app.py

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)