Update app.py

app.py

@@ -3,6 +3,8 @@ import gradio as gr
 from nltk.sentiment.vader import SentimentIntensityAnalyzer
 import nltk
 nltk.download('vader_lexicon')
+from deep_translator import (GoogleTranslator)
+from langdetect import detect
 
 zero_shot_classifier = pipeline("zero-shot-classification" , model='roberta-large-mnli')
 
@@ -13,6 +15,11 @@ issues = ["Misconduct" , "Negligence" , "Discrimination" , "Corruption" , "Viola
 
 apprecn = ["Tech-Savvy Staff" , "Co-operative Staff" , "Well-Maintained Premises" , "Responsive Staff"]
 
+def translate(input_text):
+    source_lang = detect(input_text)
+    translated = GoogleTranslator(source=source_lang, target='en').translate(text=input_text)
+    return translated
+    
 def spam_detection(input_text):
     
     return spam_detector(input_text)[0]['label'] == 'clean'
@@ -43,6 +50,8 @@ def negative_zero_shot(input_text):
     return zero_shot_classifier(input_text , candidate_labels = issues , multi_label = False)['labels'][0]
     
 def pipeline(input_text):
+
+    input_text = translate(input_text)
     
     if spam_detection(input_text):