Primer commit

appMamamIA.py

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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 células 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 predicción diagnóstica del Cáncer 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="Perímertro Promedio", min_value=0.00000, max_value=150.0,value=87.46, step=0.0001,format="%4f")
+    area_mean = form.number_input(label="Área 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 Cóncavos 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 cóncavos worst", min_value=0.00000, max_value=2.0,value=0.1288, step=0.0001,format="%4f")
+
+    submit = form.form_submit_button(label="Predicción")
+
+    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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