import streamlit as st import torch import random import numpy as np import yaml import logging import os import matplotlib.pyplot as plt from pathlib import Path import tempfile import traceback from data_utils import ( save_uploaded_files, load_dataset, ) from inference_utils import run_inference from config_utils import load_config from plot_utils import plot_prithvi_output, plot_aurora_output from prithvi_utils import ( prithvi_config_ui, initialize_prithvi_model, prepare_prithvi_batch ) from aurora_utils import aurora_config_ui, prepare_aurora_batch, initialize_aurora_model from pangu_utils import ( pangu_config_data, inference_1hr, inference_3hrs, inference_6hrs, inference_24hrs, inference_custom_hrs, plot_pangu_output, ) from fengwu_utils import (fengwu_config_data, inference_6hrs_fengwu, inference_12hrs_fengwu, inference_custom_hrs_fengwu, plot_fengwu_output) logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Set page configuration st.set_page_config( page_title="Weather Data Processor", layout="wide", initial_sidebar_state="expanded", ) header_col1, header_col2 = st.columns([4, 1]) with header_col1: st.title("🌦️ Weather & Climate Data Processor and Forecaster") with header_col2: st.markdown("### Select a Model") selected_model = st.selectbox( "", options=["Pangu-Weather", "FengWu", "Aurora", "Climax", "Prithvi", "GEOS-Specific-LSTM", "GEOS-Finetuned-Climax"], index=0, key="model_selector", help="Select the model you want to use." ) st.write("---") # --- Layout: Two Columns --- left_col, right_col = st.columns([1, 2]) with left_col: st.header("🔧 Configuration") # Dynamically show configuration UI based on selected model if selected_model == "Prithvi": (config, uploaded_surface_files, uploaded_vertical_files, clim_surf_path, clim_vert_path, config_path, weights_path) = prithvi_config_ui() elif selected_model == "Climax": st.info("Climax model is not yet available.") st.stop() elif selected_model == "GEOS-Specific-LSTM": st.info("GEOS-Specific-LSTM model is not yet available.") st.stop() elif selected_model == "GEOS-Finetuned-Climax": st.info("GEOS-Finetuned-Climax model is not yet available.") st.stop() elif selected_model == "Aurora": uploaded_files = aurora_config_ui() elif selected_model == "Pangu-Weather": input_surface_file, input_upper_file = pangu_config_data() elif selected_model == "FengWu": input_file1_fengwu, input_file2_fengwu = fengwu_config_data() else: # Generic data upload for other models st.subheader(f"{selected_model} Model Data Upload") st.markdown("### Drag and Drop Your Data Files Here") uploaded_files = st.file_uploader( f"Upload Data Files for {selected_model}", accept_multiple_files=True, key=f"{selected_model.lower()}_uploader", type=["nc", "netcdf", "nc4"], ) st.write("---") # --- Forecast Duration Selection --- st.subheader("Forecast Duration") forecast_options = ["1 hour", "3 hours", "6 hours", "24 hours", "Custom"] selected_duration = st.selectbox( "Select forecast duration", forecast_options, index=3, # Default to 24 hours help="Select how many hours to forecast." ) custom_hours = None if selected_duration == "Custom": custom_hours = st.number_input( "Enter custom forecast hours", min_value=24, max_value=480, value=48, step=24, help="Enter the number of hours you want to forecast." ) st.write("---") # Run Inference button if st.button("🚀 Run Inference"): with right_col: st.header("📈 Inference Progress & Visualization") # Set seeds and device try: torch.jit.enable_onednn_fusion(True) if torch.cuda.is_available(): device = torch.device("cuda") st.write(f"Using device: **{torch.cuda.get_device_name()}**") torch.backends.cudnn.benchmark = True torch.backends.cudnn.deterministic = True else: device = torch.device("cpu") st.write("Using device: **CPU**") random.seed(42) if torch.cuda.is_available(): torch.cuda.manual_seed(42) torch.manual_seed(42) np.random.seed(42) except Exception: st.error("Error initializing device:") st.error(traceback.format_exc()) st.stop() # Use a spinner while running inference with st.spinner("Running inference, please wait..."): # Initialize and run inference for selected model if selected_model == "Prithvi": model, in_mu, in_sig, output_sig, static_mu, static_sig = initialize_prithvi_model( config, config_path, weights_path, device ) batch = prepare_prithvi_batch( uploaded_surface_files, uploaded_vertical_files, clim_surf_path, clim_vert_path, device ) out = run_inference(selected_model, model, batch, device) # Store results st.session_state['prithvi_out'] = out st.session_state['prithvi_done'] = True elif selected_model == "Aurora": if uploaded_files: save_uploaded_files(uploaded_files) ds = load_dataset(st.session_state.temp_file_paths) if