Update app/utils/prediction.py

app/utils/prediction.py

@@ -6,9 +6,6 @@ from deep_translator import GoogleTranslator  # Import GoogleTranslator for tran
 
 # Load glossary
 def load_glossary(file_path):
-
-
-
     glossary = {}
     try:
         with open(file_path, 'r', encoding='utf-8') as file:
@@ -30,6 +27,18 @@ model = BertForSequenceClassification.from_pretrained("Maulidaaa/bert-safe-model
 glossary = load_glossary('glossary.txt')
 
 # Translate function using Google Translator and glossary
+def translate_with_glossary(text, target_lang='id'):
+    try:
+        # Translate the text first
+        translated = GoogleTranslator(source='auto', target=target_lang).translate(text)
+
+        # Replace terms based on glossary
+        for en_term, id_term in glossary.items():
+            translated = translated.replace(en_term.lower(), id_term.lower())
+
+        return translated.capitalize()
+
+    except Exception as e:
         print(f"Error during translation: {e}")
         return text.capitalize()
 
@@ -51,7 +60,10 @@ def predict_with_description(ingredient, df, target_lang='id'):
     df_match['IUPAC Name_lower'] = df_match['IUPAC Name'].str.lower()
 
     ingredient_lower = ingredient.lower()
-    match_row = df_match[(df_match['INCI name_lower'] == ingredient_lower) | (df_match['IUPAC Name_lower'] == ingredient_lower)]
+    match_row = df_match[
+        (df_match['INCI name_lower'] == ingredient_lower) |
+        (df_match['IUPAC Name_lower'] == ingredient_lower)
+    ]
 
     if not match_row.empty:
         row = match_row.iloc[0]
@@ -70,8 +82,8 @@ def predict_with_description(ingredient, df, target_lang='id'):
         risk_desc = "Risk info not available"
 
     result = predict(desc)
-    
-    # Translate only Description and Risk Description using glossary and Google Translator
+
+    # Translate only Description, Function, and Risk Description using glossary and Google Translator
     translated_desc = translate_with_glossary(desc, target_lang)
     translated_risk_desc = translate_with_glossary(risk_desc, target_lang)
     translated_function = translate_with_glossary(func, target_lang)
@@ -84,4 +96,4 @@ def predict_with_description(ingredient, df, target_lang='id'):
         ("Risk Level", risk_lvl),
         ("Risk Description", translated_risk_desc),
         ("Prediction", result),
-    ])
+    ])