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	import streamlit as st
	import matplotlib.pyplot as plt
	import numpy as np
	import pandas as pd
	import torch
	from chronos import ChronosPipeline
	from sklearn.metrics import r2_score, mean_squared_error
	import io

	# Function to make predictions and visualize the forecast
	def make_forecast(data, time_series_field, prediction_length, ground_truth=None):
	# Initialize Chronos pipeline
	pipeline = ChronosPipeline.from_pretrained(
	"amazon/chronos-t5-small",
	device_map="auto",
	torch_dtype=torch.bfloat16,
	)

	# Convert data to a compatible type
	time_series_data = data[time_series_field].astype(float)

	# Prepare context from selected time series field
	context = torch.tensor(time_series_data.values)

	# Make predictions
	forecast = pipeline.predict(context, prediction_length)

	# Calculate quantiles for visualization
	low, median, high = np.quantile(forecast[0].numpy(), [0.1, 0.5, 0.9], axis=0)

	# Plot historical data and forecast
	st.set_option('deprecation.showPyplotGlobalUse', False)
	plt.figure(figsize=(8, 4))
	plt.plot(time_series_data, color="royalblue", label="historical data")
	plt.plot(range(len(time_series_data), len(time_series_data) + prediction_length), median, color="tomato", label="median forecast")
	plt.fill_between(range(len(time_series_data), len(time_series_data) + prediction_length), low, high, color="tomato", alpha=0.3, label="80% prediction interval")
	plt.legend()
	plt.grid()
	st.pyplot()

	# Calculate evaluation metrics if ground truth is provided
	if ground_truth is not None:
	forecast_values = median[-len(ground_truth):]
	r2 = r2_score(ground_truth, forecast_values)
	rmse = np.sqrt(mean_squared_error(ground_truth, forecast_values))
	st.write(f"R-squared (R2): {r2:.4f}")
	st.write(f"Root Mean Squared Error (RMSE): {rmse:.4f}")

	# Return forecasted values as a list
	return median[-prediction_length:].tolist()


	# Streamlit app
	def main():
	st.title("Time Series Forecasting App")

	# Upload CSV file
	uploaded_file = st.file_uploader("Upload CSV file", type=["csv"])

	if uploaded_file is not None:
	data = pd.read_csv(uploaded_file)

	# Display uploaded data
	st.subheader("Uploaded Data")
	st.write(data)

	# Select time series field
	time_series_field = st.selectbox("Select the time series field", data.columns)

	# Enter prediction length
	prediction_length = st.number_input("Enter the prediction length", min_value=1, value=12)

	# Input ground truth values
	ground_truth_str = st.text_input("Enter ground truth values (comma-separated)", "")
	ground_truth = None
	if ground_truth_str:
	ground_truth = [float(x.strip()) for x in ground_truth_str.split(",")]

	# Button to make predictions
	if st.button("Make Forecast"):
	st.subheader("Forecast Visualization")
	forecast_values = make_forecast(data, time_series_field, prediction_length, ground_truth)
	st.write("Forecasted Values:", forecast_values)

	if __name__ == "__main__":
	main()