Create app.py

app.py

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+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+import folium
+from folium.plugins import MarkerCluster, HeatMap
+import plotly.graph_objects as go
+from geopy.geocoders import Nominatim
+import re
+import streamlit as st
+
+# Streamlit title and description
+st.title("Restaurant Data Extractor")
+st.write("Extracting restaurant data and displaying it on a map.")
+
+# 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", "Latitude", "Longitude", "Region"])
+
+# Initialize Nominatim geocoder
+geolocator = Nominatim(user_agent="my_app")
+
+# Function to extract region (區域) from the address using regex
+def extract_region(address):
+    match = re.search(r'(.*?)區|縣|市', address)
+    if match:
+        return match.group(0)
+    else:
+        return "Unknown"
+
+# 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:
+        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],
+        "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)
+
+# 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 a download button for the CSV file
+st.write(f"Data saved to {csv_file}")
+st.download_button(
+    label="Download restaurant data as CSV",
+    data=open(csv_file, "rb").read(),
+    file_name=csv_file,
+    mime="text/csv"
+)
+
+# Display a map using Folium in Streamlit
+st.subheader("Restaurant Locations Map")
+
+# 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)
+for index, row in df.iterrows():
+    if pd.notnull(row["Latitude"]) and pd.notnull(row["Longitude"]):
+        folium.Marker(
+            location=[row["Latitude"], row["Longitude"]],
+            popup=row["Store Name"],
+            tooltip=row["Address"]
+        ).add_to(marker_cluster)
+
+# Display the map in Streamlit
+st.components.v1.html(m._repr_html_(), height=600)
+
+# Optional: Display the DataFrame as a table
+st.subheader("Restaurant Data")
+st.dataframe(df)