Create app.py

app.py

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+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
+from tqdm import tqdm  # Progress bar
+
+# 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 with progress bar
+    for epoch in tqdm(range(epochs), desc="Training Progress"):
+        model.train()
+        epoch_loss = 0
+        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()
+            epoch_loss += loss.item()
+    
+    # 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)]))
+
+    # Calculate Root Mean Squared Error (RMSE)
+    rmse = np.sqrt(np.mean((test_plot[train_size + lookback:len(timeseries)] - timeseries[train_size + lookback:len(timeseries)])**2))
+
+    return fig, f"Mean Absolute Error (MAE) on Test Data: {mae:.4f}, Root Mean Squared Error (RMSE): {rmse:.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()