Update app.py

app.py

@@ -5,7 +5,7 @@ from sklearn.linear_model import LinearRegression
 import numpy as np
 from pandas.tseries.offsets import MonthEnd
 
-def plot_and_predict(zip, prediction_months):
+def plot_and_predict(zip, start_date, 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)]
@@ -14,6 +14,10 @@ def plot_and_predict(zip, prediction_months):
     df.columns = ['Date', 'Price']
     df['Date'] = pd.to_datetime(df['Date'])
 
+    # Filter data based on start date
+    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)
@@ -59,10 +63,11 @@ interface = gr.Interface(
     fn=plot_and_predict,
     inputs=[
         gr.Textbox(label="ZIP Code"),
+        gr.Textbox(label="Start Date (YYYY-MM-DD)", placeholder="YYYY-MM-DD"),
         gr.Slider(minimum=1, maximum=60, step=1, label="Prediction Months"),
     ],
     outputs="plot"
 )
 
 # Launch the app
-interface.launch(debug=True)
+interface.launch()