import gradio as gr
import pandas as pd
import plotly.express as px

def plot_real_estate(zip):
    # Read the CSV file
    df = pd.read_csv('https://files.zillowstatic.com/research/public_csvs/zhvi/Zip_zhvi_uc_sfrcondo_tier_0.33_0.67_sm_sa_month.csv')

    # Extract the data for zip code
    df = df[df['RegionName'] == int(zip)]

    # Select the columns with dates
    df = df.loc[:, '2000-01-31':]

    # Transpose the data
    df = df.T.reset_index()
    df.columns = ['Date', 'Price']

    # Convert 'Date' to datetime
    df['Date'] = pd.to_datetime(df['Date'])

    # Compute the moving averages
    df_ma3 = df['Price'].rolling(3).mean()
    df_ma6 = df['Price'].rolling(6).mean()
    df_ma12 = df['Price'].rolling(12).mean()
    df_ma24 = df['Price'].rolling(24).mean()

    # Plot the price data
    fig = px.line(df, x='Date', y='Price', title=f'Housing Prices for Zip Code {zip}')
    fig['data'][0]['showlegend'] = True
    fig['data'][0]['name'] = 'Price'

    # Plot the moving averages
    fig.add_scatter(x=df['Date'], y=df_ma3, mode='lines', name='3-Month MA')
    fig.add_scatter(x=df['Date'], y=df_ma6, mode='lines', name='6-Month MA')
    fig.add_scatter(x=df['Date'], y=df_ma12, mode='lines', name='12-Month MA')
    fig.add_scatter(x=df['Date'], y=df_ma24, mode='lines', name='24-Month MA')
    # Add axis labels
    fig.update_layout(xaxis_title='Date', yaxis_title='Price')

    return fig

iface = gr.Interface(fn=plot_real_estate,
                     inputs=[gr.components.Textbox(label="Zip Code")],
                     outputs=gr.Plot())

iface.launch(share=False, debug=True)