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import gradio as gr
import pandas as pd
import plotly.graph_objects as go
from sklearn.linear_model import LinearRegression
import numpy as np
from pandas.tseries.offsets import MonthEnd
import datetime
def plot_and_predict(zip, start_year, start_month, prediction_months):
# 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'])
# Combine year and month into a start date and filter data based on it
start_date = f"{start_year}-{start_month:02d}-01"
start_date = pd.to_datetime(start_date)
df = df[df['Date'] >= start_date]
# Train linear regression model
df['MonthsSinceStart'] = np.arange(len(df))
X = df['MonthsSinceStart'].values.reshape(-1, 1)
y = df['Price'].values
model = LinearRegression()
model.fit(X, y)
# Predict future prices
last_month_index = df['MonthsSinceStart'].iloc[-1]
future_months = np.array([last_month_index + i for i in range(1, prediction_months + 1)]).reshape(-1, 1)
predicted_prices = model.predict(future_months)
# 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] + MonthEnd(i) for i in range(1, prediction_months + 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
# Custom validation functions
def validate_zip(zip_code):
if not zip_code.isdigit() or len(zip_code) != 5:
return "Please enter a valid 5-digit ZIP code."
def validate_year(year):
current_year = datetime.datetime.now().year
if not year.isdigit() or not (2000 <= int(year) <= current_year):
return f"Please enter a valid year between 2000 and {current_year}."
# Gradio interface with updated inputs and validations
interface = gr.Interface(
fn=plot_and_predict,
inputs=[
gr.Textbox(label="ZIP Code", placeholder="e.g., 90210", validation=validate_zip),
gr.Textbox(label="Start Year", placeholder="e.g., 2020", validation=validate_year),
gr.Dropdown(label="Start Month", choices=[str(i) for i in range(1, 13)], placeholder="Select Month"),
gr.Slider(minimum=1, maximum=60, step=1, label="Prediction Months", default=12),
],
outputs="plot",
layout="vertical",
title="Real Estate Price Predictor",
description="Enter a ZIP code, start year, start month, and the number of months for price prediction."
)
# Launch the app
interface.launch(debug=True) |