refactor: remove unsused sentiment analysis

sources/browser.py

@@ -137,8 +137,6 @@ def create_driver(headless=False, stealth_mode=True, crx_path="./crx/nopecha.crx
     user_agent = get_random_user_agent()
     width, height = (1920, 1080)
     user_data_dir = tempfile.mkdtemp(prefix="chrome_profile_")
-    import os
-    print(f"Running as UID: {os.getuid()}")  # Will show 0 if root
     chrome_options.add_argument("--no-sandbox")
     chrome_options.add_argument('--disable-dev-shm-usage')
     profile_dir = f"/tmp/chrome_profile_{uuid.uuid4().hex[:8]}"

sources/language.py

@@ -16,7 +16,6 @@ class LanguageUtility:
         args:
             supported_language: list of languages for translation, determine which Helsinki-NLP model to load
         """
-        self.sid = None 
         self.translators_tokenizer = None 
         self.translators_model = None
         self.logger = Logger("language.log")
@@ -25,11 +24,6 @@ class LanguageUtility:
     
     def load_model(self) -> None:
         animate_thinking("Loading language utility...", color="status")
-        try:
-            nltk.data.find('vader_lexicon')
-        except LookupError:
-            nltk.download('vader_lexicon')
-        self.sid = SentimentIntensityAnalyzer()
         self.translators_tokenizer = {lang: MarianTokenizer.from_pretrained(f"Helsinki-NLP/opus-mt-{lang}-en") for lang in self.supported_language if lang != "en"}
         self.translators_model = {lang: MarianMTModel.from_pretrained(f"Helsinki-NLP/opus-mt-{lang}-en") for lang in self.supported_language if lang != "en"}
     
@@ -65,49 +59,17 @@ class LanguageUtility:
         translation = model.generate(**inputs)
         return tokenizer.decode(translation[0], skip_special_tokens=True)
 
-    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'
-            self.logger.info(f"Emotion: {dominant_emotion} for text: {text}")
-            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
+        Returns: dictionary with language related information
         """
         try:
             language = self.detect_language(text)
-            emotions = self.detect_emotion(text)
             return {
-                "language": language,
+                "language": language
-                "emotions": emotions
             }
         except Exception as e:
             raise e
@@ -125,4 +87,4 @@ if __name__ == "__main__":
         pretty_print(f"Language: {detector.detect_language(text)}", color="status")
         result = detector.analyze(text)
         trans = detector.translate(text, result['language'])
-        pretty_print(f"Translation: {trans} - from: {result['language']} - Emotion: {result['emotions']}")
+        pretty_print(f"Translation: {trans} - from: {result['language']}")