Update app.py

app.py

@@ -1,41 +1,42 @@
 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")
+# Installer le package googletrans
+!pip install googletrans==4.0.0rc1
 
-# Create a Streamlit app
+# Charger le modèle de classification des sentiments BERT
+classifier = pipeline("text-classification", model="MarieAngeA13/Sentiment-Analysis-BERT")
+
+# Créer une application Streamlit
 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
+# Ajouter un champ de saisie de texte pour l'utilisateur
 translator = Translator()
 text_input = st.text_input('Enter text here')
 
+# Détecter la langue du texte saisi
 detected_language = translator.detect(text_input).lang
 
+# Traduire le texte s'il est en français
 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)
-
+st.write(translated_text)
 
-
-# When the user submits text, run the sentiment analysis model on it
+# Lorsque l'utilisateur clique sur "Submit"
 if st.button('Submit'):
-    # Predict the sentiment of the text using our own BERT model
+    # Prédire le sentiment du texte en utilisant notre modèle BERT
     output = classifier(translated_text)
 
     best_prediction = output[0]
     sentiment = best_prediction['label']
     confidence = best_prediction['score']
     
-    # Display the sentiment prediction to the user
+    # Afficher la prédiction de sentiment à l'utilisateur
     st.write(f'Sentiment: {sentiment}')
     st.write(f'Confidence: {round(confidence, 2)}')
-