Update app.py

app.py

@@ -1,8 +1,11 @@
 import streamlit as st
 from transformers import pipeline
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from googletrans import Translator
+
 # Load the sentiment analysis model from our BERT model
-classifier = pipeline("text-classification", model = "MarieAngeA13/Sentiment-Analysis-BERT")
+classifier = pipeline("text-classification", model="MarieAngeA13/Sentiment-Analysis-BERT")
+translator = Translator()
 
 # Create a Streamlit app
 st.title('Sentiment Analysis with BERT')
@@ -14,8 +17,16 @@ text_input = st.text_input('Enter text here')
 
 # When the user submits text, run the sentiment analysis model on it
 if st.button('Submit'):
+    # Translate the text if the detected language is French
+    detected_language = translator.detect(text_input).lang
+    if detected_language == 'fr':
+        translation = translator.translate(text_input, src='fr', dest='en')
+        translated_text = translation.text
+    else:
+        translated_text = text_input
+
     # Predict the sentiment of the text using our own BERT model
-    output = classifier(text_input)
+    output = classifier(translated_text)
 
     best_prediction = output[0]
     sentiment = best_prediction['label']