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BEEPASv3 / app.py

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	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_model4.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)