Create app.py

app.py

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+import streamlit as st
+import numpy as np
+import pandas as pd
+import yfinance as yf
+from datetime import datetime
+from tensorflow.keras.models import load_model
+from joblib import load
+
+# Load the saved LSTM model and scaler
+lstm_model = load_model('lstm_model.h5')
+scaler = load('scaler.joblib')
+
+# Define the list of stocks
+stock_list = ['GOOG', 'AAPL', 'TSLA', 'AMZN', 'MSFT']
+
+# Function to get the last row of stock data
+def get_last_stock_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)
+        last_row = data.iloc[-1]
+        return last_row.to_dict()
+    except Exception as e:
+        return str(e)
+
+# Function to make predictions
+def predict_stock_price(ticker, open_price, close_price):
+    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)
+
+        # Append the user inputs as the last row in the data
+        user_input = np.array([[close_price]])
+        user_input_scaled = scaler.transform(user_input)
+        scaled_data = np.vstack([scaled_data, user_input_scaled])
+
+        # 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))
+
+        # LSTM Predictions
+        lstm_predictions = lstm_model.predict(x_test_lstm)
+        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, close_price):
+    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)
+
+        # Append the user inputs as the last row in the data
+        user_input = np.array([[close_price]])
+        user_input_scaled = scaler.transform(user_input)
+        scaled_data = np.vstack([scaled_data, user_input_scaled])
+
+        # 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[0])
+            new_input = np.append(x_test_lstm[-1][1:], pred)
+            x_test_lstm = np.append(x_test_lstm, new_input.reshape(1, 60, 1), axis=0)
+
+        predictions = np.array(predictions)
+        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)
+
+# Streamlit interface
+st.title("Stock Price Predictor")
+
+tab1, tab2, tab3 = st.tabs(["Predict Today's Price", "Predict Next Month's Price", "View Historical Data"])
+
+with tab1:
+    st.header("Predict Today's Price")
+    ticker_input = st.selectbox("Stock Ticker", stock_list)
+    open_price = st.number_input("Open Price", value=0.0)
+    close_price = st.number_input("Close Price", value=0.0)
+    if st.button("Predict Today's Price"):
+        result = predict_stock_price(ticker_input, open_price, close_price)
+        st.write(result)
+
+with tab2:
+    st.header("Predict Next Month's Price")
+    next_month_ticker_input = st.selectbox("Stock Ticker", stock_list)
+    next_month_close_price = st.number_input("Close Price", value=0.0)
+    if st.button("Predict Next Month's Price"):
+        result = predict_next_month_price(next_month_ticker_input, next_month_close_price)
+        st.write(result)
+
+with tab3:
+    st.header("View Historical Data")
+    historical_ticker_input = st.selectbox("Stock Ticker", stock_list)
+    if st.button("View Data"):
+        data = display_historical_data(historical_ticker_input)
+        st.dataframe(data)