Update app.py

app.py

@@ -5,8 +5,16 @@ from transformers import pipeline
 model_name = "google-bert/bert-base-uncased"
 pipe = pipeline("text-classification", model=model_name)
 
+# Custom labels for your classification task
+labels = {
+    "LABEL_0": "Negative",
+    "LABEL_1": "Positive"
+}
+
 # Streamlit app
-st.title("BERT Text Classification")
+st.title("Text Classification")
+
+st.write("This app uses a pre-trained BERT model to classify text into positive or negative sentiment.")
 
 input_text = st.text_area("Enter text to classify")
 
@@ -15,7 +23,11 @@ if st.button("Classify"):
         # Perform classification
         result = pipe(input_text)
         
-        st.write(f"Predicted Class: {result[0]['label']}")
-        st.write(f"Confidence: {result[0]['score']:.4f}")
+        # Extract label and score
+        label = labels.get(result[0]['label'], result[0]['label'])
+        score = result[0]['score']
+
+        st.write(f"**Predicted Class:** {label}")
+        st.write(f"**Confidence:** {score:.4f}")
     else:
         st.write("Please enter some text to classify.")