app.py

import gradio as gr
import numpy as np
import pandas as pd
import yfinance as yf
from datetime import datetime, timedelta
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):
    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")

iface = gr.Interface(
    fn=predict_stock_price,
    inputs=[
        ticker_input,
        gr.Number(label="Open"),
        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."
)

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()