Update app.py

app.py

@@ -1,11 +1,12 @@
 import streamlit as st
 from transformers import pipeline
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-# Load the sentiment analysis model from Hugging Face
-classifier = pipeline('sentiment-analysis')
+# Load the sentiment analysis model from our BERT model
+classifier = pipeline("text-classification", model = "MarieAngeA13/Sentiment-Analysis-BERT")
 
 # Create a Streamlit app
-st.title('Sentiment Analysis with Hugging Face')
+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
@@ -13,8 +14,11 @@ text_input = st.text_input('Enter text here')
 
 # When the user submits text, run the sentiment analysis model on it
 if st.button('Submit'):
-    # Predict the sentiment of the text using the Hugging Face model
-    sentiment = classifier(text_input)[0]['label']
+    # Predict the sentiment of the text using our own BERT model
+    output = classifier(text_input)
+
+    best_prediction = output[0]
     
     # Display the sentiment prediction to the user
-    st.write(f'Sentiment: {sentiment}')
+    st.write(f'Sentiment: {best_prediction['label']}')
+    st.write(f'Confidence: {best_prediction['score']}')