app.py.orig2

import streamlit as st
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
import folium
import netCDF4 as nc
import os

# Create a Streamlit app
st.title('Rainfall Prediction - KMA')
st.sidebar.title('Upload NetCDF File')
uploaded_file = st.sidebar.file_uploader("Upload your NetCDF file", type=["nc"])

if uploaded_file is not None:
    # Load NetCDF data
    with open(uploaded_file.name, "wb") as f:
        f.write(uploaded_file.read())
    
    try:
        df = nc.Dataset(uploaded_file.name)
        df_var = df.variables['rain'][:]
        rain_array = np.array(df_var)

        # Load latitude and longitude data
        lat_data = pd.read_csv('./data/dongne_lat_info.txt', header=None).values
        lon_data = pd.read_csv('./data/dongne_lon_info.txt', header=None).values

        latitude_array = lat_data
        longitude_array = lon_data

        # Trim latitude and longitude data
        latitude_trimmed = latitude_array[:-1, :-1]
        longitude_trimmed = longitude_array[:-1, :-1]

        # Trim the rain_array to match the shape of trimmed latitude and longitude arrays
        rain_trimmed = rain_array[:latitude_trimmed.shape[0], :latitude_trimmed.shape[1]]

        # Display the heatmap
        aspect_ratio = (longitude_trimmed.max() - longitude_trimmed.min()) / (latitude_trimmed.max() - latitude_trimmed.min())
        fig = plt.figure(figsize=(6 * aspect_ratio, 6))
        plt.imshow(rain_trimmed, cmap='rainbow', extent=[longitude_trimmed.min(), longitude_trimmed.max(), latitude_trimmed.min(), latitude_trimmed.max()], vmax=5)
        plt.colorbar(label='mm/hr')
        plt.title('Rainfall_Pred_KMA')
        plt.xlabel('Longitude')
        plt.ylabel('Latitude')

        st.pyplot(fig)
    except Exception as e:
        st.write("Error during loading:", e)
    finally:
        os.remove(uploaded_file.name)  # Remove the temporary uploaded file
else:
    st.write("Please upload a NetCDF file using the sidebar.")