from traning_zone.classe_prediction.prediction_classe import *
import streamlit as st 
import plotly.express as px
import warnings
warnings.filterwarnings("ignore")
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
sns.set(rc={'figure.figsize':(14.7,10.27)})

st.title("Classification")


inputs = st.text_input("Input :" , value= "150ML SECHE VERNIS VITRY", key = "input")


if inputs != None :
    X = pd.DataFrame({"X" : [inputs]})
    X = X.X
    pred = PredictionV(X)
    data = pred.prediction("spacy_spacy") 
    "Input : ", data.item_desc[0]
    
    "Pred hyper class : "
    names = list(data["hyper classe"][0].keys())
    values = list(data["hyper classe"][0].values())

    df = pd.DataFrame({"class_desc_fr": names, "score": values})

    fig = px.histogram(df, y = "class_desc_fr", x = "score", orientation="h")

    st.plotly_chart(fig)

    "Pred classe : "

    names = list(data.classe[0].keys())
    values = list(data.classe[0].values())

    df = pd.DataFrame({"class_desc_fr": names, "score": values})

    fig = px.histogram(df, y = "class_desc_fr", x = "score", orientation="h")

    st.plotly_chart(fig)