Code Editor for app.py

import streamlit as st
import requests
from bs4 import BeautifulSoup
import pandas as pd
import time
from datetime import datetime

# Initialize cities data
CITIES = {
    'New York City': {'country': 'United States', 'region': 'North America', 'coords': '40.7128,-74.0060'},
    'Tokyo': {'country': 'Japan', 'region': 'East Asia', 'coords': '35.6762,139.6503'},
    'São Paulo': {'country': 'Brazil', 'region': 'South America', 'coords': '-23.5505,-46.6333'},
    'Lagos': {'country': 'Nigeria', 'region': 'Africa', 'coords': '6.5244,3.3792'},
    'Paris': {'country': 'France', 'region': 'Western Europe', 'coords': '48.8566,2.3522'},
    'Delhi': {'country': 'India', 'region': 'South Asia', 'coords': '28.6139,77.2090'},
    'Sydney': {'country': 'Australia', 'region': 'Oceania', 'coords': '-33.8688,151.2093'},
    'Dubai': {'country': 'United Arab Emirates', 'region': 'Middle East', 'coords': '25.2048,55.2708'},
    'London': {'country': 'United Kingdom', 'region': 'Western Europe', 'coords': '51.5074,-0.1278'},
    'Mexico City': {'country': 'Mexico', 'region': 'Central America', 'coords': '19.4326,-99.1332'}
}

st.title('Global Cities Dashboard')

# Function to get weather data
@st.cache_data(ttl=1800)  # Cache for 30 minutes
def get_weather(coords):
    # NOTE: Using corsproxy.io because we're in a WASM environment. If running locally,
    # you can remove the corsproxy.io prefix.
    base_url = "https://corsproxy.io/?https://api.open-meteo.com/v1/forecast"
    params = {
        "latitude": coords.split(',')[0],
        "longitude": coords.split(',')[1],
        "current_weather": True,
    }
    
    try:
        response = requests.get(base_url, params=params)
        response.raise_for_status()
        return response.json()['current_weather']
    except Exception as e:
        st.error(f"Error fetching weather data: {str(e)}")
        return None

# Function to get city info from Wikipedia
@st.cache_data(ttl=86400)  # Cache for 24 hours
def get_city_info(city):
    # NOTE: Using corsproxy.io because we're in a WASM environment.
    base_url = f"https://corsproxy.io/?https://en.wikipedia.org/wiki/{city.replace(' ', '_')}"
    
    try:
        response = requests.get(base_url)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        
        # Extract population and GDP info from infobox
        infobox = soup.find('table', {'class': 'infobox'})
        if infobox:
            text = infobox.get_text()
            
            # Simple extraction - in real application would need more robust parsing
            population = "Population data unavailable"
            gdp = "GDP data unavailable"
            
            for row in infobox.find_all('tr'):
                if 'Population' in str(row):
                    population = row.find_all('td')[-1].get_text().strip()
                if 'GDP' in str(row):
                    gdp = row.find_all('td')[-1].get_text().strip()
            
            return {'population': population, 'gdp': gdp}
    except Exception as e:
        st.error(f"Error fetching city info: {str(e)}")
    
    return {'population': 'Data unavailable', 'gdp': 'Data unavailable'}

# Create tabs for each region
regions = sorted(list(set(city['region'] for city in CITIES.values())))
tabs = st.tabs(regions)

# Organize cities by region
cities_by_region = {region: [] for region in regions}
for city, info in CITIES.items():
    cities_by_region[info['region']].append(city)

# Display data for each region
for region, tab in zip(regions, tabs):
    with tab:
        st.header(region)
        
        for city in cities_by_region[region]:
            st.subheader(city)
            col1, col2 = st.columns(2)
            
            with col1:
                # Weather information
                weather = get_weather(CITIES[city]['coords'])
                if weather:
                    st.metric("Temperature", f"{weather['temperature']}°C")
                    st.metric("Wind Speed", f"{weather['windspeed']} km/h")
            
            with col2:
                # City information
                info = get_city_info(city)
                st.write("**Population:**", info['population'])
                st.write("**GDP Info:**", info['gdp'])
            
            st.divider()

# Add timestamp
st.sidebar.write("Last updated:", datetime.now().strftime("%Y-%m-%d %H:%M:%S"))