Update app.py

app.py

@@ -1,5 +1,11 @@
 import yfinance as yf
 import pandas as pd
+import numpy as np  # Make sure to import numpy
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense
+import matplotlib.pyplot as plt
+import gradio as gr
 
 # Fetch stock data from Yahoo Finance
 def get_stock_data(ticker, start_date, end_date):
@@ -8,25 +14,12 @@ def get_stock_data(ticker, start_date, end_date):
         raise ValueError("No data found for the given ticker and date range.")
     return stock_data
 
-# Example usage
-ticker = 'AAPL'
-start_date = '2023-01-01'
-end_date = '2024-01-01'
-data = get_stock_data(ticker, start_date, end_date)
-print(data.head())
-from sklearn.preprocessing import MinMaxScaler
-
 # Preprocess stock data
 def preprocess_data(stock_data):
     scaler = MinMaxScaler(feature_range=(0, 1))  # Normalizing the close prices
     scaled_data = scaler.fit_transform(stock_data[['Close']].values)
     return scaled_data, scaler
 
-# Preprocess the stock data
-scaled_data, scaler = preprocess_data(data)
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import LSTM, Dense
-
 # Define and train an LSTM model
 def create_lstm_model(input_shape):
     model = Sequential()
@@ -42,17 +35,14 @@ def train_model(stock_data, window_size=60):
     for i in range(window_size, len(stock_data)):
         X_train.append(stock_data[i-window_size:i, 0])
         y_train.append(stock_data[i, 0])
-    X_train, y_train = np.array(X_train), np.array(y_train)
+    X_train, y_train = np.array(X_train), np.array(y_train)  # Ensure numpy is used here
     X_train = X_train.reshape((X_train.shape[0], X_train.shape[1], 1))
 
     model = create_lstm_model((X_train.shape[1], 1))
     model.fit(X_train, y_train, epochs=10, batch_size=32)
     return model
 
-# Train the LSTM model
-lstm_model = train_model(scaled_data)
-import numpy as np
-
+# Predict future stock prices
 def predict_future_prices(model, scaler, recent_data, days_to_predict=90):
     predictions = []
     input_data = recent_data[-60:].reshape(1, 60, 1)  # Using the last 60 days to predict
@@ -65,12 +55,6 @@ def predict_future_prices(model, scaler, recent_data, days_to_predict=90):
     predicted_prices = scaler.inverse_transform(np.array(predictions).reshape(-1, 1))
     return predicted_prices
 
-# Predict future stock prices
-recent_data = scaled_data[-60:]
-future_prices = predict_future_prices(lstm_model, scaler, recent_data)
-import gradio as gr
-import matplotlib.pyplot as plt
-
 # Gradio function to predict stock prices and display results
 def stock_prediction_app(ticker, start_date, end_date):
     stock_data = get_stock_data(ticker, start_date, end_date)