Update app.py

app.py

@@ -1,4 +1,4 @@
-import gradio as gr
+import streamlit as st
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
@@ -8,6 +8,13 @@ from sklearn.preprocessing import MinMaxScaler
 from sklearn.metrics import mean_squared_error
 import tensorflow as tf
 
+# 設置頁面配置
+st.set_page_config(page_title="LSTM Stock Price Prediction", layout="wide")
+
+# 標題
+st.title("LSTM Stock Price Prediction")
+
+# 函數定義
 def create_dataset(dataset, look_back=1):
     dataX, dataY = [], []
     for i in range(len(dataset)-look_back-1):
@@ -17,48 +24,48 @@ def create_dataset(dataset, look_back=1):
     return np.array(dataX), np.array(dataY)
 
 def train_and_predict(file):
-    # Load and preprocess data
-    dataframe = pd.read_csv(file.name, usecols=[1], engine='python', encoding="big5")
+    # 載入和預處理數據
+    dataframe = pd.read_csv(file, usecols=[1], engine='python', encoding="big5")
     dataset = dataframe.values.astype('float32')
     
-    # Normalize the dataset
+    # 正規化數據集
     scaler = MinMaxScaler(feature_range=(0, 1))
     dataset = scaler.fit_transform(dataset)
     
-    # Split into train and test sets
+    # 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),:]
     
-    # Reshape into X=t and Y=t+1
+    # 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]))
     
-    # Create and fit the LSTM network
+    # 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)
     
-    # Make predictions
+    # 進行預測
     trainPredict = model.predict(trainX)
     testPredict = model.predict(testX)
     
-    # Invert predictions
+    # 反轉預測
     trainPredict = scaler.inverse_transform(trainPredict)
     trainY = scaler.inverse_transform([trainY])
     testPredict = scaler.inverse_transform(testPredict)
     testY = scaler.inverse_transform([testY])
     
-    # Calculate RMSE
+    # 計算 RMSE
     trainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
     testScore = np.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
     
-    # Prepare plot data
+    # 準備繪圖數據
     trainPredictPlot = np.empty_like(dataset)
     trainPredictPlot[:, :] = np.nan
     trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict
@@ -66,31 +73,48 @@ def train_and_predict(file):
     testPredictPlot[:, :] = np.nan
     testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict
     
-    # Create plot
-    plt.figure(figsize=(12, 8))
-    plt.plot(scaler.inverse_transform(dataset), label='Original Data', color='blue')
-    plt.plot(trainPredictPlot, label='Training Predictions', linestyle='--', color='green')
-    plt.plot(testPredictPlot, label='Test Predictions', linestyle='--', color='red')
-    plt.xlabel('Time')
-    plt.ylabel('Scaled Values')
-    plt.title('Original Data and Predictions')
-    plt.legend()
-    plt.grid(True, linestyle='--', alpha=0.7)
+    return scaler.inverse_transform(dataset), trainPredictPlot, testPredictPlot, trainScore, testScore
+
+# 文件上傳
+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)
     
-    return plt, f'Train Score: {trainScore:.2f} RMSE', f'Test Score: {testScore:.2f} RMSE'
+    # 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.")
+
+# 添加一些說明文字
+st.markdown("""
+This application uses an LSTM (Long Short-Term Memory) neural network to predict stock prices based on historical data.
 
-# Create Gradio interface
-iface = gr.Interface(
-    fn=train_and_predict,
-    inputs=gr.File(label="Upload CSV file"),
-    outputs=[
-        gr.Plot(label="Predictions Plot"),
-        gr.Textbox(label="Train Score"),
-        gr.Textbox(label="Test Score")
-    ],
-    title="LSTM Stock Price Prediction",
-    description="Upload a CSV file with stock prices to train an LSTM model and see predictions."
-)
+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.
 
-# Launch the interface
-iface.launch()
+Note: The CSV file should have the stock prices in the second column.
+""")