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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, 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)
# Set up Gradio interface
with gr.Blocks() as app:
with gr.Tab("Predict Today's Price"):
gr.Markdown("## Predict Today's Price")
ticker_input = gr.Dropdown(choices=stock_list, label="Stock Ticker")
open_price = gr.Number(label="Open")
close_price = gr.Number(label="Close")
predict_button = gr.Button("Predict")
predict_output = gr.Textbox()
predict_button.click(predict_stock_price, inputs=[ticker_input, open_price, close_price], outputs=predict_output)
with gr.Tab("Predict Next Month's Price"):
gr.Markdown("## Predict Next Month's Price")
next_month_ticker_input = gr.Dropdown(choices=stock_list, label="Stock Ticker")
next_month_close_price = gr.Number(label="Close")
next_month_predict_button = gr.Button("Predict")
next_month_predict_output = gr.Textbox()
next_month_predict_button.click(predict_next_month_price, inputs=[next_month_ticker_input, next_month_close_price], outputs=next_month_predict_output)
with gr.Tab("View Historical Data"):
gr.Markdown("## View Historical Data")
historical_ticker_input = gr.Dropdown(choices=stock_list, label="Stock Ticker")
historical_view_button = gr.Button("View Data")
historical_data_output = gr.Dataframe()
historical_view_button.click(display_historical_data, inputs=[historical_ticker_input], outputs=historical_data_output)
app.launch()
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