Update app.py

app.py

@@ -4,17 +4,16 @@ import numpy as np
 import pandas as pd
 import matplotlib.pyplot as plt
 from datetime import datetime, timedelta
-from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import MinMaxScaler
 from tensorflow.keras.models import Sequential
 from tensorflow.keras.layers import Dense, LSTM
 
-# Load stock data using Yahoo Finance
+# Function to load stock data
 def load_stock_data(ticker, start, end):
     stock = yf.download(ticker, start=start, end=end)
     return stock
 
-# Data Preprocessing
+# Function to preprocess the data for model training
 def preprocess_data(stock):
     data = stock[['Close']]
     scaler = MinMaxScaler(feature_range=(0, 1))
@@ -30,18 +29,18 @@ def preprocess_data(stock):
 
     return x_train, y_train, scaler
 
-# Build the LSTM model
+# Function to build the LSTM model
 def build_model():
     model = Sequential()
     model.add(LSTM(units=50, return_sequences=True, input_shape=(60, 1)))
     model.add(LSTM(units=50, return_sequences=False))
     model.add(Dense(units=25))
     model.add(Dense(units=1))
-
+    
     model.compile(optimizer='adam', loss='mean_squared_error')
     return model
 
-# Training and prediction
+# Function to train the model
 def train_model(ticker, start, end):
     # Load stock data
     stock_data = load_stock_data(ticker, start, end)
@@ -55,27 +54,28 @@ def train_model(ticker, start, end):
 
     return model, scaler, stock_data
 
-# Predict stock prices
-def predict_stock(model, scaler, stock_data, ticker, start, end):
-    # Load real-time stock data for future predictions
+# Function to predict stock prices
+def predict_stock(model, scaler, stock_data):
+    # Use the last 60 days of data for predictions
     test_data = stock_data[['Close']][-60:].values
     test_data_scaled = scaler.transform(test_data)
 
     x_test = []
     x_test.append(test_data_scaled)
-
+    
     x_test = np.array(x_test)
     x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
 
+    # Make predictions
     predictions = model.predict(x_test)
     predictions = scaler.inverse_transform(predictions)
 
-    # Plotting the results
+    # Plot historical and predicted prices
     plt.figure(figsize=(10, 6))
     plt.plot(stock_data.index, stock_data['Close'], label="Historical Prices")
     future_dates = [stock_data.index[-1] + timedelta(days=i) for i in range(1, 91)]
     plt.plot(future_dates, predictions.flatten(), label="Predicted Prices")
-    plt.title(f'{ticker} Stock Price Prediction')
+    plt.title("Stock Price Prediction")
     plt.xlabel('Date')
     plt.ylabel('Price')
     plt.legend()
@@ -83,15 +83,11 @@ def predict_stock(model, scaler, stock_data, ticker, start, end):
 
     return predictions[-1][0]
 
-# Gradio Interface
+# Gradio interface function
 def stock_prediction(ticker, start, end):
-    # Train the model
     model, scaler, stock_data = train_model(ticker, start, end)
+    prediction = predict_stock(model, scaler, stock_data)
 
-    # Make prediction for the next 3 months
-    prediction = predict_stock(model, scaler, stock_data, ticker, start, end)
-
-    # Stock performance data
     start_price = stock_data['Close'].iloc[0]
     end_price = stock_data['Close'].iloc[-1]
     percent_change = ((end_price - start_price) / start_price) * 100
@@ -105,11 +101,12 @@ def stock_prediction(ticker, start, end):
         "Lowest Price": lowest_price
     }
 
-# Gradio UI
+# Define stock tickers and default dates
 tickers = ['AAPL', 'GOOG', 'MSFT', 'TSLA', 'AMZN', 'NFLX', 'META', 'NVDA', 'BABA', 'INTC']
 start_default = (datetime.now() - timedelta(days=365)).strftime("%Y-%m-%d")
 end_default = datetime.now().strftime("%Y-%m-%d")
 
+# Gradio interface
 iface = gr.Interface(
     fn=stock_prediction,
     inputs=[
@@ -121,5 +118,6 @@ iface = gr.Interface(
     live=True
 )
 
+# Launch the Gradio app
 if __name__ == "__main__":
-    iface.launch()
+    iface.launch()