Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, LSTM
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import mean_squared_error
+import tensorflow as tf
+import io
+
+# 函數定義
+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)
+
+def train_and_predict(file):
+    # 載入和預處理數據
+    dataframe = pd.read_csv(file, usecols=[1], engine='python')
+    dataset = dataframe.values.astype('float32')
+    
+    # 正規化數據集
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    dataset = scaler.fit_transform(dataset)
+    
+    # 分割訓練集和測試集
+    train_size = int(len(dataset) * 0.67)
+    test_size = len(dataset) - train_size
+    train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]
+    
+    # 重塑為 X=t 和 Y=t+1
+    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]))
+    
+    # 創建和訓練 LSTM 網絡
+    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)
+    
+    # 進行預測
+    trainPredict = model.predict(trainX)
+    testPredict = model.predict(testX)
+    
+    # 反轉預測
+    trainPredict = scaler.inverse_transform(trainPredict)
+    trainY = scaler.inverse_transform([trainY])
+    testPredict = scaler.inverse_transform(testPredict)
+    testY = scaler.inverse_transform([testY])
+    
+    # 計算 RMSE
+    trainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
+    testScore = np.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
+    
+    # 準備繪圖數據
+    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
+    
+    return scaler.inverse_transform(dataset), trainPredictPlot, testPredictPlot, trainScore, testScore
+
+st.title("LSTM Stock Price Prediction")
+
+uploaded_file = st.file_uploader("Choose a CSV file", type="csv")
+
+if uploaded_file is not None:
+    # 讀取文件並進行預測
+    original_data, train_predict, test_predict, train_score, test_score = train_and_predict(uploaded_file)
+    
+    # 顯示結果
+    st.subheader("Prediction Results")
+    
+    # 繪製圖表
+    fig, ax = plt.subplots(figsize=(12, 6))
+    ax.plot(original_data, label='Original Data', color='blue')
+    ax.plot(train_predict, label='Training Predictions', linestyle='--', color='green')
+    ax.plot(test_predict, label='Test Predictions', linestyle='--', color='red')
+    ax.set_xlabel('Time')
+    ax.set_ylabel('Stock Price')
+    ax.set_title('Original Data and Predictions')
+    ax.legend()
+    ax.grid(True, linestyle='--', alpha=0.7)
+    st.pyplot(fig)
+    
+    # 顯示評分
+    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}")
+
+else:
+    st.info("Please upload a CSV file to start the prediction.")
+
+st.markdown("""
+This application uses an LSTM (Long Short-Term Memory) neural network to predict stock prices based on historical data.
+
+To use:
+1. Upload a CSV file containing historical stock price data.
+2. The app will train an LSTM model on this data and make predictions.
+3. You'll see a graph showing the original data and predictions, along with RMSE scores for training and test sets.
+
+Note: The CSV file should have the stock prices in the second column.
+""")