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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 (Assume UTF-8 encoding for Hugging Face)
dataframe = pd.read_csv(file.name, usecols=[1], engine='python')
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(share=True) # Set share=True for Hugging Face deployment
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