app.py

import streamlit as st
import pandas as pd
import torch
from chronos import ChronosPipeline
import matplotlib.pyplot as plt
import numpy as np

# Load the Chronos Pipeline model
@st.cache_resource
def load_pipeline():
    pipeline = ChronosPipeline.from_pretrained(
        "amazon/chronos-t5-small",
        device_map="cpu",  # Change to CPU
        torch_dtype=torch.float32,  # Use float32 for CPU
    )
    return pipeline

pipeline = load_pipeline()

# Streamlit app interface
st.title("Time Series Forecasting Demo with Deep Learning models")
st.write("This demo uses the ChronosPipeline model for time series forecasting.")

# Default time series data (comma-separated)
default_data = """
112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118, 115, 126, 141, 135, 125, 149, 170, 170, 158, 
133, 114, 140, 145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166, 171, 180, 193, 181, 183, 218, 
230, 242, 209, 191, 172, 194, 196, 196, 236, 235, 229, 243, 264, 272, 237, 211, 180, 201, 204, 188, 235, 
227, 234, 264, 302, 293, 259, 229, 203, 229, 242, 233, 267, 269, 270, 315, 364, 347, 312, 274, 237, 278, 
284, 277, 317, 313, 318, 374, 413, 405, 355, 306, 271, 306, 315, 301, 356, 348, 355, 422, 465, 467, 404, 
347, 305, 336, 340, 318, 362, 348, 363, 435, 491, 505, 404, 359, 310, 337, 360, 342, 406, 396, 420, 472, 
548, 559, 463, 407, 362, 405, 417, 391, 419, 461, 472, 535, 622, 606, 508, 461, 390, 432
"""

# Input field for user-provided data
user_input = st.text_area(
    "Enter time series data (comma-separated values):", 
    default_data.strip()
)

# Convert user input into a list of numbers
def process_input(input_str):
    return [float(x.strip()) for x in input_str.split(",")]

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 number of months for forecasting
prediction_length = st.slider("Select Forecast Horizon (Months)", min_value=1, max_value=64, value=12)

# If data is valid, perform the forecast
if time_series_data:
    # Convert the data to a tensor
    context = torch.tensor(time_series_data, dtype=torch.float32)

    # Make the forecast
    forecast = pipeline.predict(
        context=context,
        prediction_length=prediction_length,
        num_samples=20,
    )

    # Prepare forecast data for plotting
    forecast_index = range(len(time_series_data), len(time_series_data) + prediction_length)
    low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0)

    # Plot the historical and forecasted data
    plt.figure(figsize=(8, 4))
    plt.plot(time_series_data, color="royalblue", label="Historical data")
    plt.plot(forecast_index, median, color="tomato", label="Median forecast")
    plt.fill_between(forecast_index, low, high, color="tomato", alpha=0.3, label="80% prediction interval")
    plt.legend()
    plt.grid()

    # Show the plot in the Streamlit app
    st.pyplot(plt)

# Note for comments, feedback, or questions
st.write("### Notes")
st.write("For comments, feedback, or any questions, please reach out to me on [LinkedIn](https://www.linkedin.com/in/mjdarvishi/).")