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import streamlit as st
import pandas as pd
import pickle
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.pipeline import Pipeline
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
from sklearn.metrics import classification_report
from sklearn.metrics import accuracy_score
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import IsolationForest
from sklearn.tree import DecisionTreeClassifier
from fs import FeatureSelection
from xgboost import XGBClassifier
import warnings
warnings.filterwarnings('ignore')
from sklearn import set_config
set_config(display="diagram")
with open('mejor_modelo_tp4.pkl', 'rb') as f:
modelo = pickle.load(f)
# Define una funci贸n para hacer predicciones con el modelo
def predecir(features):
# Procesa los valores de features y hace predicciones con el modelo
predicciones = modelo.predict(features)
return predicciones
# 'Unnamed: 0', 'acc_max', 'gyro_max', 'acc_kurtosis', 'gyro_kurtosis',
# 'label', 'lin_max', 'acc_skewness', 'gyro_skewness', 'post_gyro_max',
# 'post_lin_max', 'fall']
# Crea la aplicaci贸n Streamlit
def app():
st.title('Ingrese los valores de las features')
col1, col2, col3 = st.columns(3)
with col1:
feature1 = st.text_input('Unnamed: 0', value = 61)
with col2:
feature2 = st.text_input('acc_max', value = 26.310655)
with col3:
feature3 = st.text_input('gyro_max', value = 5.192876)
col4, col5, col6 = st.columns(3)
with col4:
feature4 = st.text_input('acc_kurtosis', value = 17.569042)
with col5:
feature5 = st.text_input('gyro_kurtosis', value = 9.776727)
with col6:
feature6 = st.text_input('label', value = 'FOL')
col7, col8, col9 = st.columns(3)
with col7:
feature7 = st.text_input('lin_max', value = 11.584056)
with col8:
feature8 = st.text_input('acc_skewness', value = 3.587634)
with col9:
feature9 = st.text_input('gyro_skewness', value = 2.848477)
col10, col11, col12 = st.columns(3)
with col10:
feature10 = st.text_input('post_gyro_max', value = 4.691588)
with col11:
feature11 = st.text_input('post_lin_max', value = 10.684285)
# with col12:
# feature12 = st.text_input('3')
# # # Crea los campos de entrada de texto para las features
# feature1 = st.text_input('Unnamed: 0')
# feature2 = st.text_input('acc_max')
# feature3 = st.text_input('gyro_max')
# feature4 = st.text_input('acc_kurtosis')
# feature5 = st.text_input('gyro_kurtosis')
# feature6 = st.text_input('label')
# feature7 = st.text_input('lin_max')
# feature8 = st.text_input('acc_skewness')
# feature9 = st.text_input('gyro_skewness')
# feature10 = st.text_input('post_gyro_max')
# feature11 = st.text_input('post_lin_max')
# Crea un bot贸n para hacer predicciones con el modelo
if st.button('Predecir'):
# Convierte los valores de features en un DataFrame de Pandas
features = pd.DataFrame({
'Unnamed: 0': [feature1],
'acc_max': [feature2],
'gyro_max': [feature3],
'acc_kurtosis': [feature4],
'gyro_kurtosis': [feature5],
'label': [feature6],
'lin_max': [feature7],
'acc_skewness': [feature8],
'gyro_skewness': [feature9],
'post_gyro_max': [feature10],
'post_lin_max': [feature11],
})
# if predecir(features) == 0:
# predicciones = 'NO '
# else:
# predicciones = ''
if predecir(features) == [0]:
respuesta = ' NO '
else:
respuesta = ' SI '
st.write(f'Estos datos {respuesta}son compatibles con una ca铆da')
if st.button('Modelo'):
st.write(modelo.named_steps)
# Ejecuta la aplicaci贸n Streamlit
if __name__ == '__main__':
app()
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