "Unknown"
 
-# Function to fetch and parse data
-def fetch_data():
-    global df
-    # Progress bar in Streamlit
-    progress_bar = st.progress(0)
-    total_urls = len(urls)
-
-    # Iterate through each URL
-    for idx, url in enumerate(urls):
-        response = requests.get(url)
-        soup = BeautifulSoup(response.content, "html.parser")
-
-        try:
-            store_name = soup.find("h2", class_="restaurant-details__heading--title").text.strip()
-        except AttributeError:
-            store_name = None
-
-        try:
-            address = soup.find("li", class_="restaurant-details__heading--address").text.strip()
-            region = extract_region(address)
-        except AttributeError:
-            address = None
-            region = "Unknown"
-        
-        # Try to extract phone number
-        try:
-            phone = soup.find("a", {"data-event": "CTA_tel"}).get("href").replace("tel:", "")
-        except AttributeError:
-            phone = None
-
-        try:
-            location = geolocator.geocode(address)
-            if location:
-                latitude = location.latitude
-                longitude = location.longitude
-            else:
-                latitude = None
-                longitude = None
-        except:
-            latitude = None
-            longitude = None
-
-        new_row = pd.DataFrame({
-            "Store Name": [store_name],
-            "Address": [address],
-            "Phone": [phone],
-            "Latitude": [latitude],
-            "Longitude": [longitude],
-            "Region": [region]
-        })
-
-        df = pd.concat([df, new_row], ignore_index=True)
-
-        # Update progress bar
-        progress_bar.progress((idx + 1) / total_urls)
-
-# Button to trigger data fetching
-if st.button("爬取餐廳資料"):
-    fetch_data()
-    
-    # Save the DataFrame to CSV with UTF-8 encoding, including latitude and longitude
-    csv_file = "restaurants_data.csv"
-    df.to_csv(csv_file, encoding="utf-8-sig", index=False)
-
-    # Display the DataFrame as a table at the top
-    st.subheader("Restaurant Data")
-    st.dataframe(df)
-
-    # Display download button for the CSV
-    st.download_button(
-        label="Download restaurant data as CSV",
-        data=open(csv_file, "rb").read(),
-        file_name=csv_file,
-        mime="text/csv"
-    )
-
-    # Group the data by region and sum the number of restaurants
-    region_group = df.groupby("Region").size().reset_index(name='Count')
-
-    # Plot Sunburst chart
-    sunburst = px.sunburst(
-        region_group,
-        path=['Region'],
-        values='Count',
-        title="Restaurant Distribution by Region",
-    )
-
-    sunburst.update_layout(
-        title_x=0.5,
-        title_font=dict(size=24, family="Arial"),
-        height=600,
-        margin=dict(t=50, b=50, l=50, r=50)
-    )
-    st.subheader("Restaurant Distribution by Region (Sunburst Chart)")
-    st.plotly_chart(sunburst)
-
-    # Plot bar chart with custom colors and labels
-    bar_chart = go.Figure(go.Bar(
-        x=region_group["Region"],
-        y=region_group["Count"],
-        text=region_group["Count"],
-        textposition='auto',
-        marker=dict(color=px.colors.qualitative.Set2)
-    ))
-
-    bar_chart.update_layout(
-        title="Restaurant Count by Region",
-        title_x=0.5,
-        title_font=dict(size=24, family="Arial"),
-        height=400,
-        margin=dict(t=50, b=50, l=50, r=50),
-        xaxis_title="Region",
-        yaxis_title="Number of Restaurants",
-        xaxis=dict(tickangle=-45)
-    )
-    st.subheader("Restaurant Count by Region (Bar Chart)")
-    st.plotly_chart(bar_chart)
-
-    # Display a map using Folium
-    st.subheader("Restaurant Locations Map with Heatmap")
-
-    # Create map centered around Tainan
-    m = folium.Map(location=[23.0, 120.2], zoom_start=12)
-
-    # Add marker cluster to the map
-    marker_cluster = MarkerCluster().add_to(m)
-    
-    # Prepare data for heatmap
-    heat_data = []
-    
-    for index, row in df.iterrows():
-        if pd.notnull(row["Latitude"]) and pd.notnull(row["Longitude"]):
-            folium.Marker(
-                location=[row["Latitude"], row["Longitude"]],
-                popup=f"{row['Store Name']} ({row['Phone']})",
-                tooltip=row["Address"]
-            ).add_to(marker_cluster)
