Update app.py

app.py

@@ -4,20 +4,41 @@ import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 from sklearn.linear_model import LinearRegression
+import matplotlib
+matplotlib.use('Agg')  # Use non-interactive backend
+
+# Date validation function
+def validate_date(date_text):
+    try:
+        pd.to_datetime(date_text)
+        return True
+    except ValueError:
+        return False
 
 # Function to fetch data
 def fetch_data(ticker, start_date, end_date):
+    # Validate the dates
+    if not validate_date(start_date) or not validate_date(end_date):
+        return "Invalid date format. Please use YYYY-MM-DD.", None
+    
     # Convert input dates from strings to pandas datetime
     start_date = pd.to_datetime(start_date)
     end_date = pd.to_datetime(end_date)
     
     stock_data = yf.download(ticker, start=start_date, end=end_date)
+    
+    if stock_data.empty:
+        return f"No data found for ticker {ticker} in the specified date range.", None
+    
     return stock_data
 
 # Function to predict future prices
 def predict_stock(ticker, start_date, end_date):
     stock_data = fetch_data(ticker, start_date, end_date)
     
+    if isinstance(stock_data, str):  # In case of an error message from fetch_data
+        return stock_data, None
+    
     stock_data['Date'] = pd.to_datetime(stock_data.index)
     stock_data['Days'] = (stock_data['Date'] - stock_data['Date'].min()).dt.days
     
@@ -36,6 +57,9 @@ def predict_stock(ticker, start_date, end_date):
 def plot_stock_data(ticker, start_date, end_date):
     future_prices, stock_data = predict_stock(ticker, start_date, end_date)
     
+    if isinstance(future_prices, str):  # In case of an error message
+        return future_prices
+    
     plt.figure(figsize=(10, 6))
     plt.plot(stock_data['Date'], stock_data['Close'], label="Historical Prices")
     future_dates = pd.date_range(stock_data['Date'].max() + pd.Timedelta(days=1), periods=90)
@@ -52,15 +76,26 @@ def plot_stock_data(ticker, start_date, end_date):
 # Gradio Interface
 def stock_prediction_interface(ticker, start_date, end_date):
     stock_data = fetch_data(ticker, start_date, end_date)
+    
+    if isinstance(stock_data, str):  # If there was an error fetching data
+        return stock_data, None
+    
     price_change = (stock_data['Close'].iloc[-1] - stock_data['Close'].iloc[0]) / stock_data['Close'].iloc[0] * 100
     highest_price = stock_data['High'].max()
     lowest_price = stock_data['Low'].min()
     
     future_prices, _ = predict_stock(ticker, start_date, end_date)
+    
+    if isinstance(future_prices, str):  # If there was an error in prediction
+        return future_prices, None
+    
     decision = "Buy" if future_prices[-1] > stock_data['Close'].iloc[-1] else "Sell"
     
     graph = plot_stock_data(ticker, start_date, end_date)
     
+    if isinstance(graph, str):  # If there was an error in plotting
+        return graph, None
+    
     return f"Percentage Change: {price_change:.2f}%\nHighest Price: {highest_price}\nLowest Price: {lowest_price}\nDecision: {decision}", graph
 
 # Gradio app UI
@@ -81,4 +116,4 @@ with gr.Blocks() as demo:
         outputs=[output_text, output_plot]
     )
 
-demo.launch()
+demo.launch()