app->libretranslate; mv tests/ inside libretranslate/

libretranslate/language.py

@@ -0,0 +1,117 @@
+import string
+
+from argostranslate import translate
+from libretranslate.detect import Detector, UnknownLanguage
+
+__languages = None
+
+def load_languages():
+    global __languages
+
+    if __languages is None or len(__languages) == 0:
+        __languages = translate.get_installed_languages()
+
+    return __languages
+
+def detect_languages(text):
+    # detect batch processing
+    if isinstance(text, list):
+        is_batch = True
+    else:
+        is_batch = False
+        text = [text]
+
+    # get the candidates
+    candidates = []
+    for t in text:
+        try:
+            d = Detector(t).languages
+            for i in range(len(d)):
+                d[i].text_length = len(t)
+            candidates.extend(d)
+        except UnknownLanguage:
+            pass
+
+    # total read bytes of the provided text
+    text_length_total = sum(c.text_length for c in candidates)
+
+    # Load language codes
+    languages = load_languages()
+    lang_codes = [l.code for l in languages]
+
+    # only use candidates that are supported by argostranslate
+    candidate_langs = list(
+        filter(lambda l: l.text_length != 0 and l.code in lang_codes, candidates)
+    )
+
+    # this happens if no language could be detected
+    if not candidate_langs:
+        # use language "en" by default but with zero confidence
+        return [{"confidence": 0.0, "language": "en"}]
+
+    # for multiple occurrences of the same language (can happen on batch detection)
+    # calculate the average confidence for each language
+    if is_batch:
+        temp_average_list = []
+        for lang_code in lang_codes:
+            # get all candidates for a specific language
+            lc = list(filter(lambda l: l.code == lang_code, candidate_langs))
+            if len(lc) > 1:
+                # if more than one is present, calculate the average confidence
+                lang = lc[0]
+                lang.confidence = sum(l.confidence for l in lc) / len(lc)
+                lang.text_length = sum(l.text_length for l in lc)
+                temp_average_list.append(lang)
+            elif lc:
+                # otherwise just add it to the temporary list
+                temp_average_list.append(lc[0])
+
+        if temp_average_list:
+            # replace the list
+            candidate_langs = temp_average_list
+
+    # sort the candidates descending based on the detected confidence
+    candidate_langs.sort(
+        key=lambda l: (l.confidence * l.text_length) / text_length_total, reverse=True
+    )
+
+    return [{"confidence": l.confidence, "language": l.code} for l in candidate_langs]
+
+
+def improve_translation_formatting(source, translation, improve_punctuation=True):
+    source = source.strip()
+
+    if not len(source):
+        return ""
+    
+    if not len(translation):
+        return source
+    
+    if improve_punctuation:
+        source_last_char = source[len(source) - 1]
+        translation_last_char = translation[len(translation) - 1]
+
+        punctuation_chars = ['!', '?', '.', ',', ';']
+        if source_last_char in punctuation_chars:
+            if translation_last_char != source_last_char:
+                if translation_last_char in punctuation_chars:
+                    translation = translation[:-1]
+
+                translation += source_last_char
+        elif translation_last_char in punctuation_chars:
+            translation = translation[:-1]
+
+    if source.islower():
+        return translation.lower()
+
+    if source.isupper():
+        return translation.upper()
+
+    if source[0].islower():
+        return translation[0].lower() + translation[1:]
+
+    if source[0].isupper():
+        return translation[0].upper() + translation[1:]
+
+    return translation
+