Spaces:
Sleeping
Sleeping
| import pandas as pd | |
| import numpy as np | |
| import torch | |
| import torch.nn as nn | |
| import torch.optim as optim | |
| import torch.utils.data as data | |
| import gradio as gr | |
| import plotly.graph_objects as go | |
| # Function to create dataset for time series prediction | |
| def create_dataset(dataset, lookback): | |
| X, y = [], [] | |
| for i in range(len(dataset) - lookback): | |
| feature = dataset[i:i + lookback] | |
| target = dataset[i + 1:i + lookback + 1] | |
| X.append(feature) | |
| y.append(target) | |
| X = np.array(X).reshape(-1, lookback, 1) # Reshape to 3D (samples, lookback, features) | |
| y = np.array(y).reshape(-1, lookback, 1) # Reshape to 3D (samples, lookback, features) | |
| return torch.tensor(X).float(), torch.tensor(y).float() | |
| # Define LSTM model | |
| class AirModel(nn.Module): | |
| def __init__(self): | |
| super(AirModel, self).__init__() | |
| self.lstm = nn.LSTM(input_size=1, hidden_size=50, num_layers=1, batch_first=True) | |
| self.linear = nn.Linear(50, 1) | |
| def forward(self, x): | |
| x, _ = self.lstm(x) | |
| x = self.linear(x) | |
| return x | |
| # Training and prediction function | |
| def train_and_predict(csv_file, lookback, epochs, batch_size): | |
| # Load CSV | |
| df = pd.read_csv(csv_file.name) | |
| # Extract time series data | |
| timeseries = df[["AmtNet Sales USD"]].values.astype('float32') | |
| # Train-test split | |
| train_size = int(len(timeseries) * 0.67) | |
| test_size = len(timeseries) - train_size | |
| train, test = timeseries[:train_size], timeseries[train_size:] | |
| # Create datasets | |
| X_train, y_train = create_dataset(train, lookback=lookback) | |
| X_test, y_test = create_dataset(test, lookback=lookback) | |
| if len(X_train) == 0 or len(X_test) == 0: | |
| return "The lookback value is too large for the dataset. Please reduce the lookback value." | |
| # DataLoader for batching | |
| train_loader = data.DataLoader(data.TensorDataset(X_train, y_train), shuffle=True, batch_size=batch_size) | |
| # Initialize model, optimizer, and loss function | |
| model = AirModel() | |
| optimizer = optim.Adam(model.parameters()) | |
| loss_fn = nn.MSELoss() | |
| # Training loop | |
| for epoch in range(epochs): | |
| model.train() | |
| for X_batch, y_batch in train_loader: | |
| y_pred = model(X_batch) | |
| loss = loss_fn(y_pred, y_batch) | |
| optimizer.zero_grad() | |
| loss.backward() | |
| optimizer.step() | |
| # Prediction | |
| model.eval() | |
| with torch.no_grad(): | |
| train_plot = np.ones_like(timeseries) * np.nan | |
| train_plot[lookback:train_size] = model(X_train)[:, -1, :].numpy() | |
| test_plot = np.ones_like(timeseries) * np.nan | |
| test_plot[train_size + lookback:len(timeseries)] = model(X_test)[:, -1, :].numpy() | |
| # Plot results with Plotly | |
| fig = go.Figure() | |
| fig.add_trace(go.Scatter(y=timeseries.squeeze(), mode='lines', name='Original Data')) | |
| fig.add_trace(go.Scatter(y=train_plot.squeeze(), mode='lines', name='Train Prediction', line=dict(color='red'))) | |
| fig.add_trace(go.Scatter(y=test_plot.squeeze(), mode='lines', name='Test Prediction', line=dict(color='green'))) | |
| fig.update_layout(title="Time Series Prediction", xaxis_title="Time", yaxis_title="Sales") | |
| # Calculate Mean Absolute Error (MAE) | |
| mae = np.mean(np.abs(test_plot[train_size + lookback:len(timeseries)] - timeseries[train_size + lookback:len(timeseries)])) | |
| return fig, f"Mean Absolute Error (MAE) on Test Data: {mae:.4f}" | |
| # Gradio app interface using new API | |
| interface = gr.Interface( | |
| fn=train_and_predict, | |
| inputs=[ | |
| gr.File(label="Upload your CSV file"), | |
| gr.Slider(10, 365, step=1, value=100, label="Lookback"), | |
| gr.Slider(100, 5000, step=100, value=1000, label="Epochs"), | |
| gr.Slider(4, 32, step=1, value=8, label="Batch size") | |
| ], | |
| outputs=[ | |
| gr.Plot(label="Prediction Plot"), | |
| gr.Textbox(label="Error Metrics") | |
| ], | |
| title="Time Series Prediction with LSTM", | |
| description="Upload a CSV file with a 'Amount Net Sales' column and get time series predictions using an LSTM model.", | |
| ) | |
| # Launch the app with a shareable link | |
| interface.launch() | |