Update app.py

app.py

@@ -17,10 +17,8 @@ politeness_classifier = pipeline("text-classification", model="Genius1237/xlm-ro
 def analyze_sentiment_with_influence(icelandic_text):
     sentiment_label, sentiment_score = analyze_sentiment(icelandic_text)
     
-    # Convert sentiment label
     sentiment_label = sentiment_label.replace("LABEL_", "")
     
-    # Use Ferret to analyze influential words for sentiment
     explanations_sentiment = sentiment_bench.explain(icelandic_text, target=1)  # Adjust target as necessary
     
     influential_words = []
@@ -30,7 +28,6 @@ def analyze_sentiment_with_influence(icelandic_text):
             token_score_pairs = zip(tokens, explanation.scores)
             influential_words.extend([(token, score) for token, score in token_score_pairs])
     
-    # Format your response to include influential words
     influential_words_str = "; ".join([f"{token} ({score:.2f})" for token, score in influential_words])
     
     analysis_results = (
@@ -39,6 +36,27 @@ def analyze_sentiment_with_influence(icelandic_text):
     )
     return analysis_results
 
+def replace_encoding(tokens):
+    return [token.replace('Ġ', ' ')
+                 .replace('ð', 'ð')
+                 .replace('é', 'é')
+                 .replace('æ', 'æ')
+                 .replace('ý', 'ý')
+                 .replace('á', 'á')
+                 .replace('ú', 'ú')
+                 .replace('ÃŃ', 'í')
+                 .replace('Ãö', 'ö')
+                 .replace('þ', 'þ')
+                 .replace('Ãģ', 'Á')
+                 .replace('Ãį', 'Ú')
+                 .replace('Ãĵ', 'Ó')
+                 .replace('ÃĨ', 'Æ')
+                 .replace('ÃIJ', 'Ð')
+                 .replace('Ãĸ', 'Ö')
+                 .replace('Ãī', 'É')
+                 .replace('Ãļ', 'ý')
+            for token in tokens[1:-1]]
+
 def translate_text(text):
     translation = translator(text, max_length=512)
     return translation[0]['translation_text']
@@ -65,33 +83,26 @@ def analyze_sentiment(text):
 
 def analyze_text(icelandic_text):
     formality_label, formality_score = analyze_formality(icelandic_text)
-    sentiment_label, sentiment_score = analyze_sentiment(icelandic_text)
-
-    # Convert sentiment label
-    sentiment_label = sentiment_label.replace("LABEL_", "")
-
     translated_text = translate_text(icelandic_text)
-
     toxicity_results = analyze_toxicity(translated_text)
     if isinstance(toxicity_results, list):
         toxicity_results = toxicity_results[0]
-    
-    # Determine toxicity label based on score
     toxicity_label = '1' if toxicity_results['score'] >= 0.5 else '0'
-
     politeness_label, politeness_score = analyze_politeness(translated_text)
-    # Convert politeness label to binary
     politeness_label = '1' if politeness_label.lower() == 'polite' else '0'
 
+    sentiment_analysis_with_influence = analyze_sentiment_with_influence(icelandic_text)
+
     analysis_results = (
         f"Translated Text: {translated_text}\n\n"
-        f"Sentiment: Label: {sentiment_label}, Score: {round(sentiment_score, 2)}\n"
+        f"{sentiment_analysis_with_influence}\n"  
         f"Formality: Label: {formality_label}, Score: {round(formality_score, 2)}\n"
         f"Toxicity: Label: {toxicity_label}, Score: {round(toxicity_results['score'], 2)}\n"
         f"Politeness: Label: {politeness_label}, Score: {round(politeness_score, 2)}"
     )
     return analysis_results
 
+
 demo = gr.Interface(fn=analyze_text, 
                     inputs=gr.Textbox(lines=2, placeholder="Enter Icelandic Text Here..."), 
                     outputs=gr.Textbox(label="Analysis Results"),