Update app.py

app.py

@@ -6,7 +6,7 @@ import tensorflow as tf
 from sklearn.preprocessing import MinMaxScaler
 import matplotlib.pyplot as plt
 import gradio as gr
-from datetime import datetime
+from datetime import datetime, timedelta
 
 # Step 1: Fetch stock data from yfinance
 def fetch_stock_data(ticker, start_date, end_date):
@@ -45,12 +45,12 @@ def train_model(model, train_data, epochs=5):
     model.fit(X_train, y_train, epochs=epochs, batch_size=32, verbose=0)
     return model
 
-# Step 5: Predict future stock prices
-def predict_future(model, last_data, scaler, steps=90):
+# Step 5: Predict stock prices
+def predict_future(model, last_data, scaler, steps=1):
     predictions = []
     input_data = last_data[-60:].reshape(1, -1)
 
-    # Generate predictions for the future
+    # Generate predictions for the specified number of future days
     for _ in range(steps):
         input_reshaped = input_data.reshape(1, 60, 1)
         predicted_price = model.predict(input_reshaped, verbose=0)
@@ -63,55 +63,57 @@ def predict_future(model, last_data, scaler, steps=90):
 
 # Step 6: Plot historical and predicted stock prices
 def plot_predictions(data, predicted_prices):
+    # Fetch the last 60 days of data to plot before the prediction starts
     last_60_days = data['Close'][-60:].values
 
-    # Create a plot
+    # Create a figure for the plot
     plt.figure(figsize=(14, 6))
     
-    # Plot historical prices
+    # Plot historical stock prices
     plt.plot(data['Date'], data['Close'], label="Historical Prices", color='blue')
 
-    # Future dates for predicted prices
+    # Generate future dates for predicted prices
     future_dates = pd.date_range(start=data['Date'].iloc[-1], periods=len(predicted_prices) + 1, freq='D')[1:]
-    
-    # Plot predicted prices
+
+    # Plot predicted stock prices
     plt.plot(future_dates, predicted_prices, label="Predicted Prices", color='orange')
 
-    plt.title("Stock Price Prediction")
+    # Adding labels and title to the graph
+    plt.title("Stock Price Prediction for Tomorrow")
     plt.xlabel("Date")
     plt.ylabel("Stock Price (USD)")
     plt.legend()
-    plt.grid()
+    plt.grid(True)
 
-    # Save the plot to a file for Gradio to display
+    # Save the plot as an image file for Gradio to display
     plt.savefig("stock_prediction.png")
     plt.close()
-    
-    return "stock_prediction.png"  # Return the file path for Gradio
+
+    return "stock_prediction.png"  # Return the path to the saved image
 
 # Step 7: Gradio interface function
 def stock_prediction_app(ticker, start_date_str, end_date_str):
-    # Convert date strings to datetime objects
+    # Convert input 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
+    # Fetch stock data from yfinance
     data = fetch_stock_data(ticker, start_date, end_date)
     
-    # Prepare data for the LSTM model
+    # Prepare data for LSTM model
     scaled_data, scaler = prepare_data(data)
     
     # Build and train the LSTM model
     model = build_model((60, 1))
     model = train_model(model, scaled_data)
     
-    # Predict future stock prices for the next 90 days
-    predicted_prices = predict_future(model, scaled_data, scaler)
+    # Predict stock price for tomorrow (1 day)
+    predicted_price = predict_future(model, scaled_data, scaler, steps=1)
     
-    # Plot historical and predicted stock prices
-    plot_path = plot_predictions(data, predicted_prices)
+    # Generate and return the plot with historical and predicted prices
+    plot_path = plot_predictions(data, predicted_price)
     
-    return plot_path  # Return the plot file path
+    return plot_path, predicted_price[0][0]  # Return the path of the plot image and tomorrow's predicted price
 
 # Step 8: Gradio UI setup
 tickers = ["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA", "META", "NFLX", "NVDA", "BABA", "BA"]
@@ -124,11 +126,13 @@ ui = gr.Interface(
         gr.Textbox(label="Start Date (YYYY-MM-DD)"),
         gr.Textbox(label="End Date (YYYY-MM-DD)")
     ],
-    outputs=gr.Image(type="filepath"),  # Return the image file path for the plot
-    title="Stock Prediction App",
-    description="Select a stock ticker and date range to predict future prices."
+    outputs=[
+        gr.Image(type="filepath"),  # Return the file path for the generated graph
+        gr.Number(label="Predicted Price for Tomorrow (USD)")
+    ],
+    title="Stock Price Prediction App",
+    description="Predict future stock price for tomorrow based on historical data."
 )
 
-# Launch the app
+# Launch the Gradio app
 ui.launch()
-