Doc: readme; Feat : language utility class, small fix

sources/language.py

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+import langid
+import re
+import nltk
+from nltk.sentiment.vader import SentimentIntensityAnalyzer
+
+class LanguageUtility:
+    """LanguageUtility for language, or emotion identification"""
+    def __init__(self):
+        try:
+            nltk.data.find('vader_lexicon')
+        except LookupError:
+            nltk.download('vader_lexicon')
+        self.sid = SentimentIntensityAnalyzer()
+    
+    def detect_language(self, text: str) -> str:
+        """
+        Detect the language of the given text using langdetect
+        Args:
+            text: string to analyze
+        Returns: ISO639-1 language code
+        """
+        langid.set_languages(['fr', 'en', 'zh', 'es'])  # ISO 639-1 codes
+        lang, score = langid.classify(text)
+        return lang
+
+    def detect_emotion(self, text: str) -> str:
+        """
+        Detect the dominant emotion in the given text
+        Args:
+            text: string to analyze
+        Returns: string of the dominant emotion
+        """
+        try:
+            scores = self.sid.polarity_scores(text)
+            emotions = {
+                'Happy': max(scores['pos'], 0),
+                'Angry': 0,
+                'Sad': max(scores['neg'], 0),
+                'Fear': 0,
+                'Surprise': 0
+            }
+            if scores['compound'] < -0.5:
+                emotions['Angry'] = abs(scores['compound']) * 0.5
+                emotions['Fear'] = abs(scores['compound']) * 0.5
+            elif scores['compound'] > 0.5:
+                emotions['Happy'] = scores['compound']
+                emotions['Surprise'] = scores['compound'] * 0.5
+            dominant_emotion = max(emotions, key=emotions.get)
+            if emotions[dominant_emotion] == 0:
+                return 'Neutral'
+            return dominant_emotion
+        except Exception as e:
+            raise e
+    
+    def analyze(self, text):
+        """
+        Combined analysis of language and emotion
+        Args:
+            text: string to analyze
+        Returns: dictionary with language and emotion results
+        """
+        try:
+            language = self.detect_language(text)
+            emotions = self.detect_emotion(text)
+            return {
+                "language": language,
+                "emotions": emotions
+            }
+        except Exception as e:
+            raise e
+
+if __name__ == "__main__":
+    detector = LanguageUtility()
+    
+    test_texts = [
+        "I am so happy today!",
+        "Qué tristeza siento ahora",
+        "我不要去巴黎",
+        "La vie c'est cool"
+    ]
+    for text in test_texts:
+        print(f"\nAnalyzing: {text}")
+        result = detector.analyze(text)
+        print(result)