Normalize results to percent values

app.py

@@ -26,7 +26,7 @@ class Detect():
         docs = Document([], text=self.text)
         nlp = Pipeline(lang="multilingual", processors="langid")
         nlp(docs)
-        return [docs.lang, 1.00]
+        return [docs.lang, 100]
     def langdetect(self) -> list[str, float]:
         from langdetect import detect, detect_langs
         from langdetect import DetectorFactory
@@ -49,8 +49,8 @@ class Detect():
                  "te", "th", "tl", "tr", "ug", "uk", "ur", "vi", "vo", "wa", "xh", "zh", "zu"]
         import py3langid
         lang, prob = py3langid.classify(self.text) # unpack the result tuple in variables
-        return [lang, abs(round(number=prob, ndigits=2))]       
-    def lingua(self) -> list[str, float]:
+        return [lang, abs(round(number=prob * 100, ndigits=2))]       
+    def linguapy(self) -> list[str, float]:
         from lingua import Language, LanguageDetectorBuilder
         detector: LanguageDetector = LanguageDetectorBuilder.from_all_languages().with_preloaded_language_models().build()
         confidence_values: List[ConfidenceValue] = detector.compute_language_confidence_values(self.text)
@@ -131,7 +131,7 @@ def detect_language(input_text: str, used_libraries: list[str]) -> tuple[str, st
     if 'py3langid' in used_libraries:
         detections.append(['py3langid'] + detectinstance.py3langid())
     if 'lingua-py' in used_libraries:
-        detections.append(['lingua-py'] + detectinstance.lingua())
+        detections.append(['lingua-py'] + detectinstance.linguapy())
     if 'pycld2' in used_libraries:
         detections.append(['pycld2'] + detectinstance.pycld2())
     if 'fastlangdetect' in used_libraries: