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US-Residential-Real-Estate-Average-Price-By-Zip

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Update app.py

266757a 8 months ago

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	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)