Update app.py

app.py

@@ -3,6 +3,7 @@ from prophet import Prophet
 import gradio as gr
 import pandas as pd
 from datetime import datetime
+import matplotlib.pyplot as plt
 
 # List of stock tickers for the dropdown
 tickers = ['AAPL', 'GOOGL', 'MSFT', 'TSLA', 'AMZN', 'NFLX', 'NVDA', 'FB', 'INTC', 'AMD']
@@ -29,36 +30,42 @@ def predict_stock(ticker, start_date, end_date):
     future = model.make_future_dataframe(periods=90)
     forecast = model.predict(future)
     
+    # Plot the stock price history
+    fig, ax = plt.subplots(figsize=(10, 6))
+    ax.plot(stock_data.index, stock_data['Close'], label="Historical Prices")
+    
+    # Plot the forecast
+    ax.plot(forecast['ds'], forecast['yhat'], label="Predicted Prices", linestyle='--')
+    
+    # Customize the plot
+    ax.set_title(f"{ticker} Stock Price Prediction", fontsize=14)
+    ax.set_xlabel("Date", fontsize=12)
+    ax.set_ylabel("Price (USD)", fontsize=12)
+    ax.legend(loc="upper left")
+    
+    # Save the plot to a file
+    plt.savefig('/tmp/stock_prediction.png')
+    plt.close(fig)  # Close the plot to prevent display issues in some environments
+    
     # Calculate key statistics
     current_price = stock_data['Close'][-1]
     highest_price = stock_data['Close'].max()
     lowest_price = stock_data['Close'].min()
     percent_change = ((current_price - stock_data['Close'][0]) / stock_data['Close'][0]) * 100
     
-    # Plot historical and future stock prices
-    historical_plot = stock_data['Close'].plot(title=f"{ticker} Historical Prices").get_figure()
-    future_plot = model.plot(forecast).get_figure()
-    
     # Simple buy/sell recommendation
     future_avg = forecast['yhat'].mean()
     buy_sell = "Buy" if current_price < future_avg else "Sell"
     
-    # Return results
-    return {
-        "Current Price": current_price,
-        "Highest Price": highest_price,
-        "Lowest Price": lowest_price,
-        "Percent Change": percent_change,
-        "Recommendation": buy_sell,
-        "Historical Plot": historical_plot,
-        "Future Plot": future_plot
-    }
+    # Return the plot file path and statistics
+    return '/tmp/stock_prediction.png', current_price, highest_price, lowest_price, percent_change, buy_sell
 
 # Interface function for the Gradio UI
 def app_interface(ticker, start_date, end_date):
-    result = predict_stock(ticker, start_date, end_date)
+    graph, current_price, highest_price, lowest_price, percent_change, buy_sell = predict_stock(ticker, start_date, end_date)
     
-    return result["Historical Plot"], result["Future Plot"], result["Current Price"], result["Highest Price"], result["Lowest Price"], result["Percent Change"], result["Recommendation"]
+    # Return the file path for the plot and text outputs
+    return graph, current_price, highest_price, lowest_price, percent_change, buy_sell
 
 # Updated Gradio UI using Textbox for date input instead of DatePicker
 gr_interface = gr.Interface(
@@ -69,8 +76,7 @@ gr_interface = gr.Interface(
         gr.Textbox(label="End Date (YYYY-MM-DD)", value=datetime.now().strftime('%Y-%m-%d'))
     ],
     outputs=[
-        gr.Plot(label="Historical Stock Prices"),
-        gr.Plot(label="Future Stock Predictions"),
+        gr.Image(label="Stock Price Prediction"),  # Display the saved plot image
         gr.Textbox(label="Current Price"),
         gr.Textbox(label="Highest Price"),
         gr.Textbox(label="Lowest Price"),