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import gradio as gr |
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import pandas as pd |
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import plotly.graph_objects as go |
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from sklearn.linear_model import LinearRegression |
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import numpy as np |
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from pandas.tseries.offsets import MonthEnd |
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import datetime |
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def plot_and_predict(zip, start_year, start_month, prediction_months): |
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if not zip.isdigit() or len(zip) != 5: |
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return "Error: Please enter a valid 5-digit ZIP code." |
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current_year = datetime.datetime.now().year |
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if not start_year.isdigit() or not (2000 <= int(start_year) <= current_year): |
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return f"Error: Please enter a valid year between 2000 and {current_year}." |
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try: |
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start_month_int = int(start_month) |
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start_date = f"{start_year}-{start_month_int:02d}-01" |
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except ValueError: |
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return "Error: Invalid start month. Please enter a numeric month between 1 and 12." |
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start_date = pd.to_datetime(start_date) |
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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') |
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df = df[df['RegionName'] == int(zip)] |
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df = df.loc[:, '2000-01-31':] |
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df = df.T.reset_index() |
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df.columns = ['Date', 'Price'] |
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df['Date'] = pd.to_datetime(df['Date']) |
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df = df[df['Date'] >= start_date] |
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if df.empty: |
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return "Error: No data found for the provided ZIP code or start year/month. Please check your inputs." |
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df['MonthsSinceStart'] = np.arange(len(df)) |
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X = df['MonthsSinceStart'].values.reshape(-1, 1) |
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y = df['Price'].values |
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model = LinearRegression() |
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model.fit(X, y) |
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last_month_index = df['MonthsSinceStart'].iloc[-1] |
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future_months = np.array([last_month_index + i for i in range(1, prediction_months + 1)]).reshape(-1, 1) |
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predicted_prices = model.predict(future_months) |
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historical_prices_trace = go.Scatter( |
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x=df['Date'], |
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y=df['Price'], |
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mode="lines", |
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name="Historical Prices" |
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) |
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future_dates = [df['Date'].iloc[-1] + MonthEnd(i) for i in range(1, prediction_months + 1)] |
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predicted_prices_trace = go.Scatter( |
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x=future_dates, |
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y=predicted_prices, |
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mode="lines", |
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name="Predicted Prices" |
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) |
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fig = go.Figure() |
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fig.add_trace(historical_prices_trace) |
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fig.add_trace(predicted_prices_trace) |
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fig.update_layout( |
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title=f"Real Estate Price Prediction for Zip Code {zip}", |
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xaxis_title="Date", |
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yaxis_title="Price", |
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legend_title_text="Data" |
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) |
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return fig |
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interface = gr.Interface( |
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fn=plot_and_predict, |
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inputs=[ |
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gr.Textbox(label="ZIP Code", placeholder="e.g., 90210"), |
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gr.Textbox(label="Start Year", placeholder="e.g., 2020"), |
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gr.Dropdown(label="Start Month", choices=[str(i) for i in range(1, 13)]), |
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gr.Slider(minimum=1, maximum=60, step=1, label="Prediction Months"), |
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], |
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outputs="plot", |
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title="Real Estate Price Predictor", |
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description="Enter a ZIP code, start year, start month, and the number of months for price prediction." |
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) |
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interface.launch(debug=True) |
