diff --git a/src/Class_Mod/Regression_metrics.py b/src/Class_Mod/Regression_metrics.py
index 785cd6f0c9180ef10b5170af770983e5804ef95f..5ec8cacb36eb7eecd515311aa3ec0f2f4120be24 100644
--- a/src/Class_Mod/Regression_metrics.py
+++ b/src/Class_Mod/Regression_metrics.py
@@ -2,19 +2,13 @@ from Packages import *
class metrics:
def __init__(self, meas, pred):
if isinstance(meas, pd.DataFrame) or isinstance(meas, pd.Series):
- self.meas = meas.to_numpy()
- else :
- self.meas = meas.ravel()
-
- if isinstance(pred, pd.DataFrame):
- self.pred = pred.to_numpy().ravel()
- else :
- self.pred = pred.ravel()
+ self.meas = np.array(meas).reshape(-1)
+ self.pred = np.array(pred).reshape(-1)
@property
def evaluate_(self):
xbar = np.mean(self.meas) # the average of measured values
- e = np.subtract(self.meas.ravel(), self.pred.ravel())
+ e = np.subtract(self.meas, self.pred)
e2 = e**2# the squared error
# Sum of squared:
@@ -28,7 +22,7 @@ class metrics:
# Compute statistical metrics
metr = pd.DataFrame()
- metr['r'] = [np.corrcoef(self.meas.ravel(), self.pred)[0,1]]
+ metr['r'] = [np.corrcoef(self.meas, self.pred)[0,1]]
metr['r2'] = [1-ssr/sst]
metr['rmse'] = [np.sqrt(np.mean(e2))]
metr['mae'] = [np.mean(np.abs(e2))]
diff --git a/src/Class_Mod/SK_PLSR_.py b/src/Class_Mod/SK_PLSR_.py
index 5442971490ac476a2b1665ee112d1830e7243be2..7b9de1cbf2f1e776d91b97d700ab58b2893ec079 100644
--- a/src/Class_Mod/SK_PLSR_.py
+++ b/src/Class_Mod/SK_PLSR_.py
@@ -1,4 +1,10 @@
-from Packages import *
+from Packages import *
+from Class_Mod.Miscellaneous import *
+from Class_Mod.Regression_metrics import metrics
+
+
+
+
class PlsR:
def __init__(self, x_train, y_train, x_test, y_test):
self.x_train = x_train
@@ -6,12 +12,53 @@ class PlsR:
self.y_train = y_train
self.y_test = y_test
- def fit_(self):
- nlv = 20
- rmse = []
- for i in range(nlv):
- m = PLSRegression(n_components= 20)
+ self.trained = PLSRegression(n_components= self._optimize(), scale = False)
+ self.trained.fit(self.x_train, self.y_train)
+
+ self.yc = pd.DataFrame(self.trained.predict(self.x_train)) # make predictions on test data and assign to Y_preds variable
+ self.ycv = pd.DataFrame(cross_val_predict(self.trained, self.x_train, self.y_train, cv = 3)) # make predictions on test data and assign to Y_preds variable
+ self.yt = pd.DataFrame(self.trained.predict(self.x_test)) # make predictions on test data and assign to Y_preds variable
+
+
+ def _optimize(self):
+ nlv = 21
+ rmse = np.ones(21)
+ rmse[0] = 0.002
+ lv = {}
+ ratio = []
+ for i in range(1,nlv):
+ m = PLSRegression(n_components= i, scale = False)
ycv = cross_val_predict(m, self.x_train, self.y_train, cv = 5)
- rmse.append(mean_squared_error(self.y_train, ycv))
- print(rmse)
+ rmse[i] = mean_squared_error(self.y_train, ycv)
+
+ ratio.append(((rmse[i-1]-rmse[i])/rmse[i-1])*100)
+ return np.argmax(ratio)+1
+
+ ################################################################################################################
+
+
+ ################################################################################################################
