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
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
import pickle as pk
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(location_name,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']
            print("feature : ",feature)
            print("BANDES : ",bands)
            
            X = pd.DataFrame([feature],columns = bands)
            print("==================== X SHAPE", X.shape)
            ## Coordinate
            cord_df = pd.DataFrame({"Latitude":[lat],
                                    "Longitude":[lon]})
            print("==================== cord_df SHAPE", cord_df.shape)
            ## PCA dimension reduction
            # later reload the pickle file
            sdc_reload = pk.load(open("data/sdc.pkl",'rb'))
            pca_reload = pk.load(open("data/pca.pkl",'rb'))
            # standardization
            X_pca = sdc_reload.transform(X)
            # make pca
            principalComponents = pca_reload .transform(X_pca)
            principalDf = pd.DataFrame(data =principalComponents[:,:4],
                           columns = ["PC1","PC2","PC3","PC4"])
            print("==================== principalDf SHAPE", principalDf.shape)

            # vegetation index calculation
            X = indices(X)
            # Drop all 12 bands of S2
            tab = list(range(12))
            X_index = X.drop(X.iloc[:,tab],axis=1)

            print("=============SHAPE1",X_index.shape)

            # Create predictive features
            X_final =pd.concat([cord_df,principalDf,X_index],axis=1)
            print("=============SHAPE2",X_final.shape)

            # load the model from disk
            filename = "data/finalized_model3.sav"
            loaded_model = pk.load(open(filename, 'rb'))
            # make prediction
            biomass = loaded_model.predict(X_final)[0]
            if biomass<0:
                biomass =0.0
            
            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)+" t/ha", str(carbon)+" tC/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 = [["Foret du banco :",5.379913, -4.050445],["Pharmacie Y4 :",5.363292, -3.9481601],["Treichville Bernabé :",5.293168, -3.999796],["Adjamé :",5.346938, -4.027849],["ile boulay :",5.280498,-4.089883]]


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


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

demo.launch(share=True)