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import streamlit as st
import pandas as pd
import models.model as mod
from menu import get_menu
st.set_page_config(
layout="centered"
)
get_menu()
st.title("Formulaire")
with st.form("prediction_form", border=False):
# info bancaire
name = st.text_input('Votre nom')
age = st.number_input('Votre age', min_value=16)
job,education = st.columns(2)
job_val = job.selectbox('Votre metier', ["admin.","unknown","unemployed","management","housemaid","entrepreneur","student","blue-collar","self-employed","retired","technician","services"])
education_val = education.selectbox('Votre niveau d\'etude', ["unknown","secondary","primary","tertiary"])
marital = st.selectbox('Votre status marital', ["married","divorced","single"])
balance = st.number_input('Votre solde annuel moyen (en euro)', min_value=1)
default = st.checkbox('Avez vous un crédit en déficite ?')
housing = st.checkbox('Avez vous un pret logement ?')
loan = st.checkbox('Avez vous un pret ?')
#informations contact
contact = st.selectbox('Moyen de contact', ["unknown","telephone","cellular"])
day,month = st.columns([2,2])
day_val = day.number_input('Dernier jours de contact du mois', min_value=0)
month_val = month.selectbox('Dernier mois de contact de l\'année', ['jan','fed','mar','apr','may','jun','jul','aug','sep','oct','nov','dec'])
duration = st.number_input('Durée de la derniere conversation', min_value=0)
#autres infos
campaign = st.number_input('Nombre de contact effectuer pour cette campagne', min_value=0)
pdays = st.number_input('Nombre de jours ecoulé depuis le dernier contact', min_value=0)
previous = st.number_input('Nombre de contact effectuer avant cette campagne', min_value=0)
poutcome = st.selectbox('Resultat de la derniere campagne', ["unknown","other","failure","success"])
selected_model = st.selectbox('Choisir le model', ["XGBOOST", "KNN", "SVC LINEAIRE","SVC", "RAMDOM FOREST"])
submitted = st.form_submit_button('Predire le choix', use_container_width=True)
if submitted:
if name == "":
st.error('Le nom est obligatoire !')
else:
data_vals = [
age,
job_val,
marital,
education_val,
'yes' if default else 'no',
balance,
'yes' if housing else 'no',
'yes' if loan else 'no',
contact,
day_val,
month_val,
duration,
campaign,
pdays,
previous,
poutcome
]
index = [
"age",
"job",
"marital",
"education",
"default",
"balance",
"housing",
"loan",
"contact",
"day",
"month",
"duration",
"campaign",
"pdays",
"previous",
"poutcome"
]
data = pd.DataFrame([data_vals], columns=index)
data_transform = mod.transform_data(data)
if selected_model == "XGBOOST":
res = mod.xg_boost_model(data_transform)
elif selected_model == "KNN":
res = mod.knn_model(data_transform)
elif selected_model == "SVC LINEAIRE":
res = mod.svc_linear_model(data_transform)
elif selected_model == "SVC":
res = mod.svc_model(data_transform)
else:
res = mod.ramdom_forest_model(data_transform)
msg = f"D'apres notre model {selected_model} le client {name} "
if res == 0:
st.error(f"{msg} ne va pas souscrire à l'offre.")
else:
st.success(f"{msg} va pas souscrire à l'offre.")
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