Update app.py

app.py

@@ -6,22 +6,25 @@ from sklearn.preprocessing import MinMaxScaler
 from tensorflow import keras
 
 # Load your trained model
-model = keras.models.load_model('your_model.h5')
+model = keras.models.load_model('your_model.h5')  # Ensure this path is correct
 
 # Function to predict stock prices
 def predict_stock_price(stock_ticker, start_date, end_date):
     # Fetch data
     data = yf.download(stock_ticker, start=start_date, end=end_date)
     
+    # Check if data is returned
+    if data.empty:
+        return "No data available for the selected dates."
+
     # Preprocess data
     scaler = MinMaxScaler()
     scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1, 1))
-    
+
     # Prepare input for the model
-    # This assumes your model expects a certain shape of input
     input_data = scaled_data[-60:]  # Use the last 60 days of data
     input_data = input_data.reshape((1, input_data.shape[0], 1))
-    
+
     # Predict stock prices
     prediction = model.predict(input_data)
     predicted_price = scaler.inverse_transform(prediction)  # Rescale back to original price
@@ -30,7 +33,7 @@ def predict_stock_price(stock_ticker, start_date, end_date):
 
 # Create the Gradio interface
 stock_ticker_input = gr.Dropdown(
-    choices=["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA"],
+    choices=["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA"],  # Add more tickers as needed
     label="Select Stock Ticker"
 )
 
@@ -46,7 +49,7 @@ iface = gr.Interface(
     ],
     outputs="text",
     title="Stock Price Prediction App",
-    description="Enter the stock ticker and date range to predict the stock price."
+    description="Enter the stock ticker and date range to predict the stock price for tomorrow."
 )
 
 # Launch the Gradio app