+
+ @property
+ def model_(self):
+ return self.trained
+
+ @property
+ def metrics_(self):
+ metc = metrics(self.y_train, self.yc)
+ metc = metc.evaluate_
+
+ metcv = metrics(self.y_train, self.ycv)
+ metcv = metcv.evaluate_
+
+ mett = metrics( self.y_test, self.yt)
+ mett = mett.evaluate_
+
+ met = pd.concat([metc, metcv, mett], axis = 0)
+ met.index = ['calib','cv','test']
+ return met
+ @property
+ def pred_data_(self):
+
+ return self.yc, self.ycv, self.yt
\ No newline at end of file
diff --git a/src/Class_Mod/__init__.py b/src/Class_Mod/__init__.py
index a3a0e22f34ebf253c4ffda9782a091f818f2cbd6..a2182b4dc2a93586c062c3c7049927103f2def42 100644
--- a/src/Class_Mod/__init__.py
+++ b/src/Class_Mod/__init__.py
@@ -11,4 +11,4 @@ from .VarSel import TpeIpls
from .Miscellaneous import resid_plot, reg_plot
from .DxReader import DxRead, read_dx
from .HDBSCAN_Clustering import Hdbscan
-
+from .SK_PLSR_ import PlsR
\ No newline at end of file
diff --git a/src/Modules.py b/src/Modules.py
index 067b954347564d671ab959111b79457eecd4e007..5de6b6f793e062505b1ac79483954c92a487bda5 100644
--- a/src/Modules.py
+++ b/src/Modules.py
@@ -1,4 +1,5 @@
-from Class_Mod import LinearPCA, Umap, find_col_index, PinardPlsr, LWPLSR, list_files, metrics, TpeIpls, reg_plot, resid_plot, Sk_Kmeans, DxRead, Hdbscan, read_dx
+from Class_Mod import PlsR, LinearPCA, Umap, find_col_index, PinardPlsr
+from Class_Mod import LWPLSR, list_files, metrics, TpeIpls, reg_plot, resid_plot, Sk_Kmeans, DxRead, Hdbscan, read_dx
# find_col_index
from Class_Mod.Miscellaneous import prediction, download_results, plot_spectra
diff --git a/src/Packages.py b/src/Packages.py
index d2abd429cfe69cdcbd6a031290edc930c928337f..c7bf15dcdb6ef8ba2320d02ee4477c6c00f4ad5b 100644
--- a/src/Packages.py
+++ b/src/Packages.py
@@ -5,12 +5,14 @@ import csv
import re
import jcamp
import random
+import datetime
import numpy as np
import pandas as pd
from os import listdir
from os.path import isfile, join
from sklearn.preprocessing import StandardScaler, MinMaxScaler, LabelEncoder
import time
+from scipy.stats import skew, kurtosis
### Exploratory data analysis-Dimensionality reduction
from umap.umap_ import UMAP
diff --git a/src/app.py b/src/app.py
index 4f9e2b7ac1b0a888d0650e930e3c1ad5c2eb4b30..9b63fe162aa26ebce48e20c0d918e79b41b0c779 100644
--- a/src/app.py
+++ b/src/app.py
@@ -19,10 +19,13 @@ st.markdown(bandeau_html, unsafe_allow_html=True)
# # TOC menu on the left
show_pages(
[Page("app.py", "Home"),
+ Page("pages\\4-inputs.py", "Inputs"),
+
Page("pages\\1-samples_selection.py", "Samples Selection"),
Page("pages\\2-model_creation.py", "Models Creation & Predictions"),
+
]
-)
+)
# hide_pages("Samples Selection")
# hide_pages("Models Creation")
# hide_pages("Predictions")
@@ -37,13 +40,16 @@ with st.sidebar:
elif st.session_state['interface'] == 'advanced':
show_pages(
[Page("app.py", "Home"),
+ Page("pages\\4-inputs.py", "Inputs"),
Page("pages\\1-samples_selection.py", "Samples Selection"),
Page("pages\\2-model_creation.py", "Models Creation"),
