import gradio as gr
from api import *
from processing import *
import pandas as pd
from indices import indices
import xgboost as xgb
from lightgbm import LGBMRegressor
import pickle
import json
import boto3
from shapely.geometry import MultiPolygon,shape
from shapely.geometry import Point
from shapely.geometry.polygon import Polygon
from glob import glob
import wget


def predict(lat, lon):
    cord = [lon,lat]
    lon = round(lon,4)
    lat = round(lat,4)
    x1 = [lon,lat]
    x2 = [lat,lon]
    with open("data/CIV_0.json","r") as file:
        data = json.load(file)
    # extract ivory coast polygone
    features = [data['features'][0]['geometry']['coordinates'][0]+data['features'][0]['geometry']['coordinates'][1]+data['features'][0]['geometry']['coordinates'][2]]
    data['features'][0]['geometry']['coordinates'] = features
    ci_polygone = data['features'][0]['geometry']['coordinates'][0][0]  
    point1 = Point(x1)
    point2 = Point(x2)
    polygon = Polygon(ci_polygone)
    result = polygon.contains(point1)

    if not result:
        return "Choisissez une zone de la CI","","","",""
    
    else:
        df = pd.read_csv("data/frame.csv")
        name = find_good_tile(df,point2)
        if name ==404:
            reponse = "Sentinel-2 ne dispose pas de données ce sur ce lieu à ce jour"
            return reponse,"","","",""
        else:
            path = "https://data354-public-assets.s3.eu-west-3.amazonaws.com/cisentineldata/"
            url = path+name
            wget.download(url)
            unzip()
            name,cld_prob,days_ago = select_best_cloud_coverage_tile()
            bandes_path_10,bandes_path_20,bandes_path_60,tile_path,path_cld_20,path_cld_60 =paths(name)
            # create image dataset
            images_10 = extract_sub_image(bandes_path_10,tile_path,cord)

            # bandes with 20m resolution
            #path_cld_20
            images_20 = extract_sub_image(bandes_path_20,tile_path,cord,20,1)

            # bandes with 60m resolution
            #path_cld_60
            images_60 = extract_sub_image(bandes_path_60,tile_path,cord,60)
            #
            feature = images_10.tolist()+images_20.tolist()+images_60.tolist()
            bands = ['B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B11', 'B12','B01','B09']
            X = pd.DataFrame([feature],columns = bands)
            # vegetation index calculation
            X = indices(X)
            # load the model from disk
            filename = "data/new_version_model.sav"
            loaded_model = pickle.load(open(filename, 'rb'))
            # make prediction
            biomass = loaded_model.predict(X)[0]
            carbon = 0.55*biomass

            # NDVI
            ndvi_index = ndvi(cord,name)

            # deleted download files
            delete_tiles()

            return str(cld_prob)+ " % cloud coverage", str(days_ago)+" days ago",str(biomass)+" Kg/ha", str(carbon)+" KgC/ha","NDVI: "+ str(ndvi_index)

# Create title, description and article strings
title = "🌴BEEPAS : Biomass estimation to Evaluate the Environmental Performance of Agroforestry Systems🌴"
description = "This application estimates the biomass of certain areas using AI and satellite images (S2)."
article = "Created by data354."

# Create examples list from "examples/" directory
#example_list = [["examples/" + example] for example in os.listdir("examples")]
example_list = [[5.379913, -4.050445],[5.363292, -3.9481601],[5.316458, -4.017172],[5.346938, -4.027849]]

outputs = [
    gr.Textbox(label="Cloud coverage"),
    gr.Textbox(label="Number of days since sensing"),
    gr.Textbox(label="Above ground biomass density(AGBD) Kg/ha"),
    gr.Textbox(label="Carbon stock density KgC/ha "),
    gr.Textbox(label="Mean NDVI"),]


demo = gr.Interface(
    fn=predict,
    inputs=["number", "number"],
    outputs=outputs, #[ "text", "text","text","text","text"],
    examples=example_list,
    title=title,
    description=description,
    article=article,
    )

demo.launch(share=True)