'first working example of the app'

app.py

@@ -1,5 +1,79 @@
 import streamlit as st
+import pandas as pd
+import torch
+from chronos import ChronosPipeline
+import matplotlib.pyplot as plt
+import numpy as np
 
-x = st.slider('Select a value')
-st.write(x, 'squared is', x * x)
+# 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)

requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+transformers
+torch