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import os
import gradio as gr
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import torch
from datetime import datetime, timedelta
import io
import base64
from typing import Optional, Tuple, Dict, Any
import warnings
warnings.filterwarnings('ignore')
# Mock implementations for the original imports
# In actual deployment, you'd import these from the original modules
class MaskedTimeseries:
def __init__(self, series, padding_mask, id_mask, timestamp_seconds, time_interval_seconds):
self.series = series
self.padding_mask = padding_mask
self.id_mask = id_mask
self.timestamp_seconds = timestamp_seconds
self.time_interval_seconds = time_interval_seconds
class MockToto:
"""Mock Toto model for demonstration"""
def __init__(self):
self.model = self
@classmethod
def from_pretrained(cls, model_name):
return cls()
def to(self, device):
return self
def compile(self):
return self
class MockForecaster:
"""Mock forecaster for demonstration"""
def __init__(self, model):
self.model = model
def forecast(self, inputs, prediction_length, num_samples, samples_per_batch, use_kv_cache=True):
# Generate mock forecast data
n_variates, context_length = inputs.series.shape
# Create realistic-looking synthetic forecasts
samples = []
for _ in range(num_samples):
# Use last values as starting point and add some trend/noise
last_values = inputs.series[:, -1:]
forecast_sample = []
for t in range(prediction_length):
# Add some trend and noise
trend = torch.randn(n_variates, 1) * 0.1
noise = torch.randn(n_variates, 1) * 0.5
next_val = last_values + trend + noise
forecast_sample.append(next_val)
last_values = next_val
sample = torch.cat(forecast_sample, dim=1)
samples.append(sample)
# Stack samples along a new dimension
forecast_tensor = torch.stack(samples, dim=-1) # shape: (n_variates, prediction_length, num_samples)
class MockForecast:
def __init__(self, samples):
self.samples = MockSamples(samples)
class MockSamples:
def __init__(self, tensor):
self.tensor = tensor
def squeeze(self):
return self.tensor
def cpu(self):
return self.tensor
def quantile(self, q, dim):
# Calculate quantiles along the specified dimension
sorted_tensor = torch.sort(self.tensor, dim=dim)[0]
indices = (q.unsqueeze(0).unsqueeze(0) * (self.tensor.shape[dim] - 1)).long()
return torch.gather(sorted_tensor, dim, indices.expand(sorted_tensor.shape[0], sorted_tensor.shape[1], -1).permute(2, 0, 1))
return MockForecast(forecast_tensor)
# Global variables
toto_model = None
forecaster = None
def initialize_model():
"""Initialize the Toto model"""
global toto_model, forecaster
if toto_model is None:
# In production, replace with: toto_model = Toto.from_pretrained('Datadog/Toto-Open-Base-1.0')
toto_model = MockToto()
toto_model.to("cpu") # Use CPU for broader compatibility
toto_model.compile()
forecaster = MockForecaster(toto_model.model)
return toto_model, forecaster
def load_sample_data():
"""Load sample ETT data for demonstration"""
# Generate synthetic ETT-like data
dates = pd.date_range(start='2020-01-01', end='2020-12-31 23:45:00', freq='15T')
n_points = len(dates)
# Create synthetic multivariate time series
t = np.arange(n_points)
# Base patterns with different frequencies and amplitudes
hufl = 5 + 2 * np.sin(2 * np.pi * t / (24 * 4)) + 0.5 * np.sin(2 * np.pi * t / (24 * 4 * 7)) + np.random.normal(0, 0.3, n_points)
hull = 4 + 1.5 * np.cos(2 * np.pi * t / (24 * 4)) + 0.3 * np.sin(2 * np.pi * t / (24 * 4 * 30)) + np.random.normal(0, 0.25, n_points)
mufl = 6 + 1.8 * np.sin(2 * np.pi * t / (24 * 4)) + 0.4 * np.cos(2 * np.pi * t / (24 * 4 * 7)) + np.random.normal(0, 0.35, n_points)
mull = 5.5 + 1.2 * np.cos(2 * np.pi * t / (24 * 4)) + 0.6 * np.sin(2 * np.pi * t / (24 * 4 * 14)) + np.random.normal(0, 0.28, n_points)
lufl = 3.5 + 2.2 * np.sin(2 * np.pi * t / (24 * 4)) + 0.8 * np.cos(2 * np.pi * t / (24 * 4 * 21)) + np.random.normal(0, 0.32, n_points)
lull = 4.2 + 1.6 * np.cos(2 * np.pi * t / (24 * 4)) + 0.5 * np.sin(2 * np.pi * t / (24 * 4 * 10)) + np.random.normal(0, 0.27, n_points)
ot = 25 + 8 * np.sin(2 * np.pi * t / (24 * 4)) + 3 * np.cos(2 * np.pi * t / (24 * 4 * 365)) + np.random.normal(0, 1.2, n_points)
df = pd.DataFrame({
'date': dates,
'HUFL': hufl,
'HULL': hull,
'MUFL': mufl,
'MULL': mull,
'LUFL': lufl,
'LULL': lull,
