Create app.py

app.py

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import importlib
+
+
+from st_on_hover_tabs import on_hover_tabs
+
+import streamlit as st
+
+import streamlit_presentation
+import streamlit_presentation.analyse
+importlib.reload(streamlit_presentation.analyse)
+from streamlit_presentation.analyse import repartition_par_categorie
+from streamlit_presentation.analyse import repartition_longueur_categorie
+
+
+import streamlit_presentation.preprocessing
+importlib.reload(streamlit_presentation.preprocessing)
+from streamlit_presentation.preprocessing import detection_langage_et_traduction
+
+import streamlit_presentation.modele
+importlib.reload(streamlit_presentation.modele)
+from streamlit_presentation.modele import presentation_modele
+from sklearn.metrics import f1_score
+
+plt.rcParams['font.size'] = 12
+plt.rcParams['axes.labelsize'] = 10
+plt.rcParams['axes.titlesize'] = 12
+plt.rcParams['xtick.labelsize'] = 8
+plt.rcParams['ytick.labelsize'] = 8
+plt.rcParams['legend.fontsize'] = 8
+plt.rcParams['lines.linewidth'] = 1
+
+#on charge les donnees utilisees
+data = pd.read_csv( 'data.csv')
+extract_data = pd.read_csv( 'data_tr_extract.csv')
+sum_data = pd.read_csv( 'data_sum_extract.csv')
+test_data = pd.read_pickle( 'data_test.pkl')
+
+from keras.models import load_model
+import tensorflow as tf
+from tensorflow.keras import backend as K
+import ast
+
+
+def f1_weighted(true, pred):  
+
+    # Classes
+    classes = K.arange(0, 27) 
+    true = K.one_hot(K.cast(true, 'int32'), 27)
+    
+    # Calcule les TP, FP, FN pour chaque classe
+    tp = K.dot(K.transpose(true), K.round(pred))
+    fp = K.dot(K.transpose(1-true), K.round(pred))
+    fn = K.dot(K.transpose(true), 1-K.round(pred))
+
+    # Calcule le score F1 pour chaque classe
+    p = tp / (tp + fp + K.epsilon())
+    r = tp / (tp + fn + K.epsilon())
+    f1 = 2*p*r / (p+r+K.epsilon())
+
+    
+    weighted_f1 = K.sum(f1 * K.sum(true, axis=0) / K.sum(true))
+    return weighted_f1
+
+model = load_model("final_model_kfold.h5", custom_objects={'f1_weighted': f1_weighted})
+
+
+
+
+
+from sklearn.preprocessing import LabelEncoder
+encoder =  LabelEncoder()
+print(test_data.columns)
+y_test = encoder.fit_transform(test_data["prdtypecode"])
+class_labels = encoder.classes_
+label_size = 27
+
+
+
+####### Page principale
+st.set_page_config(layout="wide")
+st.markdown('<style>' + open('./style.css').read() + '</style>', unsafe_allow_html=True)
+
+st.title("Mon Application")
+
+with st.sidebar:
+    tabs = on_hover_tabs(tabName=['Introduction', "Analyse", "Preprocessing", "Modèle", "Pistes exploratoires"], 
+                         iconName=['apps', 'bar_chart', "sync", "memory", "topic"], default_choice=0)
+
+st.markdown("""
+<style>
+    .rounded-border-parent {
+        border-radius: 15px !important;
+        border: 1px solid blue !important;
+        background-color: lightgray !important;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+
+if tabs == "Introduction":
+    st.write("# Introduction")
+    st.write("Ici")
+
+elif tabs == "Analyse":
+    st.write("# Analyse")
+    
+    st.dataframe(data.head(30))
+    st.write("")
+    
+    repartition_par_categorie(st, data)
+    repartition_longueur_categorie(st, data)
+
+elif tabs == "Preprocessing":
+    detection_langage_et_traduction(st, extract_data, sum_data)
+
+elif tabs == "Modèle":
+    presentation_modele(st, test_data, model,class_labels,y_test)
+
+elif tabs == "Pistes exploratoires":
+    st.write("# Pistes exploratoires")
+    st.write("Ici")