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, timedelta
+from datetime import datetime
 
 # Step 1: Fetch stock data from yfinance
 def fetch_stock_data(ticker, start_date, end_date):
@@ -109,11 +109,30 @@ def stock_prediction_app(ticker, start_date_str, end_date_str):
     
     # Predict stock price for tomorrow (1 day)
     predicted_price = predict_future(model, scaled_data, scaler, steps=1)
+
+    # Current, highest, lowest prices, and percentage change
+    current_price = data['Close'].iloc[-1]
+    highest_price = data['Close'].max()
+    lowest_price = data['Close'].min()
     
+    # Calculate percentage change
+    percentage_change = ((predicted_price[0][0] - current_price) / current_price) * 100
+
+    # Buy/Sell recommendation
+    recommendation = "Buy" if predicted_price[0][0] > current_price else "Sell"
+
     # Generate and return the plot with historical and predicted prices
     plot_path = plot_predictions(data, predicted_price)
     
-    return plot_path, predicted_price[0][0]  # Return the path of the plot image and tomorrow's predicted price
+    return (
+        plot_path, 
+        predicted_price[0][0],  # Tomorrow's predicted price
+        current_price,           # Current price
+        highest_price,           # Highest price
+        lowest_price,            # Lowest price
+        percentage_change,       # Percentage change
+        recommendation            # Buy/Sell recommendation
+    )
 
 # Step 8: Gradio UI setup
 tickers = ["AAPL", "GOOGL", "MSFT", "AMZN", "TSLA", "META", "NFLX", "NVDA", "BABA", "BA"]
@@ -128,7 +147,12 @@ ui = gr.Interface(
     ],
     outputs=[
         gr.Image(type="filepath"),  # Return the file path for the generated graph
-        gr.Number(label="Predicted Price for Tomorrow (USD)")
+        gr.Number(label="Predicted Price for Tomorrow (USD)"),
+        gr.Number(label="Current Price (USD)"),
+        gr.Number(label="Highest Price (USD)"),
+        gr.Number(label="Lowest Price (USD)"),
+        gr.Number(label="Percentage Change (%)"),
+        gr.Textbox(label="Recommendation")
     ],
     title="Stock Price Prediction App",
     description="Predict future stock price for tomorrow based on historical data."