'OT': ot
})
df['timestamp_seconds'] = (df['date'] - pd.Timestamp("1970-01-01")) // pd.Timedelta('1s')
return df
def prepare_data(df: pd.DataFrame, context_length: int, prediction_length: int) -> Tuple[MaskedTimeseries, pd.DataFrame, pd.DataFrame]:
"""Prepare data for Toto model"""
feature_columns = ["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL", "OT"]
n_variates = len(feature_columns)
interval = 60 * 15 # 15-min intervals
# Ensure we have enough data
if len(df) < (context_length + prediction_length):
raise ValueError(f"Dataset too small. Need at least {context_length + prediction_length} points, got {len(df)}")
input_df = df.iloc[-(context_length + prediction_length):-prediction_length].copy()
target_df = df.iloc[-prediction_length:].copy()
input_series = torch.from_numpy(input_df[feature_columns].values.T).to(torch.float)
timestamp_seconds = torch.from_numpy(input_df.timestamp_seconds.values).expand((n_variates, context_length))
time_interval_seconds = torch.full((n_variates,), interval)
inputs = MaskedTimeseries(
series=input_series,
padding_mask=torch.full_like(input_series, True, dtype=torch.bool),
id_mask=torch.zeros_like(input_series),
timestamp_seconds=timestamp_seconds,
time_interval_seconds=time_interval_seconds,
)
return inputs, input_df, target_df
def create_forecast_plot(input_df: pd.DataFrame, target_df: pd.DataFrame, forecast, feature_columns: list) -> plt.Figure:
"""Create forecast visualization"""
DARK_GREY = "#1c2b34"
BLUE = "#3598ec"
PURPLE = "#7463e1"
LIGHT_PURPLE = "#d7c3ff"
PINK = "#ff0099"
fig = plt.figure(figsize=(16, 12), dpi=100)
fig.suptitle("Toto Time Series Forecasts", fontsize=16, fontweight='bold')
n_variates = len(feature_columns)
for i, feature in enumerate(feature_columns):
plt.subplot(n_variates, 1, i + 1)
if i != n_variates - 1:
plt.gca().set_xticklabels([])
plt.gca().tick_params(axis="x", color=DARK_GREY, labelcolor=DARK_GREY)
plt.gca().tick_params(axis="y", color=DARK_GREY, labelcolor=DARK_GREY)
plt.ylabel(feature, rotation=0, ha='right', va='center')
# Set x-axis limits
context_points = min(960, len(input_df))
plt.xlim(input_df.date.iloc[-context_points], target_df.date.iloc[-1])
# Vertical line separating context and forecast
plt.axvline(target_df.date.iloc[0], color=PINK, linestyle=":", alpha=0.8, linewidth=2)
# Plot historical data
plt.plot(input_df["date"].iloc[-context_points:], input_df[feature].iloc[-context_points:],
color=BLUE, linewidth=1.5, label='Historical' if i == 0 else None)
# Plot ground truth in forecast period
plt.plot(target_df["date"], target_df[feature], color=BLUE, linewidth=1.5, alpha=0.7,
label='Actual' if i == 0 else None)
# Plot median forecast
forecast_median = np.median(forecast.samples.squeeze()[i].cpu().numpy(), axis=-1)
plt.plot(target_df["date"], forecast_median, color=PURPLE, linestyle="--", linewidth=2,
label='Forecast' if i == 0 else None)
# Plot confidence intervals
alpha = 0.05
device = torch.device('cpu')
qs = forecast.samples.quantile(q=torch.tensor([alpha, 1 - alpha], device=device), dim=-1)
plt.fill_between(
target_df["date"],
qs[0].squeeze()[i].cpu().numpy(),
qs[1].squeeze()[i].cpu().numpy(),
color=LIGHT_PURPLE,
alpha=0.6,
label=f'{int((1-2*alpha)*100)}% CI' if i == 0 else None
)
if i == 0:
plt.legend(loc='upper left', frameon=True, fancybox=True, shadow=True)
plt.tight_layout()
return fig
def run_forecast(context_length: int, prediction_length: int, num_samples: int,
samples_per_batch: int, use_kv_cache: bool, progress=gr.Progress()) -> Tuple[plt.Figure, str]:
"""Run forecasting with given parameters"""
try:
progress(0.1, desc="Initializing model...")
model, forecaster = initialize_model()
progress(0.2, desc="Loading data...")
df = load_sample_data()
progress(0.3, desc="Preparing data...")
inputs, input_df, target_df = prepare_data(df, context_length, prediction_length)
progress(0.5, desc="Running forecast...")
forecast = forecaster.forecast(
inputs,
prediction_length=prediction_length,
num_samples=num_samples,
samples_per_batch=min(samples_per_batch, num_samples),
use_kv_cache=use_kv_cache,
)
progress(0.8, desc="Creating visualization...")
feature_columns = ["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL", "OT"]
fig = create_forecast_plot(input_df, target_df, forecast, feature_columns)
progress(1.0, desc="Complete!")