Page("pages\\3-prediction.py", "Predictions"),
+
+
]
)
st.page_link('pages\\2-model_creation.py')
- st.page_link('pages\\3-prediction.py')
+ st.page_link('pages\\3-prediction.py')
# Page header
@@ -65,7 +71,9 @@ with st.container():
unsafe_allow_html=True
)
- header1, header2, header3 = st.columns(3)
+ header1, header2, header3,header4 = st.columns(4)
+ if header3.button("Inputs"):
+ st.switch_page('pages\\4-inputs.py')
if header1.button("Samples Selection"):
st.switch_page('pages\\1-samples_selection.py')
if header2.button("Model Creation"):
diff --git a/src/pages/1-samples_selection.py b/src/pages/1-samples_selection.py
index c9f3315d89c2a1059f688bb1ecea69e448b45d85..fd13d437fd63f4e729c7d0d58180aca9af6db37b 100644
--- a/src/pages/1-samples_selection.py
+++ b/src/pages/1-samples_selection.py
@@ -42,9 +42,9 @@ if data_file:
if test== '.csv':
with col1:
# Select list for CSV delimiter
- psep = st.selectbox("Select csv separator - _detected_: " + str(find_delimiter('data/'+data_file.name)), options=[";", ","], index=[";", ","].index(str(find_delimiter('data/'+data_file.name))), key=9)
+ psep = st.radio("Select csv separator - _detected_: " + str(find_delimiter('data/'+data_file.name)), options=[";", ","], index=[";", ","].index(str(find_delimiter('data/'+data_file.name))), key=9)
# Select list for CSV header True / False
- phdr = st.selectbox("indexes column in csv? - _detected_: " + str(find_col_index('data/'+data_file.name)), options=["no", "yes"], index=["no", "yes"].index(str(find_col_index('data/'+data_file.name))), key=31)
+ phdr = st.radio("indexes column in csv? - _detected_: " + str(find_col_index('data/'+data_file.name)), options=["no", "yes"], index=["no", "yes"].index(str(find_col_index('data/'+data_file.name))), key=31)
if phdr == 'yes':
col = 0
else:
@@ -226,30 +226,31 @@ if not t.empty:
import plotly.express as px
- num_clusters = len(np.unique(labels))
+ if labels:
+ num_clusters = len(np.unique(labels))
- custom_color_palette = px.colors.qualitative.Plotly[:num_clusters]
- color_discrete_sequence=custom_color_palette
+ custom_color_palette = px.colors.qualitative.Plotly[:num_clusters]
+ color_discrete_sequence=custom_color_palette
- # Créer et exporter le graphique Axe1-Axe2 en PNG
- fig_axe1_axe2 = px.scatter(tcr, x=axis1, y=axis2, color=labels if list(labels) else None, color_discrete_sequence=custom_color_palette)
- fig_axe1_axe2.update_layout(title='Axe1-Axe2')
- fig_axe1_axe2.update_traces(marker=dict(size=4))
- fig_axe1_axe2.write_image("plot_axe1_axe2.png")
+ # Créer et exporter le graphique Axe1-Axe2 en PNG
+ fig_axe1_axe2 = px.scatter(tcr, x=axis1, y=axis2, color=labels if list(labels) else None, color_discrete_sequence=custom_color_palette)
+ fig_axe1_axe2.update_layout(title='Axe1-Axe2')
+ fig_axe1_axe2.update_traces(marker=dict(size=4))
+ fig_axe1_axe2.write_image("./Report/Figures/plot_axe1_axe2.png")
- # Créer et exporter le graphique Axe1-Axe3 en PNG
- fig_axe1_axe3 = px.scatter(tcr, x=axis1, y=axis3, color=labels if list(labels) else None, color_discrete_sequence=custom_color_palette)
