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# Actualizado por: José Carlos Machicao, Fecha de actualización: 2024_07_08, Lima
# Esta vinculado a los PKL de https://sites.google.com/continental.edu.pe/edusights/inicio
# Importacion de librerias
import torch
import torch.nn as nn
import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader, TensorDataset
import plotly.express as px
import streamlit as st
from es_class_nn import SimplePlusNN2
version_name = 'CON_31'
c1, c2 = st.columns([6,6])
with c2:
st.image('logo_vidad.png', width=300, caption='https://www.continental.edu.pe/')
st.title("Predicción de Abandono o Permanencia")
st.write("Cargue el archivo PKL para visualizar el análisis de su contenido.")
uploaded_file = st.file_uploader("Cargar archivo: ", type='xlsx')
cat_sel = pd.read_excel('df_cat_prior.xlsx')
df_categ = pd.read_excel('lista_categorias.xlsx')
df_muestra = pd.read_excel('df_muestra_carga.xlsx')
if uploaded_file is not None:
# Lectura del archivo de predicción
df_test = pd.read_excel(uploaded_file)
verif = df_test.columns == df_muestra.columns
st.write(verif.sum())
df_scaled = pd.concat([df_muestra, df_test], axis=0, ignore_index=True)
df_scaled = df_scaled.fillna(0)
df = df_scaled.tail(len(df_test)).reset_index(drop=True)
st.write(df)
X = df.values
X_test_tensor = torch.tensor(X.astype(np.float32), dtype=torch.float32)
# Carga de Modelo Entrenado
input_size = X_test_tensor.shape[1]
num_classes = 2
model = SimplePlusNN2(input_size, num_classes)
data_path = ''
dict_name = f'edusights_20240702_state_dict_{version_name}.pth'
model.load_state_dict(torch.load(data_path+dict_name))
model.eval()
# Predicciones
inputs = X_test_tensor
outputs = model(inputs)
outputs_show = outputs.detach().numpy().flatten()
outputs_show[outputs_show > 0.60] = 1.0
outputs_show[outputs_show < 0.40] = 0.0
filtered_arr = outputs_show[(outputs_show == 0.0) | (outputs_show == 1.0)]
df['Pred'] = filtered_arr
st.write(df['Pred'])
csv_out = df.to_csv(encoding='iso-8859-1')
st.download_button(
label="Descargar CSV",
data=csv_out,
file_name='predicciones_carga.csv',
mime='text/csv'
)
c3, c4 = st.columns([6,6])
with c3:
st.image('gdmklogo.png', width=100, caption='Powered by GestioDinámica 2024')
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