# Generate summary statistics
forecast_data = forecast.samples.squeeze().cpu().numpy()
summary = f"""
## Forecast Summary
**Parameters Used:**
- Context Length: {context_length} time steps
- Prediction Length: {prediction_length} time steps
- Number of Samples: {num_samples}
- Samples per Batch: {samples_per_batch}
- KV Cache: {'Enabled' if use_kv_cache else 'Disabled'}
**Results:**
- Variables Forecasted: {len(feature_columns)}
- Forecast Shape: {forecast_data.shape}
- Mean Absolute Forecast Range: {np.mean(np.max(forecast_data, axis=1) - np.min(forecast_data, axis=1)):.3f}
The plot shows historical data in blue, actual values in the forecast period in light blue,
median forecasts as purple dashed lines, and 95% confidence intervals in light purple.
"""
return fig, summary
except Exception as e:
error_msg = f"Error during forecasting: {str(e)}"
fig = plt.figure(figsize=(10, 6))
plt.text(0.5, 0.5, error_msg, ha='center', va='center', fontsize=12, color='red')
plt.axis('off')
return fig, error_msg
# Create Gradio interface
def create_interface():
with gr.Blocks(title="Toto Time Series Forecasting", theme=gr.themes.Soft()) as demo:
gr.Markdown("""
# 🔮 Toto Time Series Forecasting
This app demonstrates zero-shot time series forecasting using the Toto foundation model.
Adjust the parameters below to customize your forecast and see how different settings affect the predictions.
**Note:** This demo uses synthetic ETT-like data for illustration purposes.
""")
with gr.Row():
with gr.Column(scale=1):
gr.Markdown("### Forecasting Parameters")
context_length = gr.Slider(
minimum=96, maximum=2048, value=512, step=32,
label="Context Length",
info="Number of historical time steps to use as input"
)
prediction_length = gr.Slider(
minimum=24, maximum=720, value=96, step=24,
label="Prediction Length",
info="Number of time steps to forecast into the future"
)
num_samples = gr.Slider(
minimum=8, maximum=512, value=64, step=8,
label="Number of Samples",
info="More samples = more stable predictions but slower inference"
)
samples_per_batch = gr.Slider(
minimum=8, maximum=256, value=32, step=8,
label="Samples per Batch",
info="Batch size for sample generation (affects memory usage)"
)
use_kv_cache = gr.Checkbox(
value=True,
label="Use KV Cache",
info="Enable key-value caching for faster inference"
)
forecast_btn = gr.Button("🚀 Run Forecast", variant="primary", size="lg")
with gr.Column(scale=2):
gr.Markdown("### Forecast Results")
forecast_plot = gr.Plot()
forecast_summary = gr.Markdown()
# Event handlers
forecast_btn.click(
fn=run_forecast,
inputs=[context_length, prediction_length, num_samples, samples_per_batch, use_kv_cache],
outputs=[forecast_plot, forecast_summary]
)
# Load initial forecast
demo.load(
fn=lambda: run_forecast(512, 96, 64, 32, True),
outputs=[forecast_plot, forecast_summary]
)
return demo
# For deployment
if __name__ == "__main__":
# Create and launch the interface
demo = create_interface()
# For local development
if os.getenv("GRADIO_DEV"):
demo.launch(debug=True, share=False)
else:
# For production deployment
demo.launch(server_name="0.0.0.0", server_port=7860, share=True)
# For Modal.com deployment, add this:
"""
# modal_app.py
import modal
image = modal.Image.debian_slim().pip_install([
"gradio",
"torch",
"numpy",
"pandas",
"matplotlib",
"transformers",
# Add other required packages
])
app = modal.App("toto-forecasting")
@app.function(image=image, gpu="T4")
def run_gradio():
from main import create_interface
demo = create_interface()
demo.launch(server_name="0.0.0.0", server_port=8000, share=False)
if __name__ == "__main__":
with app.run():
run_gradio()
"""
# For Hugging Face Spaces deployment:
"""
Create these files:
1. app.py (this file)
2. requirements.txt:
gradio
torch
numpy
pandas
matplotlib
transformers
3. README.md with your Space description
""" |