Create app.py

app.py

@@ -0,0 +1,103 @@
+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")