gradio_modal.py

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
"""