- fig_axe1_axe3.update_layout(title='Axe1-Axe3')
- fig_axe1_axe3.update_traces(marker=dict(size=4))
- fig_axe1_axe3.write_image("plot_axe1_axe3.png")
+ # Créer et exporter le graphique Axe1-Axe3 en PNG
+ fig_axe1_axe3 = px.scatter(tcr, x=axis1, y=axis3, color=labels if list(labels) else None, color_discrete_sequence=custom_color_palette)
+ fig_axe1_axe3.update_layout(title='Axe1-Axe3')
+ fig_axe1_axe3.update_traces(marker=dict(size=4))
+ fig_axe1_axe3.write_image("./Report/Figures/plot_axe1_axe3.png")
- # Créer et exporter le graphique Axe2-Axe3 en PNG
- fig_axe2_axe3 = px.scatter(tcr, x=axis2, y=axis3, color=labels if list(labels) else None, color_discrete_sequence=custom_color_palette)
- fig_axe2_axe3.update_layout(title='Axe2-Axe3')
- fig_axe2_axe3.update_traces(marker=dict(size=4))
- fig_axe2_axe3.write_image("plot_axe2_axe3.png")
+ # Créer et exporter le graphique Axe2-Axe3 en PNG
+ fig_axe2_axe3 = px.scatter(tcr, x=axis2, y=axis3, color=labels if list(labels) else None, color_discrete_sequence=custom_color_palette)
+ fig_axe2_axe3.update_layout(title='Axe2-Axe3')
+ fig_axe2_axe3.update_traces(marker=dict(size=4))
+ fig_axe2_axe3.write_image("./Report/Figures/plot_axe2_axe3.png")
if not spectra.empty:
diff --git a/src/pages/2-model_creation.py b/src/pages/2-model_creation.py
index 114161c9623660d1cd961d91c57b59787743b573..2b08fda6f3b1851fe04172044adc76e300c6f3a6 100644
--- a/src/pages/2-model_creation.py
+++ b/src/pages/2-model_creation.py
@@ -2,6 +2,8 @@ from Packages import *
st.set_page_config(page_title="NIRS Utils", page_icon=":goat:", layout="wide")
from Modules import *
from Class_Mod.DATA_HANDLING import *
+
+
# HTML pour le bandeau "CEFE - CNRS"
bandeau_html = """
<div style="width: 100%; background-color: #4682B4; padding: 10px; margin-bottom: 10px;">
@@ -19,7 +21,7 @@ if st.session_state["interface"] == 'simple':
def nn(x):
return x is not None
########################################################################################
-reg_algo = ["","Full-PLSR", "Locally Weighted PLSR", "Interval-PLSR"]
+reg_algo = ["","Full-PLSR", "Locally Weighted PLSR", "Interval-PLSR", "Full-PLSR-sklearn"]
# page Design
st.header("Calibration Model Development", divider='blue')
@@ -47,26 +49,40 @@ y = pd.DataFrame
# load .csv file
if file == files_format[0]:
xcal_csv = M3.file_uploader("Select NIRS Data", type="csv", help=" :mushroom: select a csv matrix with samples as rows and lambdas as columns")
+ if xcal_csv:
+ sepx = M3.radio("Select separator (X file) - _detected_: " + str(find_delimiter('data/'+xcal_csv.name)),
+ options=[";", ","], index=[";", ","].index(str(find_delimiter('data/'+xcal_csv.name))), key=0)
+ hdrx = M3.radio("samples name (X file)? - _detected_: " + str(find_col_index('data/'+xcal_csv.name)),
+ options=["no", "yes"], index=["no", "yes"].index(str(find_col_index('data/'+xcal_csv.name))), key=1)
+ if hdrx == "yes": col = 0
+ else: col = False
+
ycal_csv = M3.file_uploader("Select corresponding Chemical Data", type="csv", help=" :mushroom: select a csv matrix with samples as rows and chemical values as a column")
