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from numpy import dtype
import streamlit as st
import pandas as pd
from sklearn.preprocessing import StandardScaler
import numpy as np
import joblib as jl
# VALORES POR DEFECTO QUE INDICAN CELULAS NO CANCEROSAS
# radius_mean 14.12
# texture_mean 19.28
# perimeter_mean 91.96
# area_mean 551,17
# compactness_mean 0.0092
# concavity_mean 0.061
# concave_points_mean 0.033
# area_se 24.5
# radius_worst 14.97
# texture_worst 25.41
# perimeter_worst 97.6
# area_worst 686.5
# smoothness_worst 0.1313
# compactness_worst 0.20
# concavity_worst 0.22
# concave points_worst 0.09
col=['radius_mean', 'texture_mean', 'perimeter_mean',
'area_mean', 'compactness_mean', 'concavity_mean',
'concave points_mean', 'area_se', 'radius_worst', 'texture_worst',
'perimeter_worst', 'area_worst', 'smoothness_worst',
'compactness_worst', 'concavity_worst', 'concave points_worst']
modnames=['mlp_final.pkl','svm_final.pkl','lr_final.pkl']
#@st.cache
def getScaler():
# Cargo el dataset para poder normalizar los valores recogidos en el formulario
print ("cargando dataset")
data=pd.read_csv('https://raw.githubusercontent.com/gitmecalvon/mamamIA/main/resources/data/cleaned/train_web.csv',sep=';')
print("dataset cargado")
scaler = StandardScaler()
scaler.fit(data)
return scaler
# cargandolos para poder usarlos desde un sidebar si da tiempo
def cargaModelos (indice):
print('Preparando el guardado de Modelos ' )
modelo=jl.load(modnames[indice])
return modelo
def interpreta (prediccion):
respuesta ="Los datos introducidos pronostican que son c茅lulas de tipo "
if prediccion ==1:
respuesta= respuesta + "Maligno"
else:
respuesta= respuesta + "BENIGNO"
return respuesta
def contruyeFormulario():
# st.set_page_config(layout="wide")
st.title("Mama mIA")
st.markdown('<style>body{background-color: Black;}</style>',unsafe_allow_html=True)
html_temp = """ <div style ="background-color:Pink;padding:13px">
<h1 style ="color:black;text-align:center;">Algoritmo de ayuda a la predicci贸n diagn贸stica del C谩ncer de mama</h1>
</div>"""
st.markdown(html_temp, unsafe_allow_html = True)
st.subheader("Por favor introduzca las medidas de la muestra")
form = st.form(key="formulario")
# col1, col2 = form.columns(2) # intento de dos columnas sin recurrir a html
# with col1:
radius_mean = form.number_input( label="Radio Promedio", min_value=0.00000, max_value=20.0,value=13.54, step=0.0001,format="%4f")
texture_mean = form.number_input(label="Textura Promedio", min_value=0.00000, max_value=36.0,value=14.36, step=0.0001,format="%4f")
perimeter_mean = form.number_input(label="Per铆mertro Promedio", min_value=0.00000, max_value=150.0,value=87.46, step=0.0001,format="%4f")
area_mean = form.number_input(label="脕rea Promedio", min_value=0.00000, max_value=1600.0,value=566.3, step=0.0001,format="%4f")
compactness_mean = form.number_input(label="Promedio de Compactabilidad", min_value=0.00000, max_value=1.0,value=0.08129, step=0.0001,format="%5f")
concavity_mean = form.number_input(label="Promedio de Concavidad", min_value=0.00000, max_value=1.0,value=0.06664, step=0.0001,format="%5f")
concave_points_mean = form.number_input(label="Puntos C贸ncavos promedio", min_value=0.00000, max_value=1.0,value=0.04781, step=0.0001,format="%4f")
area_se = form.number_input(label="Area Error Estandar", min_value=0.00000, max_value=150.0,value=23.56, step=0.0001,format="%4f")
# with col2:
radius_worst = form.number_input(label="Radio worst ", min_value=0.00000, max_value=30.0,value=15.11, step=0.0001,format="%4f")
texture_worst= form.number_input(label="Textura worsk", min_value=0.00000, max_value=70.0,value=19.26, step=0.0001,format="%4f")
perimeter_worst = form.number_input(label="Perimetro worst", min_value=0.00000, max_value=99.70,value=0.0092, step=0.0001,format="%4f")
area_worst = form.number_input(label="Area ", min_value=0.00000, max_value=800.0,value=711.2, step=0.0001,format="%4f")
smoothness_worst = form.number_input(label="Suavidad worst", min_value=0.00000, max_value=1.0,value=0.144, step=0.0001,format="%4f")
compactness_worst = form.number_input(label="Compactabilidad worst", min_value=0.00000, max_value=2.0,value=0.1773, step=0.0001,format="%4f")
concavity_worst = form.number_input(label="Concavidad worst", min_value=0.00000, max_value=2.0,value=0.2390, step=0.0001,format="%4f")
concavepoints_worst = form.number_input(label="Puntos c贸ncavos worst", min_value=0.00000, max_value=2.0,value=0.1288, step=0.0001,format="%4f")
submit = form.form_submit_button(label="Predicci贸n")
if submit:
# Escalamos los datos del formulario
scaler=getScaler()
nbnormaliz=scaler.transform ([[radius_mean, texture_mean, perimeter_mean ,area_mean , compactness_mean , concavity_mean ,
concave_points_mean , area_se , radius_worst , texture_worst ,perimeter_worst , area_worst , smoothness_worst ,
compactness_worst , concavity_worst , concavepoints_worst ]])
# Recuperamos el modelo
print ("cargando modelo")
print (modnames[2])
algoritmo=cargaModelos(2)
# Realizamos la prediccion
print ("Preparando la prediccion...")
prediccion=algoritmo.predict (nbnormaliz)
print (prediccion)
st.write ("")
st.write (interpreta (prediccion))
def main():
contruyeFormulario()
if __name__ == '__main__':
main()
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