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import gradio as gr
import pandas as pd
import plotly.graph_objects as go
from sklearn.linear_model import LinearRegression
from datetime import timedelta
def plot_and_predict(zip, prediction_days):
# Read and process the real estate data from Zillow
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')
df = df[df['RegionName'] == int(zip)]
df = df.loc[:, '2000-01-31':]
df = df.T.reset_index()
df.columns = ['Date', 'Price']
df['Date'] = pd.to_datetime(df['Date'])
# Train linear regression model
df['Timestamp'] = (df['Date'] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1D')
X = df['Timestamp'].values.reshape(-1, 1)
y = df['Price'].values
model = LinearRegression()
model.fit(X, y)
# Predict future prices
last_timestamp = df['Timestamp'].iloc[-1]
future_timestamps = [last_timestamp + i for i in range(1, prediction_days + 1)]
predicted_prices = model.predict(pd.np.array(future_timestamps).reshape(-1, 1))
# Prepare data for plotting
historical_prices_trace = go.Scatter(
x=df['Date'],
y=df['Price'],
mode="lines",
name="Historical Prices"
)
future_dates = [df['Date'].iloc[-1] + timedelta(days=i) for i in range(1, prediction_days + 1)]
predicted_prices_trace = go.Scatter(
x=future_dates,
y=predicted_prices,
mode="lines",
name="Predicted Prices"
)
# Plot data
fig = go.Figure()
fig.add_trace(historical_prices_trace)
fig.add_trace(predicted_prices_trace)
fig.update_layout(
title=f"Real Estate Price Prediction for Zip Code {zip}",
xaxis_title="Date",
yaxis_title="Price",
legend_title_text="Data"
)
return fig
# Gradio interface
interface = gr.Interface(
fn=plot_and_predict,
inputs=[
gr.Textbox(label="ZIP Code"),
gr.Slider(minimum=1, maximum=365, step=1, label="Prediction Days"),
],
outputs="plot"
)
# Launch the app
interface.launch(debug=True) |