diff --git a/src/pages/1-samples_selection.py b/src/pages/1-samples_selection.py
index 8b33b5f16c78cb57c43520476311ad99f3b94070..c9085bd0d096c62d619ef6b0b26fc11220fbd6f5 100644
--- a/src/pages/1-samples_selection.py
+++ b/src/pages/1-samples_selection.py
@@ -280,7 +280,7 @@ if not spectra.empty:
             residuals = dr_model.residuals_
             fig = px.scatter(x=leverage[ax1], y=residuals[ax1], color=leverage[ax1]*residuals[ax1], color_continuous_scale='Blues')
             fig.update_layout(xaxis_title="Leverage", yaxis_title="Residuals")
-            st.plotly_chart(fig)
+            st.plotly_chart(fig, use_container_width=True)
             img = pio.to_image(fig, format="png")
             with open("./Report/figures/graphe_influence.png", "wb") as f:
                 f.write(img)
diff --git a/src/pages/2-model_creation.py b/src/pages/2-model_creation.py
index 8b0ea6ec6453e613c6016c4bf99c47e1a0cc9023..a58048f72f4fa29decf78dad8d0b1a043857e8e4 100644
--- a/src/pages/2-model_creation.py
+++ b/src/pages/2-model_creation.py
@@ -28,7 +28,7 @@ reg_algo = ["","Full-PLSR", "Locally Weighted PLSR", "Interval-PLSR", "Full-PLSR
 # page Design
 st.header("Calibration Model Development", divider='blue')
 st.write("Create a predictive model, then use it for predicting your target variable (chemical values) from NIRS spectra")
-M1, M2, M3 = st.columns([2,2,2])
+M1, M2, M3 = st.columns([2,3,2])
 M4, M5 = st.columns([6,2])
 st.write("---")
 st.header("Model Diagnosis", divider='blue')