Create app.py

app.py

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+import pandas as pd
+import numpy as np
+import tensorflow as tf
+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 matplotlib.pyplot as plt
+import gradio as gr
+
+# fix random seed for reproducibility
+tf.random.set_seed(7)
+
+def train_and_predict(file, epochs):
+    # Load the dataset
+    dataframe = pd.read_csv(file.name, usecols=[1], engine='python', encoding="big5")
+    dataset = dataframe.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),:]
+
+    # Convert an array of values into a dataset matrix
+    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)
+
+    # Reshape into X=t and 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
+    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=epochs, batch_size=1, verbose=2)
+
+    # 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 root mean squared error
+    trainScore = np.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))
+    testScore = np.sqrt(mean_squared_error(testY[0], testPredict[:,0]))
+
+    # Plot predictions
+    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
+
+    plt.figure(figsize=(12, 8))
+    plt.plot(scaler.inverse_transform(dataset), label='Original Data')
+    plt.plot(trainPredictPlot, label='Training Predictions', linestyle='--')
+    plt.plot(testPredictPlot, label='Test Predictions', linestyle='--')
+    plt.xlabel('Time')
+    plt.ylabel('Scaled Values')
+    plt.title('Original Data and Predictions')
+    plt.legend()
+
+    return (f'Train Score: {trainScore:.2f} RMSE\nTest Score: {testScore:.2f} RMSE'), plt
+
+# Gradio interface
+file_input = gr.File(label="Upload CSV File")
+epochs_input = gr.Slider(minimum=1, maximum=100, value=50, label="Epochs")
+output_text = gr.Textbox(label="Training and Testing RMSE Scores")
+output_plot = gr.Plot(label="Original Data and Predictions")
+
+gr.Interface(
+    fn=train_and_predict,
+    inputs=[file_input, epochs_input],
+    outputs=[output_text, output_plot],
+    title="LSTM Model for Time Series Prediction",
+    description="Upload a CSV file with time series data and specify the number of epochs to train the model."
+).launch()