Create app.py

app.py

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+import streamlit as st
+from transformers import pipeline
+
+# Load the text classification model pipeline
+classifier = pipeline("text-classification",model='isom5240sp24/bert-base-uncased-emotion', return_all_scores=True)
+
+# Streamlit application title
+st.title("Text Classification")
+st.write("Classification for 6 emotions: sadness, joy, love, anger, fear, surprise")
+
+# Text input for user to enter the text to classify
+text = st.text_area("Enter the text to classify", "")
+
+# Perform text classification when the user clicks the "Classify" button
+if st.button("Classify"):
+    # Perform text classification on the input text
+    results = classifier(text)[0]
+
+    # Display the classification result
+    max_score = float('-inf')
+    max_label = ''
+
+    for result in results:
+        if result['score'] > max_score:
+            max_score = result['score']
+            max_label = result['label']
+
+    st.write("Text:", text)
+    st.write("Label:", max_label)
+    st.write("Score:", max_score)