-            heat_data.append([row["Latitude"], row["Longitude"]])
-
-    # Add heatmap layer
-    HeatMap(heat_data).add_to(m)
-
-    # Display the map in Streamlit
-    st.components.v1.html(m._repr_html_(), height=600)
+# Function to get latitude and longitude with caching
+@st.cache_data
+def get_lat_lon(district):
+    try:
+        location = geolocator.geocode(f"台南市{district}")
+        if location:
+            time.sleep(1)  # Delay to avoid rate limiting
+            return location.latitude, location.longitude
+    except GeocoderInsufficientPrivileges:
+        st.error("地理編碼器遇到權限問題，請稍後再試。")
+    return None, None
+
+# Apply geocoding to the dataframe
+df['Region'] = df['地址'].apply(extract_region)
+df['Latitude'], df['Longitude'] = zip(*df['Region'].apply(get_lat_lon))
+
+# Display the DataFrame as a table at the top
+st.subheader("餐廳數據")
+st.dataframe(df)
+
+# Group the data by region and sum the number of restaurants
+region_group = df.groupby("Region").agg({'Store Name': 'count', '推薦度': 'mean'}).reset_index()
+region_group.columns = ['Region', 'Count', 'Avg_Recommendation']
+
+# Create hierarchical data for the Sunburst chart
+region_group['Total'] = 'All Regions'  # Add a root level
+hierarchical_data = region_group[['Total', 'Region', 'Count']]
+
+# Plot interactive Sunburst chart
+sunburst = go.Figure(go.Sunburst(
+    labels=hierarchical_data['Region'].tolist() + hierarchical_data['Total'].tolist(),
+    parents=hierarchical_data['Total'].tolist() + [''],
+    values=hierarchical_data['Count'].tolist() + [hierarchical_data['Count'].sum()],
+    branchvalues="total",
+    hovertemplate='<b>%{label}</b><br>餐廳數量: %{value}<extra></extra>',
+    maxdepth=2,
+))
+
+sunburst.update_layout(
+    title="餐廳分佈（點擊可放大查看）",
+    title_x=0.5,
+    title_font=dict(size=24, family="Arial"),
+    height=600,
+    margin=dict(t=50, b=50, l=0, r=0)
+)
+
+# Add custom JavaScript for click events
+sunburst.update_layout(
+    updatemenus=[{
+        'type': 'buttons',
+        'showactive': False,
+        'buttons': [{
+            'label': '重置視圖',
+            'method': 'update',
+            'args': [{'visible': [True] * len(sunburst.data)},
+                     {'title': '餐廳分佈（點擊可放大查看）'}]
+        }]
+    }]
+)
+
+st.subheader("餐廳分佈（Sunburst 圖）")
+st.plotly_chart(sunburst, use_container_width=True)
+
+# Plot bar chart with custom colors and labels
+bar_chart = go.Figure(go.Bar(
+    x=region_group["Region"],
+    y=region_group["Count"],
+    text=region_group["Count"],
+    textposition='auto',
+    marker=dict(color=px.colors.qualitative.Set2)
+))
+
+bar_chart.update_layout(
+    title="各區域餐廳數量",
+    title_x=0.5,
+    title_font=dict(size=24, family="Arial"),
+    height=400,
+    margin=dict(t=50, b=50, l=50, r=50),
+    xaxis_title="區域",
+    yaxis_title="餐廳數量",
+    xaxis=dict(tickangle=-45)
+)
+st.subheader("各區域餐廳數量（條形圖）")
+st.plotly_chart(bar_chart)
+
+# 推薦度與地理位置的關聯性
+st.header("推薦度與地理位置的關聯性")
+
+# 區域性推薦度分析
+fig_bar = px.bar(region_group, x="Region", y="Avg_Recommendation", 
+                 title="不同區域的平均推薦度比較", 
+                 color_discrete_sequence=['#66CDAA'])
+st.plotly_chart(fig_bar)
+
+# Display a map using Folium
+st.subheader("餐廳位置地圖（含推薦度熱力圖）")
+
+# Create map centered around Tainan
+m = folium.Map(location=[23.0, 120.2], zoom_start=12)
+
+# Add marker cluster to the map
+marker_cluster = MarkerCluster().add_to(m)
+
+# Prepare data for heatmap
+heat_data = []
+
+for index, row in df.iterrows():
+    if pd.notnull(row["Latitude"]) and pd.notnull(row["Longitude"]):
+        folium.Marker(
+            location=[row["Latitude"], row["Longitude"]],
+            popup=f"{row['Store Name']} (推薦度: {row['推薦度']})",
+            tooltip=row["地址"]
+        ).add_to(marker_cluster)
+        heat_data.append([row["Latitude"], row["Longitude"], row["推薦度"]])
+
+# Add heatmap layer
+HeatMap(heat_data, radius=15, blur=10, max_zoom=1, name="推薦度熱力圖").add_to(m)
+
+# Add layer control
+folium.LayerControl().add_to(m)
+
+# Display the map in Streamlit
+st.components.v1.html(m._repr_html_(), height=600)
+
+# Save the DataFrame to CSV with UTF-8 encoding
+csv_file = "restaurants_data.csv"
+df.to_csv(csv_file, encoding="utf-8-sig", index=False)
+
+# Display download button for the CSV
+st.download_button(
+    label="下載餐廳數據 CSV 檔案",
+    data=open(csv_file, "rb").read(),
+    file_name=csv_file,
+    mime="text/csv"
+)