Code Editor for app.py

import streamlit as st
import pandas as pd
import plotly.express as px
from PIL import Image
import requests
import io
import json

st.set_page_config(page_title="Real Estate Metrics", layout="wide")

# NOTE: Using corsproxy.io because we're in a WASM environment. If running locally,
# you can remove the corsproxy.io prefix.
ZILLOW_DATA_URL = "https://corsproxy.io/?https://files.zillowstatic.com/research/public_csvs/zhvi/Metro_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv"

def load_zillow_data():
    try:
        df = pd.read_csv(ZILLOW_DATA_URL)
        # Melt the dataframe to convert date columns to rows
        id_vars = [col for col in df.columns if not col.startswith('20')]
        df_melted = df.melt(id_vars=id_vars, var_name='Date', value_name='Price')
        df_melted['Date'] = pd.to_datetime(df_melted['Date'])
        return df_melted
    except Exception as e:
        st.error(f"Error loading Zillow data: {str(e)}")
        return None

def main():
    st.title("Multi-Family Real Estate Market Analysis")
    
    # Sidebar filters
    st.sidebar.header("Filters")
    
    # Load data
    df = load_zillow_data()
    
    if df is not None:
        # Get unique regions
        regions = sorted(df['RegionName'].unique())
        selected_region = st.sidebar.selectbox("Select Region", regions)
        
        # Create two columns for the layout
        col1, col2 = st.columns([2, 1])
        
        with col1:
            st.subheader("Price Trends")
            # Filter data for selected region
            region_data = df[df['RegionName'] == selected_region]
            
            # Create price trend chart
            fig = px.line(region_data, 
                         x='Date', 
                         y='Price',
                         title=f'Price Trends in {selected_region}')
            st.plotly_chart(fig, use_container_width=True)
            
            # Calculate key metrics
            latest_price = region_data.iloc[-1]['Price']
            yoy_change = ((latest_price - region_data.iloc[-13]['Price']) / 
                         region_data.iloc[-13]['Price'] * 100)
            
            # Display metrics
            st.metric("Current Average Price", f"${latest_price:,.2f}")
            st.metric("Year-over-Year Change", f"{yoy_change:.1f}%")
            
        with col2:
            st.subheader("Market Statistics")
            
            # Regional Stats
            st.write("Regional Statistics")
            stats_df = df[df['RegionName'] == selected_region].describe()
            st.dataframe(stats_df)
            
            # Show sample listing disclaimer
            st.info("""
            Note: Due to data availability limitations, we're showing aggregated 
            market data from Zillow's Home Value Index. For actual listings and 
            photos, please visit real estate websites like Zillow, Redfin, or 
            local real estate agencies.
            """)
    else:
        st.error("Unable to load real estate data. Please try again later.")

    # Additional Resources
    st.subheader("Additional Resources")
    st.markdown("""
    For more detailed information about specific properties and current listings, visit:
    - [Zillow](https://www.zillow.com)
    - [Redfin](https://www.redfin.com)
    - [Apartments.com](https://www.apartments.com)
    """)

if __name__ == "__main__":
    main()