biomap/streamlit_app.py

import streamlit as st
from streamlit_folium import st_folium
import folium
import logging
import sys
import hydra
from plot_functions import *
import hydra

import torch
from model import LitUnsupervisedSegmenter
from helper import inference_on_location_and_month, inference_on_location

DEFAULT_LATITUDE = 48.81
DEFAULT_LONGITUDE = 2.98
DEFAULT_ZOOM = 5

MIN_YEAR = 2018
MAX_YEAR = 2024

FOLIUM_WIDTH = 925
FOLIUM_HEIGHT = 300


st.set_page_config(layout="wide")
@st.cache_resource 
def init_cfg(cfg_name):
    hydra.initialize(config_path="configs", job_name="corine")
    return hydra.compose(config_name=cfg_name)

@st.cache_resource 
def init_app(cfg_name) -> LitUnsupervisedSegmenter:
    file_handler = logging.FileHandler(filename='biomap.log')
    stdout_handler = logging.StreamHandler(stream=sys.stdout)
    handlers = [file_handler, stdout_handler]

    logging.basicConfig(handlers=handlers, encoding='utf-8', level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
    # # Initialize hydra with configs
    # GlobalHydra.instance().clear()
    
    cfg = init_cfg(cfg_name)
    logging.info(f"config : {cfg}")
    nbclasses = cfg.dir_dataset_n_classes
    model = LitUnsupervisedSegmenter(nbclasses, cfg)
    model = model.cpu()
    logging.info(f"Model Initialiazed")
    
    model_path = "biomap/checkpoint/model/model.pt"
    saved_state_dict = torch.load(model_path, map_location=torch.device("cpu"))
    logging.info(f"Model weights Loaded")
    model.load_state_dict(saved_state_dict)
    return model

def app(model):
    if "infered" not in st.session_state:
        st.session_state["infered"] = False
    if "submit" not in st.session_state:
        st.session_state["submit"] = False
    if "submit2" not in st.session_state:
        st.session_state["submit2"] = False

    st.markdown("<h1 style='text-align: center;'>🐢 Biomap by Ekimetrics 🐢</h1>", unsafe_allow_html=True)
    st.markdown("<h2 style='text-align: center;'>Estimate Biodiversity in the world with the help of land cover.</h2>", unsafe_allow_html=True)
    st.markdown("<p style='text-align: center;'>The segmentation model is an association of UNet and DinoV1 trained on the dataset CORINE. Land use is divided into 6 differents classes : Each class is assigned a GBS score from 0 to 1</p>", unsafe_allow_html=True)
    st.markdown("<p style='text-align: center;'>Buildings : 0.1 | Infrastructure : 0.1 | Cultivation : 0.4 | Wetland : 0.9 | Water : 0.9 | Natural green : 1 </p>", unsafe_allow_html=True)
    st.markdown("<p style='text-align: center;'>The score is then averaged on the full image.</p>", unsafe_allow_html=True)
    
    if st.session_state["submit"]:
            fig = inference_on_location(model, st.session_state["lat"], st.session_state["long"], st.session_state["start_date"], st.session_state["end_date"], st.session_state["segment_interval"])
            st.session_state["infered"] = True
            st.session_state["previous_fig"] = fig
    
    if st.session_state["submit2"]:
            fig = inference_on_location_and_month(model, st.session_state["lat_2"], st.session_state["long_2"], st.session_state["date_2"])
            st.session_state["infered"] = True
            st.session_state["previous_fig"] = fig

    if st.session_state["infered"]:
            st.plotly_chart(st.session_state["previous_fig"], use_container_width=True)
        
    col_1, col_2 = st.columns([0.5, 0.5])
    with col_1:
        m = folium.Map(location=[DEFAULT_LATITUDE, DEFAULT_LONGITUDE], zoom_start=DEFAULT_ZOOM)
        m.add_child(folium.LatLngPopup())
        f_map = st_folium(m, width=FOLIUM_WIDTH, height=FOLIUM_HEIGHT)
    
        selected_latitude = DEFAULT_LATITUDE
        selected_longitude = DEFAULT_LONGITUDE

        if f_map.get("last_clicked"):
            selected_latitude = f_map["last_clicked"]["lat"]
            selected_longitude = f_map["last_clicked"]["lng"]

    with col_2:
        tabs1, tabs2 = st.tabs(["TimeLapse", "Single Image"])
        with tabs1:
            submit = st.button("Predict TimeLapse", use_container_width=True, type="primary")
            st.session_state["submit"] = submit

            col_tab1_1, col_tab1_2 = st.columns(2)
            with col_tab1_1:
                lat = st.text_input("latitude", value=selected_latitude)
                st.session_state["lat"] = lat
            with col_tab1_2:
                long = st.text_input("longitude", value=selected_longitude)
                st.session_state["long"] = long

            col_tab1_11, col_tab1_22 = st.columns(2)
            years = list(range(MIN_YEAR, MAX_YEAR, 1))
            with col_tab1_11:
                start_date = st.selectbox("Start date", years)
                st.session_state["start_date"] = start_date

            end_years = [year for year in years if year > start_date]
            with col_tab1_22:
                end_date = st.selectbox("End date", end_years) 
                st.session_state["end_date"] = end_date
        
            segment_interval = st.radio("Interval of time between two segmentation", options=['month','2months', 'year'],horizontal=True)
            st.session_state["segment_interval"] = segment_interval

        with tabs2:
            submit2 = st.button("Predict Single Image", use_container_width=True, type="primary")
            st.session_state["submit2"] = submit2
    
            col_tab2_1, col_tab2_2 = st.columns(2)
            with col_tab2_1:
                lat_2 = st.text_input("lat.", value=selected_latitude)
                st.session_state["lat_2"] = lat_2
            with col_tab2_2:
                long_2 = st.text_input("long.", value=selected_longitude)
                st.session_state["long_2"] = long_2

            date_2 = st.text_input("date", "2021-01-01", placeholder="2021-01-01")
            st.session_state["date_2"] = date_2


if __name__ == "__main__":
    model = init_app("my_train_config.yml")
    app(model)