Update app.py

app.py

@@ -1,6 +1,8 @@
 import streamlit as st
 from transformers import pipeline
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
+pip install googletrans==4.0.0rc1
+from googletrans import Translator
 
 # Load the sentiment analysis model from our BERT model
 classifier = pipeline("text-classification", model = "MarieAngeA13/Sentiment-Analysis-BERT")
@@ -10,12 +12,24 @@ st.title('Sentiment Analysis with BERT')
 st.write('Enter some text and we will predict its sentiment!')
 
 # Add a text input box for the user to enter text
+translator = Translator()
 text_input = st.text_input('Enter text here')
 
+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
+print(translated_text)
+
+
+
 # When the user submits text, run the sentiment analysis model on it
 if st.button('Submit'):
     # 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']
@@ -24,4 +38,4 @@ if st.button('Submit'):
     # Display the sentiment prediction to the user
     st.write(f'Sentiment: {sentiment}')
     st.write(f'Confidence: {round(confidence, 2)}')
-   
+