Update app.py

app.py

@@ -1,3 +1,4 @@
+
 # Import required libraries
 import yfinance as yf
 import pandas as pd
@@ -6,6 +7,7 @@ import tensorflow as tf
 from sklearn.preprocessing import MinMaxScaler
 import matplotlib.pyplot as plt
 import gradio as gr
+from datetime import datetime
 
 # Step 1: Fetch stock data
 def fetch_stock_data(ticker, start_date, end_date):
@@ -136,19 +138,23 @@ def plot_predictions(data, predicted_prices, scaler):
     plt.show()
 
 # Step 7: Gradio Interface Function
-def stock_prediction_app(ticker, start_date, end_date):
+def stock_prediction_app(ticker, start_date_str, end_date_str):
     """
     The core function for the Gradio app. Fetches stock data, trains the LSTM model,
     predicts future prices, and visualizes the results.
 
     Args:
         ticker (str): Stock ticker symbol selected by the user.
-        start_date (str): Start date selected by the user.
-        end_date (str): End date selected by the user.
+        start_date_str (str): Start date selected by the user (YYYY-MM-DD).
+        end_date_str (str): End date selected by the user (YYYY-MM-DD).
 
     Returns:
         None (Displays a plot of historical and predicted stock prices).
     """
+    # Convert date strings to datetime objects
+    start_date = datetime.strptime(start_date_str, "%Y-%m-%d").date()
+    end_date = datetime.strptime(end_date_str, "%Y-%m-%d").date()
+
     # Fetch stock data
     data = fetch_stock_data(ticker, start_date, end_date)
     
@@ -173,8 +179,8 @@ ui = gr.Interface(
     fn=stock_prediction_app,
     inputs=[
         gr.Dropdown(tickers, label="Select Stock Ticker"),
-        gr.Date(label="Start Date"),   # Updated syntax
-        gr.Date(label="End Date")      # Updated syntax
+        gr.Textbox(label="Start Date (YYYY-MM-DD)"),   # Use textbox instead
+        gr.Textbox(label="End Date (YYYY-MM-DD)")      # Use textbox instead
     ],
     outputs="plot",
     title="Stock Prediction App",