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import gradio as gr
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, high_price, low_price, close_price, adj_close_price, volume):
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]
# Plot the data
plt.figure(figsize=(10, 6))
plt.plot(data.index, data['Close'], label='Historical Close Prices')
plt.axvline(x=data.index[-1], color='r', linestyle='--', label='Prediction Date')
plt.plot([data.index[-1], data.index[-1] + pd.DateOffset(1)], [data['Close'].iloc[-1], next_day_lstm_price], 'go-', label='Predicted Price')
plt.title(f'Predicted Closing Price for {ticker}')
plt.xlabel('Date')
plt.ylabel('Close Price USD ($)')
plt.legend()
# Save the plot to a buffer
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
plt.close()
result = f"Predicted future price for {ticker}: ${next_day_lstm_price:.2f}"
return result,buf
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")
],
outputs=[gr.Textbox(), gr.Image(type="plot")],
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()
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