+
+
+ if ycal_csv:
+ sepy = M3.radio("separator (Y file): ", options=[";", ","], key=2)
+ hdry = M3.radio("samples name (Y file)?: ", options=["no", "yes"], key=3)
+ if hdry == "yes": col = 0
+ else: col = False
+
if xcal_csv and ycal_csv:
-
- # Select list for CSV delimiter
- sep = M3.radio("Select csv separator - _detected_: " + str(find_delimiter('data/'+xcal_csv.name)),
- options=[";", ","], index=[";", ","].index(str(find_delimiter('data/'+xcal_csv.name))), key=0)
- # Select list for CSV header True / False
- hdr = M3.radio("indexes column in csv? - _detected_: " + str(find_col_index('data/'+xcal_csv.name)),
- options=["no", "yes"], index=["no", "yes"].index(str(find_col_index('data/'+xcal_csv.name))), key=1)
- ###############
- if hdr == 'yes':
- col = 0
+ spectra, meta_data = col_cat(pd.read_csv(xcal_csv, decimal='.', sep=sepx, index_col=col, header=0))
+ y, _ = col_cat(pd.read_csv(ycal_csv, decimal='.', sep=sepy, index_col=col))
+
+ y = pd.DataFrame(y).astype(float).iloc[:,0]
+ spectra = pd.DataFrame(spectra).astype(float)
+ st.write(meta_data)
+
+ if spectra.shape[0] == y.shape[0]:
+ pass
else:
- col = False
- ###############
- spectra, y = utils.load_csv(xcal_csv, ycal_csv, autoremove_na=True, sep=sep, x_hdr=0, y_hdr=0, x_index_col=col, y_index_col=col)
- spectra = pd.DataFrame(spectra)
- y = pd.DataFrame(y).iloc[:,0]
-
+ M3.warning('The number of samples is different in X and Y')
+ y = pd.DataFrame
+ spectra = pd.DataFrame
+
+
## Load .dx file
@@ -94,7 +110,22 @@ if not spectra.empty and not y.empty:
# Assign data to training and test sets
X_train, y_train = pd.DataFrame(spectra.iloc[train_index,:]), y.iloc[train_index]
X_test, y_test = pd.DataFrame(spectra.iloc[test_index,:]), y.iloc[test_index]
- M2.write("ADD HERE A TABLE WITH SHAPE OF THE DATA: SAMPLES NUMBER AND WAVELENGTHS OR CHEMICAL VALUES NUMBER FOR TRAIN AND TEST")
+
+ sk = lambda x: skew(x, axis=0, bias=True)
+ ku = lambda x:kurtosis(x, axis=0, bias=True)
+ cv = lambda x: x.std()*100/x.mean()
+
+ M2.write('Loaded data summary')
+ M2.write(f'The loaded spectra consist of {spectra.shape[1]} wavelengths')
+ datainf = pd.DataFrame()
+ datainf['N samples'] = [X_train.shape[0], X_test.shape[0], spectra.shape[0] ]
+ datainf['Mean'] = [y_train.mean(), y_test.mean(), y.mean()]
+ datainf['SD'] = [y_train.std(), y_test.std(), y.std()]
+ datainf['CV(%)'] = [cv(y_train), cv(y_test), cv(y)]
+ datainf['Skewness'] = [sk(y_train), sk(y_test), sk(y)]
+ datainf['Kurtosis'] = [ku(y_train), ku(y_test), ku(y)]
+ datainf.index = ['Train', 'Test', 'Total']
+ M2.write(datainf.round(3))
#######################################
regression_algo = M1.selectbox("Choose the algorithm for regression", options=reg_algo, key = 12)
@@ -121,6 +152,10 @@ if not spectra.empty and not y.empty:
M2.write('-- Table of selected wavelengths --')
M2.table(rega[0])
+ elif regression_algo == reg_algo[4]:
+ Reg = PlsR(x_train = X_train, x_test = X_test, y_train = y_train, y_test = y_test)
+ reg_model = Reg.model_
