app.py

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.")