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omit-radiotherapy

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santarabantoosoo

modified app.py

6198d18 almost 2 years ago

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	# import gradio as gr
	# import pickle
	# import pandas as pd
	# import numpy as np
	# import lightgbm as lgb
	# from autogluon.tabular import TabularPredictor

	# loaded_model = pickle.load(open('Autogluon/models/XGBoost_BAG_L1/model.pkl', 'rb'))
	# # pred_proba_radio = loaded_model.predict_proba(X_test_radio)
	# # pred_proba_radio


	# def relapse(age, pathology, B_symptoms):

	# X_test_radio = pd.DataFrame.from_dict(
	# {
	# "Age": [age],
	# "pathology_Nodular Sclerosis cHL": [1 if pathology == 'Nodular' else 0],
	# "pathology_others": [1 if pathology == 'Others' else 0],
	# "B_symptoms_Yes" : [1 if B_symptoms else 0],
	# 'radio_Yes': [1]
	# }
	# )

	# X_test_no_radio = pd.DataFrame.from_dict(
	# {
	# "Age": [age],
	# "pathology_Nodular Sclerosis cHL": [1 if pathology == 'Nodular' else 0],
	# "pathology_others": [1 if pathology == 'Others' else 0],
	# "B_symptoms_Yes" : [1 if B_symptoms else 0],
	# 'radio_Yes': [0]
	# }
	# )

	# pred_proba_radio = loaded_model.predict_proba(X_test_radio)

	# pred_proba_radio = round(np.ndarray.item(pred_proba_radio),2)

	# pred_radio = loaded_model.predict(X_test_radio)

	# pred_proba_no_radio = loaded_model.predict_proba(X_test_no_radio)

	# pred_proba_no_radio = round(np.ndarray.item(pred_proba_no_radio),2)

	# pred_no_radio = loaded_model.predict(X_test_no_radio)

	# return {"Radio": pred_proba_radio, "No Radio": pred_proba_no_radio}

	# iface = gr.Interface(

	# title = 'Should we omit radiotherapy?',
	# description = 'This model predicts relapse according to risk factors.',
	# fn=relapse,
	# inputs= [
	# gr.Number(label = 'Age', show_label = True),
	# gr.Radio( choices = ['Mixed cellularity', 'Nodular', 'Others'], label = 'Pathology', show_label = True),
	# gr.Checkbox(label = 'B_symptoms', show_label = True)],
	# outputs = gr.Label(label = 'Has a higher probability of relapse', show_label = True)
	# ,live = True,
	# interpretation="default",
	# )

	# iface.launch()






	import gradio as gr
	import pandas as pd
	import numpy as np
	from autogluon.tabular import TabularPredictor

	predictor_1 = TabularPredictor.load("no_path_PET_1/")
	predictor_2 = TabularPredictor.load("no_path_PET_2/")
	predictor_3 = TabularPredictor.load("no_path_PET_3/")
	predictor_4 = TabularPredictor.load("no_path_PET_4/")

	## Import mapping dataset

	stage_mapping = pd.read_csv('stage_mapping.csv', index_col = 0)

	# stage_mapping

	def get_encoding(stage):
	return stage_mapping.loc[stage, 'stage_encoded'] # this is the dataframe for mappings that will be created from the training set

	def relapse(age_group, stage):

	X_test_radio = pd.DataFrame.from_dict(
	{
	"age_group": ["<=15" if age_group else ">15"],
	'radio': ["Yes"],
	'stage_encoded' : get_encoding(stage)

	}
	)

	X_test_no_radio = pd.DataFrame.from_dict(
	{
	"age_group": ["<=15" if age_group else ">15"],
	'radio': ["No"],
	'stage_encoded' : get_encoding(stage)

	}
	)

	rad_1 = float(round(predictor_1.predict_proba(X_test_radio).iloc[0,1],3))
	rad_2 = float(round(predictor_2.predict_proba(X_test_radio).iloc[0,1],3))
	rad_3 = float(round(predictor_3.predict_proba(X_test_radio).iloc[0,1],3))
	rad_4 = float(round(predictor_4.predict_proba(X_test_radio).iloc[0,1],3))

	pred_proba_radio = float(round(np.mean([rad_1, rad_2, rad_3, rad_4]),3))

	no_rad1 = float(round(predictor_1.predict_proba(X_test_no_radio).iloc[0,1],3))
	no_rad2 = float(round(predictor_2.predict_proba(X_test_no_radio).iloc[0,1],3))
	no_rad3 = float(round(predictor_3.predict_proba(X_test_no_radio).iloc[0,1],3))
	no_rad4 = float(round(predictor_4.predict_proba(X_test_no_radio).iloc[0,1],3))


	pred_proba_no_radio = float(round(np.mean([no_rad1, no_rad2, no_rad3, no_rad4]),3))


	return {"Radio": pred_proba_radio, "No Radio": pred_proba_no_radio}


	iface = gr.Interface(

	title = 'Should we omit radiotherapy?',
	description = 'This model predicts relapse according to risk factors.',
	fn=relapse,
	inputs= [
	gr.Checkbox(label = 'Age <= 15', show_label = True),
	gr.Dropdown(choices = ['1A', '1B', '2A', '2B', '3A', '3B', '4A', '4B'],
	label = 'stage', show_label = True) ]
	,outputs = gr.Label(label = 'Has a higher probability of relapse', show_label = True)
	,live = True,
	interpretation="default",
	)

	iface.launch()