Update app.py

app.py

@@ -2,9 +2,10 @@ import gradio as gr
 import pandas as pd
 import plotly.graph_objects as go
 from sklearn.linear_model import LinearRegression
-from datetime import timedelta
+import numpy as np
+from pandas.tseries.offsets import MonthEnd
 
-def plot_and_predict(zip, prediction_days):
+def plot_and_predict(zip, 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)]
@@ -14,16 +15,16 @@ def plot_and_predict(zip, prediction_days):
     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)
+    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_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))
+    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(
@@ -32,7 +33,7 @@ def plot_and_predict(zip, prediction_days):
         mode="lines",
         name="Historical Prices"
     )
-    future_dates = [df['Date'].iloc[-1] + timedelta(days=i) for i in range(1, prediction_days + 1)]
+    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,
@@ -58,7 +59,7 @@ interface = gr.Interface(
     fn=plot_and_predict,
     inputs=[
         gr.Textbox(label="ZIP Code"),
-        gr.Slider(minimum=1, maximum=365, step=1, label="Prediction Days"),
+        gr.Slider(minimum=1, maximum=60, step=1, label="Prediction Months"),
     ],
     outputs="plot"
 )