+
################# Model analysis ############
if regression_algo in reg_algo[1:]:
yc = Reg.pred_data_[0]
@@ -137,24 +172,27 @@ if not spectra.empty and not y.empty:
#model_export = M1.selectbox("Choose way to export", options=["pickle", "joblib"], key=20)
model_name = M9.text_input('Give it a name')
+ date_time = datetime.datetime.strftime(datetime.date.today(), '_%Y_%m_%d_')
if M9.button('Export Model'):
path = 'data/models/model_'
if file == files_format[0]:
#export_package = __import__(model_export)
- with open(path + model_name + '_on_' + xcal_csv.name + '_and_' + ycal_csv.name + '_data_' + '.pkl','wb') as f:
+ with open(path + model_name + date_time + '_created_on_' + xcal_csv.name[:xcal_csv.name.find(".")] +""+
+ '_and_' + ycal_csv.name[:ycal_csv.name.find(".")] + '_data_' + '.pkl','wb') as f:
joblib.dump(reg_model, f)
if regression_algo == reg_algo[3]:
rega[1].sort()
- pd.DataFrame(rega[1]).to_csv(path + model_name + '_on_' + xcal_csv.name + '_and_' + ycal_csv.name + '_data_'+'Wavelengths_index.csv', sep = ';')
+ pd.DataFrame(rega[1]).to_csv(path + model_name + date_time + '_on_' + xcal_csv.name[:xcal_csv.name.find(".")]
+ + '_and_' + ycal_csv.name[:ycal_csv.name.find(".")] + '_data_'+'Wavelengths_index.csv', sep = ';')
elif file == files_format[1]:
#export_package = __import__(model_export)
- with open(path + model_name + '_on_' + '_data_' + '.pkl','wb') as f:
+ with open(path + model_name + '_on_'+ data_file.name[:data_file.name.find(".")] + '_data_' + '.pkl','wb') as f:
joblib.dump(reg_model, f)
if regression_algo == reg_algo[3]:
rega[1].sort()
- pd.DataFrame(rega[1]).to_csv(path + model_name + '_on_' + '_data_'+'Wavelengths_index.csv', sep = ';')
- st.write('Model Exported')
+ pd.DataFrame(rega[1]).to_csv(path +data_file.name[:data_file.name.find(".")]+ model_name + date_time+ '_on_' + '_data_'+'Wavelengths_index.csv', sep = ';')
+ st.write('Model Exported ')
if regression_algo == reg_algo[3]:
st.write('Model Exported')
diff --git a/src/pages/4-inputs.py b/src/pages/4-inputs.py
new file mode 100644
index 0000000000000000000000000000000000000000..1480b8768e5b07ceb26d67c8c43c4d95c38787ac
--- /dev/null
+++ b/src/pages/4-inputs.py
@@ -0,0 +1,100 @@
+import streamlit as st
+
+
+# HTML for the banner "CEFE - CNRS"
+bandeau_html = """
+<div style="width: 100%; background-color: #4682B4; padding: 10px; margin-bottom: 10px;">
+ <h1 style="text-align: center; color: white;">CEFE - CNRS</h1>
+</div>
+"""
+
+
+# Inject the HTML code for the banner
+st.markdown(bandeau_html, unsafe_allow_html=True)
+
+
+# Initialize session state
+if 'form_submitted' not in st.session_state:
+ st.session_state['form_submitted'] = False
+
+
+# Page header
+with st.container():
+ # Center the buttons
+ st.markdown(
+ """
+ <style>
+ .stButton>button {
+ display: block;
+ margin: 0 auto;
+ width: 200px;
+ height: 50px;
+ font-size: 16px;
+ }
+ </style>
+ """,
+ unsafe_allow_html=True
+ )
+
+
+ # Text input fields
+ with st.form(key='my_form'):
+ st.markdown("#### Fill in your details: ####")
+
+
+ col1, col3,col2 = st.columns((2,0.5,2))
