Upload app_streamlit.py

app_streamlit.py

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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')