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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import mean_squared_error
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM
import tensorflow as tf
import streamlit as st
def predict_stock(csv_file):
# Load and preprocess data
dataset = pd.read_csv(csv_file, usecols=[1], engine='python', encoding="big5")
dataset = dataset.values.astype('float32')
# Normalize the dataset
scaler = MinMaxScaler(feature_range=(0, 1))
dataset = scaler.fit_transform(dataset)
# Split into train and test sets
train_size = int(len(dataset) * 0.8)
train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
# Create dataset function
def create_dataset(dataset, look_back=1):
dataX, dataY = [], []
for i in range(len(dataset)-look_back-1):
a = dataset[i:(i+look_back), 0]
dataX.append(a)
dataY.append(dataset[i + look_back, 0])
return np.array(dataX), np.array(dataY)
# Prepare data for LSTM
look_back = 1
trainX, trainY = create_dataset(train, look_back)
testX, testY = create_dataset(test, look_back)
trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1]))
# Create and fit the LSTM network
model = Sequential()
model.add(LSTM(4, input_shape=(1, look_back)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
model.fit(trainX, trainY, epochs=50, batch_size=1, verbose=0)
# Make predictions
trainPredict = model.predict(trainX)
testPredict = model.predict(testX)
# Invert predictions
trainPredict = scaler.inverse_transform(trainPredict)
trainY = scaler.inverse_transform([trainY])
testPredict = scaler.inverse_transform(testPredict)
testY = scaler.inverse_transform([testY])
# Calculate RMSE
trainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
testScore = np.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
# Prepare plot data
trainPredictPlot = np.empty_like(dataset)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
testPredictPlot = np.empty_like(dataset)
testPredictPlot[:, :] = np.nan
testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
# Create plot
fig, ax = plt.subplots(figsize=(12, 8))
ax.plot(scaler.inverse_transform(dataset), label='Original Data', color='blue')
ax.plot(trainPredictPlot, label='Training Predictions', linestyle='--', color='green')
ax.plot(testPredictPlot, label='Test Predictions', linestyle='--', color='red')
ax.set_xlabel('Time')
ax.set_ylabel('Stock Price')
ax.set_title('Stock Price Prediction')
ax.legend()
ax.grid(True, linestyle='--', alpha=0.7)
return fig, trainScore, testScore
# Streamlit UI
st.set_page_config(page_title="Stock Price Prediction with LSTM", layout="wide")
st.title("Stock Price Prediction with LSTM")
st.write("Upload the 2330TW.csv file to predict stock prices using LSTM.")
uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
if uploaded_file is not None:
with st.spinner('Predicting...'):
fig, train_score, test_score = predict_stock(uploaded_file)
st.pyplot(fig)
col1, col2 = st.columns(2)
with col1:
st.metric("Train Score (RMSE)", f"{train_score:.2f}")
with col2:
st.metric("Test Score (RMSE)", f"{test_score:.2f}")
st.markdown("---")
st.write("Created with ❤️ using Streamlit") |