diff --git a/src/pages/3-prediction.py b/src/pages/3-prediction.py
index 8600dee93c0ea6b3d83d84485eed64ec05f04495..f73aa24998196e1a44f341b117b75309a4527fdf 100644
--- a/src/pages/3-prediction.py
+++ b/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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