gestion d erreur

src/pages/3-prediction.py

@@ -115,12 +115,13 @@ if not pred_data.empty and params:# Load the model with joblib
     #dir = os.listdir('data/models/')[1:]
     dir = os.listdir('data/models/')
     dir.insert(0,'')
-    model_name = M6.selectbox("Select your model from the dropdown list:", options = dir, key = 21)
+    model_name = M6.selectbox("Select your model from the dropdown list:", options = dir, key = 21, format_func=lambda x: x if x else "<Select>")
 
-    if model_name and model_name !='':
+    if model_name:
         export_name += '_with_' + model_name[:model_name.find('.')]
         with open('data/models/'+ model_name,'rb') as f:
             loaded_model = joblib.load(f)
+            ncols = loaded_model.n_features_in_
             
         if loaded_model:
             M6.success("The model has been loaded successfully", icon="✅")
@@ -132,50 +133,55 @@ if not pred_data.empty and params:# Load the model with joblib
                     idx = []
                     for i in range(intervalls.shape[0]):
                         idx.extend(np.arange(intervalls[i,0], intervalls[i,1]+1))
+                    if max(idx) <= preprocessed.shape[1]:
+                        preprocessed = preprocessed.iloc[:,idx] ### get predictors
+                    else:
+                        M6.error("Error: The number of columns in your data does not match the number of columns used to train the model. Please ensure they are the same.")
+
 
 if loaded_model:
     if M6.button('Predict', type='primary'):
-            if s:
-                result = loaded_model.predict(preprocessed.iloc[:,idx])
+            if ncols == preprocessed.shape[1]:
+                result = pd.DataFrame(loaded_model.predict(preprocessed), index = preprocessed.index)
+
+                #############################
+                if preprocessed.shape[1]>1:
+                    M5.write('Predicted values distribution')
+                    # Creating histogram
+                    fig, axs = plt.subplots(1, 1, figsize =(15, 3), 
+                                            tight_layout = True)
+                    
+                    # Add x, y gridlines 
+                    axs.grid( color ='grey', linestyle ='-.', linewidth = 0.5, alpha = 0.6) 
+                    # Remove axes splines 
+                    for s in ['top', 'bottom', 'left', 'right']: 
+                        axs.spines[s].set_visible(False) 
+                    # Remove x, y ticks
+                    axs.xaxis.set_ticks_position('none') 
+                    axs.yaxis.set_ticks_position('none') 
+                    # Add padding between axes and labels 
+                    axs.xaxis.set_tick_params(pad = 5) 
+                    axs.yaxis.set_tick_params(pad = 10) 
+                    # Creating histogram
+                    N, bins, patches = axs.hist(result, bins = 12)
+                    # Setting color
+                    fracs = ((N**(1 / 5)) / N.max())
+                    norm = colors.Normalize(fracs.min(), fracs.max())
+                    
+                    for thisfrac, thispatch in zip(fracs, patches):
+                        color = plt.cm.viridis(norm(thisfrac))
+                        thispatch.set_facecolor(color)
+
+                    M5.pyplot(fig)
+                st.write('Predicted values table')
+                st.dataframe(result.T)
+                ##################################
+
+                # result.to_csv(export_folder + export_name + '.csv', sep = ';')
+                # export to local drive - Download
+                download_results(export_folder + export_name + '.csv', export_name + '.csv')
+                # create a report with information on the prediction
+                ## see https://stackoverflow.com/a/59578663
             else:
-                # use prediction function from application_functions.py to predict chemical values
-                result = loaded_model.predict(x2)
-            result = pd.DataFrame(result, index = pred_data.index)
-
-            #############################
-            M5.write('Predicted values distribution')
-            # Creating histogram
-            fig, axs = plt.subplots(1, 1, figsize =(15, 3), 
-                                    tight_layout = True)
-            
-            # Add x, y gridlines 
-            axs.grid( color ='grey', linestyle ='-.', linewidth = 0.5, alpha = 0.6) 
-            # Remove axes splines 
-            for s in ['top', 'bottom', 'left', 'right']: 
-                axs.spines[s].set_visible(False) 
-            # Remove x, y ticks
-            axs.xaxis.set_ticks_position('none') 
-            axs.yaxis.set_ticks_position('none') 
-            # Add padding between axes and labels 
-            axs.xaxis.set_tick_params(pad = 5) 
-            axs.yaxis.set_tick_params(pad = 10) 
-            # Creating histogram
-            N, bins, patches = axs.hist(result, bins = 12)
-            # Setting color
-            fracs = ((N**(1 / 5)) / N.max())
-            norm = colors.Normalize(fracs.min(), fracs.max())
+                M6.error(f'Error: The model was trained with {ncols} wavelengths, but you provided {preprocessed.shape[1]} wavelengths for prediction. Please ensure they match.')
             
-            for thisfrac, thispatch in zip(fracs, patches):
-                color = plt.cm.viridis(norm(thisfrac))
-                thispatch.set_facecolor(color)
-
-            M5.pyplot(fig)
-            st.write('Predicted values table')
-            st.dataframe(result.T)
-            ##################################
-
-            result.to_csv(export_folder + export_name + '.csv', sep = ';')
-            # export to local drive - Download
-            download_results(export_folder + export_name + '.csv', export_name + '.csv')
-            # create a report with information on the prediction
-            ## see https://stackoverflow.com/a/59578663
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