| 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") |
|
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|
|
| 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) |
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| |
