app.py

import streamlit as st
import pandas as pd
import torch
import matplotlib.pyplot as plt
import numpy as np
from datetime import datetime, timedelta
from models import ModelConfig, load_model_pipeline
from data import DataConfig, process_input, fetch_data_with_fallback

# Load the forecasting model pipeline
@st.cache_resource
def load_pipeline(model_name):
    """Load and cache the model pipeline"""
    return load_model_pipeline(model_name, device_map="cpu", dtype=torch.float32)

# Fetch data with caching
@st.cache_data(ttl=3600)  # Cache for 1 hour
def fetch_data(source_name, days_back=180):
    """Fetch data from specified source with caching"""
    return fetch_data_with_fallback(source_name, days_back)

# Streamlit app interface
st.title("⚡ Electricity Market Price Forecasting")
st.write("""
This application uses **Amazon Chronos** pretrained models for zero-shot time series forecasting on electricity market data.
Select a model, choose your data source, and evaluate forecasting performance with backtesting on real ERCOT prices.
""")

# Model selection
available_model_names = ModelConfig.get_model_names()

selected_model_name = st.selectbox(
    "Select Forecasting Model:",
    options=available_model_names,
    index=0  # Default to first model (Chronos-2)
)

# Load the selected model
with st.spinner(f"Loading {selected_model_name}..."):
    pipeline = load_pipeline(selected_model_name)

# Data source selection
available_sources = DataConfig.get_source_names()
data_source = st.radio(
    "Select Data Source:",
    available_sources + ["Custom Data"],
    index=0
)

# Input field for user-provided data
if data_source == "Custom Data":
    user_input = st.text_area(
        "Enter time series data (comma-separated values):", 
        "",
        height=150
    )
    data_source_used = "Custom"
    error_msg = None
else:
    # Fetch data from selected source
    with st.spinner(f"Fetching data from {data_source}..."):
        default_data, data_source_used, error_msg = fetch_data(data_source)
    
    if error_msg:
        st.warning(f"⚠️ {error_msg}\nUsing sample data instead.")
    
    user_input = st.text_area(
        f"{data_source_used} - Daily Average Prices ($/MWh):", 
        default_data.strip(),
        height=150
    )
    if "ERCOT" in data_source_used:
        st.info("💡 Live data from ERCOT's Day-Ahead Market (DAM SPP) - Daily average prices across all settlement points")

try:
    time_series_data = process_input(user_input)
except ValueError:
    st.error("Please make sure all values are numbers, separated by commas.")
    time_series_data = []  # Set empty data on error to prevent further processing

# Select the forecast window for backtesting
max_test_days = min(64, len(time_series_data) - 10) if len(time_series_data) > 10 else 1
prediction_length = st.slider(
    "Forecast Horizon (Days to Backtest)", 
    min_value=1, 
    max_value=max_test_days, 
    value=min(14, max_test_days),
    help="The model will use historical context to forecast the last N days, then compare predictions with actual values to evaluate performance."
)

# If data is valid, perform the forecast
if time_series_data:
    # Split data into context (historical) and test
    context_length = len(time_series_data) - prediction_length
    context_data = time_series_data[:context_length]
    test_data = time_series_data[context_length:]
    
    # Create timestamps
    end_date = datetime.now()
    start_date = end_date - timedelta(days=len(time_series_data) - 1)
    all_dates = pd.date_range(start=start_date, periods=len(time_series_data), freq='D')
    context_dates = all_dates[:context_length]
    test_dates = all_dates[context_length:]
    
    # Create a DataFrame with context for the model
    context_df = pd.DataFrame({
        'timestamp': context_dates,
        'target': context_data,
        'id': 'ercot_prices'
    })

    # Make the forecast using the model
    with st.spinner("Generating forecast..."):
        pred_df = pipeline.predict_df(
            context_df,
            prediction_length=prediction_length,
            quantile_levels=[0.1, 0.5, 0.9],
            id_column="id",
            timestamp_column="timestamp",
            target="target",
        )

    # Extract predictions
    median = pred_df["predictions"].values
    low = pred_df["0.1"].values
    high = pred_df["0.9"].values

    # Calculate error metrics
    mae = np.mean(np.abs(np.array(test_data) - median))
    mape = np.mean(np.abs((np.array(test_data) - median) / np.array(test_data))) * 100
    rmse = np.sqrt(np.mean((np.array(test_data) - median) ** 2))

    # Plot the historical and forecasted data with dates
    plt.figure(figsize=(14, 7))
    plt.plot(context_dates, context_data, color="royalblue", label="Historical Context", linewidth=2)
    plt.plot(test_dates, test_data, color="green", label="Actual Values", linewidth=2, marker='o', markersize=4)
    plt.plot(test_dates, median, color="tomato", label="Forecast", linewidth=2, linestyle='--', marker='s', markersize=4)
    plt.fill_between(test_dates, low, high, color="tomato", alpha=0.3, label="80% Prediction Interval")
    plt.axvline(x=context_dates[-1], color='gray', linestyle=':', linewidth=1, alpha=0.7)
    plt.text(context_dates[-1], plt.ylim()[1]*0.95, ' Forecast Window', fontsize=10, color='gray')
    plt.xlabel("Date")
    plt.ylabel("Price ($/MWh)")
    plt.title(f"ERCOT Electricity Price Forecast - {prediction_length} Day Test Window")
    plt.legend(loc='best')
    plt.grid(alpha=0.3)
    plt.xticks(rotation=45)
    plt.tight_layout()

    # Show the plot in the Streamlit app
    st.pyplot(plt)
    
    # Display forecast statistics and error metrics
    st.write("### Model Performance Metrics")
    col1, col2, col3, col4 = st.columns(4)
    with col1:
        st.metric("MAE", f"${mae:.2f}")
    with col2:
        st.metric("RMSE", f"${rmse:.2f}")
    with col3:
        st.metric("MAPE", f"{mape:.2f}%")
    with col4:
        st.metric("Avg Actual", f"${np.mean(test_data):.2f}/MWh")
    
    # Show detailed comparison table
    with st.expander("View Detailed Comparison"):
        comparison_df = pd.DataFrame({
            'Date': test_dates.strftime('%Y-%m-%d'),
            'Actual': test_data,
            'Forecast': median.round(2),
            'Error': (median - np.array(test_data)).round(2),
            'Error %': ((median - np.array(test_data)) / np.array(test_data) * 100).round(2)
        })
        st.dataframe(comparison_df, use_container_width=True)

# Note for comments, feedback, or questions
st.write("### About")
st.write("""
**Features:**
- 🤖 Multiple pretrained models (7 options: Chronos-2, Chronos-T5, TiRex)
- 📊 Real-time ERCOT electricity market data (180+ days)
- 🎯 Backtesting with error metrics (MAE, RMSE, MAPE)
- 📈 Visual comparison of forecasts vs actual values
- 🔧 Modular architecture for easy extension

**Models:**
- **Chronos-2**: Amazon's latest (46M-120M params)
- **Chronos-T5**: Original family (8M-710M params)
- **TiRex**: NX-AI's xLSTM-based model (35M params)

For questions or feedback, reach out on [LinkedIn](https://www.linkedin.com/in/javadbayazi/).
""")