add logging

app.py

@@ -13,6 +13,9 @@ from sklearn import preprocessing
 from langdetect import detect
 from matplotlib import pyplot as plt
 import imageio
+import logging
+
+logging.getLogger().setLevel(logging.INFO)
 
 DESCRIPTION = """Diese Anwendung teilt Vorstösse an das federführende Departement zu und
     macht einen Vorschlag für das zuständige Amt. Der Vorschlag der Anwendung ist nicht
@@ -135,6 +138,9 @@ labelencoderOffice.classes_ = np.load("classes_office.npy")
 
 def textclassification(SubmittedText):
     language = detect(SubmittedText)
+    logging.info(
+        f"SubmittedText received. Detected language: {language}. SubmittedText: {SubmittedText}"
+    )
 
     # Translate the input to german if necessary
     if language == "fr":
@@ -151,8 +157,9 @@ def textclassification(SubmittedText):
 
     for pipe, barnames in zip((pipeDep, pipeOffice), (labelsDep, labelsOffice)):
         plt.clf()
-        prediction = pipe(SubmittedText[0:1000], return_all_scores=True)
-        rates = [row["score"] for row in prediction[0]]
+        prediction = pipe(SubmittedText[0:1000], top_k=None)
+        # print(prediction)
+        rates = [row["score"] for row in prediction]
 
         # Create barplot & output text
         im, barnames = create_bar_plot(rates, barnames)
@@ -162,6 +169,9 @@ def textclassification(SubmittedText):
         chosenCategoryTexts.append(chosenCategoryText)
 
     # return chosenCategoryText & image for both predictions
+    logging.info(
+        f"Prediction Department: {chosenCategoryTexts[0]}\n\n, Prediction Amt: {chosenCategoryTexts[1]}"
+    )
     return chosenCategoryTexts[0], images[0], chosenCategoryTexts[1], images[1]