ds is not None: batch = prepare_aurora_batch(ds) model = initialize_aurora_model(device) out = run_inference(selected_model, model, batch, device) st.session_state['aurora_out'] = out st.session_state['aurora_ds_subset'] = ds st.session_state['aurora_done'] = True else: st.error("Failed to load dataset for Aurora.") st.stop() else: st.error("Please upload data files for Aurora.") st.stop() elif selected_model == "FengWu": if input_file1_fengwu and input_file2_fengwu: try: input1 = np.load(input_file1_fengwu) input2 = np.load(input_file2_fengwu) if selected_duration == "1 hour": st.warning("1hr inference is not yet available on this model.") elif selected_duration == "3 hours": st.warning("3hrs inference is not yet available on this model.") elif selected_duration == "6 hours": output_fengwu = inference_6hrs_fengwu(input1, input2) elif selected_duration == "12 hours": output_fengwu = inference_12hrs_fengwu(input1, input2) else: output_fengwu = inference_custom_hrs_fengwu(input1, input2, custom_hours) st.session_state['output_fengwu'] = output_fengwu st.session_state['fengwu_done'] = True st.session_state['input_fengwu'] = input_file2_fengwu except Exception as e: st.error(f"An error occurred: {e}") else: st.error("Please upload data files for Aurora.") st.stop() elif selected_model == "Pangu-Weather": if input_surface_file and input_upper_file: try: surface_data = np.load(input_surface_file) upper_data = np.load(input_upper_file) # Decide which inference function to use based on selection if selected_duration == "1 hour": out_upper, out_surface = inference_1hr(upper_data, surface_data) elif selected_duration == "3 hours": out_upper, out_surface = inference_3hrs(upper_data, surface_data) elif selected_duration == "6 hours": out_upper, out_surface = inference_6hrs(upper_data, surface_data) elif selected_duration == "24 hours": out_upper, out_surface = inference_24hrs(upper_data, surface_data) else: out_upper, out_surface = inference_custom_hrs(upper_data, surface_data, custom_hours) # Store results in session_state st.session_state['pangu_upper_data'] = upper_data st.session_state['pangu_surface_data'] = surface_data st.session_state['pangu_out_upper'] = out_upper st.session_state['pangu_out_surface'] = out_surface st.session_state['pangu_done'] = True st.write("**Forecast Results:**") st.write("Upper Data Forecast Shape:", out_upper.shape) st.write("Surface Data Forecast Shape:", out_surface.shape) except Exception as e: st.error(f"An error occurred: {e}") else: st.error("Please upload data files for Pangu-Weather.") st.stop() else: st.warning("Inference not implemented for this model.") st.stop() # Visualization after inference is done if selected_model == "Prithvi": if 'prithvi_done' in st.session_state and st.session_state['prithvi_done']: plot_prithvi_output(st.session_state['prithvi_out']) elif selected_model == "Aurora": if 'aurora_done' in st.session_state and st.session_state['aurora_done']: plot_aurora_output(st.session_state['aurora_out'], st.session_state['aurora_ds_subset']) elif selected_model == "FengWu": if 'fengwu_done' in st.session_state and st.session_state['fengwu_done']: plot_fengwu_output(st.session_state['input_fengwu'], st.session_state['output_fengwu']) elif selected_model == "Pangu-Weather": if 'pangu_done' in st.session_state and st.session_state['pangu_done']: plot_pangu_output( st.session_state['pangu_upper_data'], st.session_state['pangu_surface_data'], st.session_state['pangu_out_upper'], st.session_state['pangu_out_surface'] ) else: st.info("No visualization implemented for this model.") else: # If not running inference now, but we have previously computed results, show them with right_col: st.header("🖥️ Visualization & Progress") # Check which model was selected and if we have done inference before if selected_model == "Prithvi" and 'prithvi_done' in st.session_state and st.session_state['prithvi_done']: plot_prithvi_output(st.session_state['prithvi_out']) elif selected_model == "Aurora" and 'aurora_done' in st.session_state and st.session_state['aurora_done']: plot_aurora_output(st.session_state['aurora_out'], st.session_state['aurora_ds_subset']) elif selected_model == "Pangu-Weather" and 'pangu_done' in st.session_state and st.session_state['pangu_done']: plot_pangu_output( st.session_state['pangu_upper_data'], st.session_state['pangu_surface_data'], st.session_state['pangu_out_upper'], st.session_state['pangu_out_surface'] ) elif selected_model == "FengWu" and 'output_fengwu' in st.session_state and st.session_state['fengwu_done']: plot_fengwu_output(st.session_state['input_fengwu'], st.session_state['output_fengwu']) else: st.info("Awaiting inference to display results.")