Update app.py

app.py

@@ -1,9 +1,8 @@
-
 import gradio as gr
 import numpy as np
 import pandas as pd
 import yfinance as yf
-from datetime import datetime
+from datetime import datetime, timedelta
 from tensorflow.keras.models import load_model
 from joblib import load
 
@@ -26,7 +25,7 @@ def get_last_stock_data(ticker):
         return str(e)
 
 # Function to make predictions
-def predict_stock_price(ticker, open_price, high_price, low_price, close_price, adj_close_price, volume):
+def predict_stock_price(ticker, open_price, close_price):
     try:
         start_date = '2010-01-01'
         end_date = datetime.now().strftime('%Y-%m-%d')
@@ -55,48 +54,95 @@ def predict_stock_price(ticker, open_price, high_price, low_price, close_price,
         lstm_predictions = scaler.inverse_transform(lstm_predictions)
         next_day_lstm_price = lstm_predictions[-1][0]
         
-        
-    
-        
         result = f"Predicted future price for {ticker}: ${next_day_lstm_price:.2f}"
 
         return result
     except Exception as e:
         return str(e)
 
+# Function to predict next month's price
+def predict_next_month_price(ticker):
+    try:
+        start_date = '2010-01-01'
+        end_date = datetime.now().strftime('%Y-%m-%d')
+        data = yf.download(ticker, start=start_date, end=end_date)
+
+        # Prepare the data
+        data = data[['Close']]
+        dataset = data.values
+        scaled_data = scaler.transform(dataset)
+
+        # Prepare the data for LSTM
+        x_test_lstm = []
+        for i in range(60, len(scaled_data)):
+            x_test_lstm.append(scaled_data[i-60:i])
+
+        x_test_lstm = np.array(x_test_lstm)
+        x_test_lstm = np.reshape(x_test_lstm, (x_test_lstm.shape[0], x_test_lstm.shape[1], 1))
+
+        # Predicting the next 30 days
+        predictions = []
+        for _ in range(30):
+            pred = lstm_model.predict(x_test_lstm[-1].reshape(1, 60, 1))
+            predictions.append(pred)
+            x_test_lstm = np.append(x_test_lstm, pred.reshape(1, 1, 1), axis=1)
+            x_test_lstm = x_test_lstm[:, 1:, :]
+
+        predictions = np.array(predictions).reshape(-1, 1)
+        next_month_predictions = scaler.inverse_transform(predictions)
+        next_month_price = next_month_predictions[-1][0]
+
+        result = f"Predicted price for {ticker} next month: ${next_month_price:.2f}"
+
+        return result
+    except Exception as e:
+        return str(e)
+
+# Function to display historical data
+def display_historical_data(ticker):
+    try:
+        start_date = '2010-01-01'
+        end_date = datetime.now().strftime('%Y-%m-%d')
+        data = yf.download(ticker, start=start_date, end=end_date)
+        return data.tail(30)
+    except Exception as e:
+        return str(e)
+
 # Set up Gradio interface
 ticker_input = gr.Dropdown(choices=stock_list, label="Stock Ticker")
 
-def get_user_inputs(ticker):
-    last_data = get_last_stock_data(ticker)
-    if isinstance(last_data, str):
-        return gr.Textbox.update(value=last_data)
-    else:
-        return gr.update(inputs=[
-            gr.Number(value=last_data['Open'], label='Open'),
-            gr.Number(value=last_data['High'], label='High'),
-            gr.Number(value=last_data['Low'], label='Low'),
-            gr.Number(value=last_data['Close'], label='Close'),
-            gr.Number(value=last_data['Adj Close'], label='Adj Close'),
-            gr.Number(value=last_data['Volume'], label='Volume')
-        ])
-
 iface = gr.Interface(
     fn=predict_stock_price,
     inputs=[
         ticker_input,
         gr.Number(label="Open"),
-        gr.Number(label="High"),
-        gr.Number(label="Low"),
-        gr.Number(label="Close"),
-        gr.Number(label="Adj Close"),
-        gr.Number(label="Volume")
+        gr.Number(label="Close")
     ],
     outputs=gr.Textbox(),
     title="Stock Price Predictor",
     description="Select the stock ticker and input the last recorded values to predict the closing price using the LSTM model."
 )
 
-iface.launch()
+next_month_iface = gr.Interface(
+    fn=predict_next_month_price,
+    inputs=[ticker_input],
+    outputs=gr.Textbox(),
+    title="Next Month Stock Price Predictor",
+    description="Select the stock ticker to predict the closing price for the next month using the LSTM model."
+)
 
+historical_data_iface = gr.Interface(
+    fn=display_historical_data,
+    inputs=[ticker_input],
+    outputs=gr.Dataframe(),
+    title="Historical Data Viewer",
+    description="Select the stock ticker to view the historical data."
+)
+
+# Combine interfaces
+app = gr.TabbedInterface(
+    interface_list=[iface, next_month_iface, historical_data_iface],
+    tab_names=["Predict Today's Price", "Predict Next Month's Price", "View Historical Data"]
+)
 
+app.launch()