+
+
+ with col1:
+ meta_contact_name = st.text_input('First and Last name :', 'Life of Brian')
+ meta_project = st.text_input('Project name :', 'Life of Brian')
+ meta_sample_species = st.text_input('If relevant, sample species :', 'Life of Brian')
+ meta_sample_category_options = ["Soil", "Plant", "Animal", "Other"]
+ meta_sample_category = st.radio("Sample category description :", meta_sample_category_options)
+ meta_sample_pretreatment_options = ["Powder", "Pastile", "Liquid"]
+ meta_sample_pretreatment = st.radio("Type of sample pre-treatment :", meta_sample_pretreatment_options)
+
+
+ with col2:
+ meta_contact_email = st.text_input('Email :', 'Example@cefe.cnrs.fr')
+ meta_machine_ID = st.text_input('NIRS ID :', 'Life of Brian')
+ meta_sample_sub_category_options = ["Green leave", "Leaf litter", "Litter", "Humus", "Soil", "Animal part", "Animal Powder", "Fungal sample", "Other"]
+ meta_sample_sub_category = st.radio("Sample category description :", meta_sample_sub_category_options)
+ meta_sample_humidity_options = ["Dry", "Fresh", "Wet"]
+ meta_sample_humidity = st.radio("Humidity state of the sample:", meta_sample_humidity_options)
+ meta_scan_place_options = ["Pace", "Other"]
+ meta_scan_place = st.radio("If relevant, sample species :", meta_scan_place_options)
+
+
+ submitted = st.form_submit_button(label='Send')
+
+
+ if submitted:
+ # Check if email is valid and not null
+ if '@' not in meta_contact_email or meta_contact_email == '':
+ st.warning("Please enter a valid email address.")
+ else:
+ # Save the form data here
+ st.session_state['form_submitted'] = True
+ st.success('Form sent successfully!')
+
+
+ # Afficher les boutons seulement si le formulaire a été soumis
+ if st.session_state['form_submitted']:
+ # Buttons
+ with st.container():
+ header3, header4 = st.columns(2)
+ if header3.button("Samples Selection"):
+ st.session_state['current_page'] = 'pages\\1-samples_selection.py'
+ st.switch_page('pages\\1-samples_selection.py')
+ if header4.button("Model Creation"):
+ st.session_state['current_page'] = 'pages\\2-model_creation.py'
+ st.switch_page('pages\\2-model_creation.py')
+
+
+
+
+# Bouton de retour à la page d'accueil
+st.markdown('<div style="text-align: left;"><a href="/"> <img src="house.jpg" alt="Home" style="width:50px;height:50px;"> </a></div>', unsafe_allow_html=True)
+
+
diff --git a/src/plot_axe1_axe2.png b/src/plot_axe1_axe2.png
new file mode 100644
index 0000000000000000000000000000000000000000..8d466994394313fcf101e4163fc95a2fbef329b4
Binary files /dev/null and b/src/plot_axe1_axe2.png differ
diff --git a/src/plot_axe1_axe3.png b/src/plot_axe1_axe3.png
new file mode 100644
index 0000000000000000000000000000000000000000..9ba83fbfdd2e3470319c5ff9597824e66a8d11e5
Binary files /dev/null and b/src/plot_axe1_axe3.png differ
diff --git a/src/plot_axe2_axe3.png b/src/plot_axe2_axe3.png
new file mode 100644
index 0000000000000000000000000000000000000000..168fd6c0f1d50b2810d3e695de65f9d104c4b901
Binary files /dev/null and b/src/plot_axe2_axe3.png differ