update plot for office classification, upload training notebook

app.py

@@ -81,9 +81,7 @@ def translate_to_de(SubmittedText):
     return text
 
 
-def create_bar_plot(rates, language):
-    barnames = BARS_DEP_FR if language == "fr" else BARS_DEP_DE
-
+def create_bar_plot(rates, barnames):
     y_pos = np.arange(len(barnames))
     plt.barh(y_pos, rates)
     plt.yticks(y_pos, barnames)
@@ -128,7 +126,7 @@ def show_chosen_category(barnames, rates, language):
 
 
 pipeDep = load_model("saved_model_dep")
-pipeOffice = load_model("saved_model_dep")
+pipeOffice = load_model("saved_model_office")
 
 labelencoderOffice = preprocessing.LabelEncoder()
 labelencoderOffice.classes_ = np.load("classes_office.npy")
@@ -147,12 +145,16 @@ def textclassification(SubmittedText):
     # Make the prediction with the 1000 first characters
     images = []
     chosenCategoryTexts = []
-    for pipe in (pipeDep, pipeOffice):
+
+    labelsDep = BARS_DEP_FR if language == "fr" else BARS_DEP_DE
+    labelsOffice = labelencoderOffice.classes_
+
+    for pipe, barnames in zip((pipeDep, pipeOffice), (labelsDep, labelsOffice)):
         prediction = pipe(SubmittedText[0:1000], return_all_scores=True)
         rates = [row["score"] for row in prediction[0]]
 
         # Create barplot & output text
-        im, barnames = create_bar_plot(rates, language)
+        im, barnames = create_bar_plot(rates, barnames)
         images.append(im)
 
         chosenCategoryText = show_chosen_category(barnames, rates, language)