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BEEPAS / app.py

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Update app.py

addd16f 8 months ago

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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
	import pickle

	def predict(lat, lon):
	cord = [lon,lat]
	download(cord)
	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/finalized_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)+" Mg/ha", str(carbon)+" MgC/ha","NDVI: "+ str(ndvi_index)

	# Create title, description and article strings
	title = "🌴BEEPAS : Biomass estimation to Evaluate the Environmental Performance of Agroforestry System🌴"
	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],[6.54644,-7.86156],[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) Mg/ha"),
	gr.Textbox(label="Carbon stock density MgC/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=False)